Perovskite-silicon tandem solar cells have become the clearest path to a real jump in solar-panel efficiency in decades, and their patent landscape is distinctive because the underlying physics gives every developer the same target while leaving wide latitude in how to reach it. A single-junction silicon cell is capped by a physical ceiling that combines the Shockley-Queisser detailed-balance limit with Auger recombination losses; a Fraunhofer ISE analysis that combines both effects puts the accurate theoretical efficiency limit for a silicon-based monolithic tandem at 43.2 percent¹. The path to that ceiling runs through several distinct patenting layers. Interface passivation is currently the dominant lever on record efficiency: mixed self-assembled monolayer (SAM) contacts first enabled a certified 28.3 percent tandem², further interface-passivation work pushed a certified cell to 31.25 percent³, and subsequent bilayer SAM strategies have continued to close the non-radiative recombination gap⁴. A separate, equally central problem is depositing perovskite uniformly onto the pyramid-textured surface of industrial silicon wafers — the texture that gives production-grade silicon its light-trapping advantage also makes uniform, defect-free perovskite deposition difficult, typically causing localized electrical leakage at the pyramid peaks. Proposed fixes include selective passivation of the pyramid tips specifically (32.9 percent reported)⁵, "iceberg-like" pyramid engineering compatible with industrial texturing (33 percent)⁶, and advanced light-management approaches for textured interfaces more broadly⁷. Because record efficiency and manufacturable durability are driven by different, not always overlapping, sets of techniques, freedom-to-operate and white space analysis must span composition, interface, and texture-compatible process together.

The field has moved from a laboratory curiosity to early commercial shipment within the past two years, even as the record-chasing and the product-shipping efforts remain distinct. The current widely cited two-terminal cell efficiency record is 34.85 percent, announced by LONGi and stated by the company to be NREL-certified — a figure that should be read as certified-per-developer disclosure at cell area, since the independent certification certificate itself was not directly available in this research pass, and it is not (as of this writing) also a peer-reviewed published result⁸. This record is categorically distinct from module-level performance: Oxford PV and Fraunhofer ISE reported a full-size commercial-format module at 25 percent efficiency, a genuinely different, lower, and non-comparable figure because module-area results inherently lag cell-area records⁹. Preserving this cell-versus-module distinction, and the related single-junction-perovskite-versus-tandem distinction, matters throughout any reading of the field's efficiency claims. Commercial shipment and pilot-line status is confirmed via primary company disclosure for Oxford PV⁹; comparable primary shipment-volume disclosures for other major developers were not located in this pass and should be treated as unconfirmed pending each company's own investor-relations or regulatory filing. Because applications publish about eighteen months after filing, the most recent passivation and encapsulation filings are under-represented, so the current frontier is more active than granted-patent counts suggest.

The strategic picture turns on which side of the record-versus-durability divide an owner is defending. Efficiency-record IP, concentrated in interface-passivation chemistry, is advancing quickly through a small number of well-resourced developers and research groups, and durability improvements are increasingly bundled with efficiency claims rather than reported separately in the most recent literature. The more open, commercially decisive ground remains the manufacturing and durability layer: deposition techniques compatible with textured industrial silicon at scale, and encapsulation and barrier-layer chemistry that closes the stability gap with silicon's multi-decade outdoor lifetime. Reading the landscape by layer, technique, and owner, and tracking both the patents and the underlying materials-science research, is what separates a workable manufacturing position from a blocked one.

Where the perovskite-silicon tandem white space is

Textured-silicon-compatible deposition. Depositing a uniform, leakage-free perovskite layer onto the pyramid-textured surface of industrial silicon wafers, rather than the flat substrates used for the highest record cells, is the central manufacturing barrier standing between lab records and mass production, with several distinct proposed solutions still competing⁵,⁶,⁷.

Encapsulation and durability chemistry bundled with efficiency. The most recent passivation literature increasingly targets stability and efficiency together rather than treating them as separate problems, which is itself a signal of where the field is converging.

Verified, primary-sourced commercial shipment data. Commercial shipment status is confirmed for Oxford PV via primary disclosure; comparable confirmation for other major developers remains outstanding, making rigorously verified shipment and production-volume claims a genuine differentiator.

Module-scale (not just cell-scale) efficiency. Because record efficiency is consistently reported at small cell area while commercial products are judged at full module scale, IP and technique that closes this cell-to-module gap is disproportionately valuable relative to further small-area record chasing.

Flexible and building-integrated form factors. Solution-processable, flexible tandem cells for curved surfaces, windows, and other building-integrated applications exploit perovskite's inherent advantages rather than competing directly with rigid silicon, and remain a less-crowded adjacent frontier.

How AI-powered landscape and white space analysis helps

Resolving a landscape that spans perovskite composition, interface chemistry, texture-compatible deposition, and encapsulation — where record-setting efficiency claims are announced by multiple developers within the same quarter, and where cell-area, module-area, certified, and company-announced figures are easily conflated — requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by layer and technique across varied terminology, attribution that normalizes solar-manufacturer, materials-supplier, and research-institution filers to canonical entities, and continuous monitoring that keeps pace with a field where the efficiency record itself changes multiple times a year. Because photovoltaics advances appear in materials-science literature before they are patented, reading both patents and literature gives the earliest signal of which technique is actually closing the gap between record cell and bankable product.

The competitive landscape by the numbers

The perovskite/tandem solar-cell patent family set totals roughly 12,575 documents, though this figure includes broader "tandem solar cell" and general photovoltaics art and therefore overstates perovskite-silicon-specific filings on its own (Cypris corpus, indicative; 2025–26 partial). Geography is led by China (approximately 1,994 families) and the United States (approximately 1,623), followed by Germany (approximately 846), Japan (approximately 773), and South Korea (approximately 758) (Cypris corpus, indicative; 2025–26 partial). Top assignees mix photovoltaics incumbents and materials firms — Trina Solar, Oxford Photovoltaics, Kaneka, LG, BASF, CEA, and JinkoSolar — alongside broader electronics players such as Canon and Toshiba, whose filings contribute some non-perovskite tandem/PV art to the set (Cypris corpus, indicative; 2025–26 partial). Filings show a recent perovskite-driven resurgence, rising from roughly 526 families in 2021 to about 1,191 in 2025 (Cypris corpus, indicative; 2025–26 partial).

Where Cypris fits

Cypris runs patent landscape and white space analysis for fast-moving materials fields such as perovskite-silicon tandem photovoltaics across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by layer, perovskite composition, interface passivation, tandem architecture, and encapsulation, and normalizes solar-manufacturer, materials-supplier, and research-institution filers to canonical entities, so a team can resolve which layers and techniques are crowded and which remain open as white space. Semantic search across patents and scientific literature connects filings to the underlying materials-science research, which is where tandem-cell advances appear first, often well ahead of the patent record. Cypris Q, the platform's agentic layer, lets teams run landscape and white space analysis conversationally and chain the clustering, attribution, and gap analysis, and Agentic Monitoring tracks a defined layer over time and flags new patents and papers as they publish. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.

FAQ

What is a perovskite-silicon tandem solar cell? A perovskite-silicon tandem solar cell stacks a perovskite top cell on top of a crystalline-silicon bottom cell so the combined device captures more of the solar spectrum than either material could alone, allowing it to exceed the theoretical ceiling that limits any single-junction silicon cell. That combined ceiling, accounting for both Shockley-Queisser and Auger effects, is calculated at 43.2 percent¹. The current company-reported, NREL-certified cell-area record stands at 34.85 percent⁸.

Why can't a single-junction silicon cell just be made more efficient instead? A single-junction silicon cell cannot exceed its combined Shockley-Queisser/Auger efficiency ceiling because that limit is set by the fundamental physics of extracting energy from a broad-spectrum light source using a single semiconductor bandgap, not by manufacturing quality¹. Continuing to refine single-junction silicon can approach that limit but never exceed it. Stacking a second, complementary-bandgap material is the only way to break through it.

What layers does the tandem patent landscape cover? The landscape covers perovskite composition and bandgap engineering, interface passivation chemistry, tandem cell architecture compatible with industrial silicon texturing, and encapsulation and stability engineering. Interface passivation is currently the dominant lever on record efficiency²,³,⁴, while texture-compatible deposition remains the central manufacturing barrier⁵,⁶,⁷. A bankable product depends on progress across all of these layers together.

Are perovskite-silicon tandem panels available to buy in 2026? Only in limited volume, and only confirmed for certain developers. Oxford PV has a confirmed primary disclosure of a full-size commercial-format module reaching 25 percent efficiency in partnership with Fraunhofer ISE⁹, but comparable shipment-volume confirmation for other major manufacturers was not available in current primary disclosures. The efficiency record (34.85 percent, cell-area) and the shipping product (25 percent, module-area) are currently very different numbers describing different things.

Where is the white space in perovskite-silicon tandem solar cells? The white space includes textured-silicon-compatible deposition, durability chemistry bundled with efficiency gains, verified commercial shipment data, closing the cell-to-module efficiency gap, and flexible or building-integrated form factors. Record-efficiency IP is advancing quickly through interface-passivation chemistry specifically. The manufacturing, durability, and module-scale layers are the more open and commercially decisive ground.

Why is depositing perovskite onto textured silicon so hard? Depositing perovskite onto textured silicon is hard because industrial silicon wafers use a pyramid-textured surface to trap light and boost efficiency, but that same texture makes it difficult to deposit a uniform, defect-free perovskite layer, often causing localized electrical leakage at the pyramid peaks. The highest record cells are typically demonstrated on flatter or smaller-area substrates that partially avoid this problem. Multiple distinct fixes are being pursued in parallel, including selective peak passivation and engineered pyramid geometry⁵,⁶.

Why does perovskite-silicon tandem analysis need scientific literature? Perovskite-silicon tandem analysis needs scientific literature because composition, passivation, and encapsulation advances appear in materials-science research before they are patented, and because distinguishing certified from company-announced figures, and cell-area from module-area results, requires reading the primary literature rather than press coverage. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.

Which teams use perovskite-silicon tandem patent landscape analysis? Perovskite-silicon tandem patent landscape analysis is used by R&D, IP, and strategy teams at solar manufacturers and materials suppliers, as well as investors assessing the photovoltaics sector. Because record efficiency and commercial durability are driven by different techniques at different maturity levels, and because efficiency claims require careful certified-versus-announced and cell-versus-module verification, structured analysis is essential. Cypris serves hundreds of enterprise customers across advanced materials, energy, and other research-intensive industries.

Endnotes

1. Schubert MC, Glunz SW, Fell A, Bivour M, Messmer C. Elucidating the efficiency limit of silicon-based monolithic tandem cells through the combination of Auger and Shockley-Queisser limits. EES Solar. DOI: 10.1039/d5el00085h.
2. Kishimoto K, Uzu H, Yamamoto K, Yoshida W, Okamoto S. 28.3% efficient perovskite-silicon tandem solar cells with mixed self-assembled monolayers. Applied Physics Express. DOI: 10.35848/1882-0786/ac727b.
3. Artuk K, Sahli F, Jeangros Q, Boccard M, Tabean S. Interface passivation for 31.25%-efficient perovskite/silicon tandem solar cells. Science. DOI: 10.1126/science.adg0091.
4. Jia Y, Xu X, Li P, Li Z, Xiao C. Perovskite/silicon tandem solar cells with bilayer interface passivation. Nature. DOI: 10.1038/s41586-024-07997-7.
5. Ye JP, Yang X, Ying Z, Du H, Yang W. Selective passivation of pyramid peaks for 32.9%-efficient perovskite/silicon tandem solar cells. Matter. DOI: 10.1016/j.matt.2026.102824.
6. Wei J, Li R, Yu X, Hang P, Wu T. Iceberg-like pyramids in industrially textured silicon enabled 33% efficient perovskite-silicon tandem solar cells. Nature Communications. DOI: 10.1038/s41467-025-62389-3.
7. Stannowski B, Korte L, Jošt M, Al-Ashouri A, Lipovšek B. Textured interfaces in monolithic perovskite/silicon tandem solar cells: advanced light management for improved efficiency and energy yield. Energy & Environmental Science. DOI: 10.1039/c8ee02469c.
8. LONGi Green Energy. 34.85%! LONGi Breaks World Record for Crystalline Silicon-Perovskite Tandem Solar Cell Efficiency Again. Company press release, longi.com.
9. Oxford PV and Fraunhofer ISE. Oxford PV and Fraunhofer ISE Develop Full-sized Tandem PV Module with Record Efficiency of 25 Percent. Fraunhofer ISE press release, ise.fraunhofer.de.
10. Cypris platform corpus analysis, perovskite/tandem solar-cell patent families. Indicative figures; 2025–2026 partial.

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Commercial fusion energy has moved from a distant public-research goal to a well-funded private race, and its patent landscape is being staked out as companies compress decades of physics into engineering programs. Fusion fuses light nuclei to release energy, and its progress is measured by the fusion gain, or Q, and the triple product of density, temperature, and confinement time, the parameters that determine whether a device produces more energy than it consumes.³ It is pursued through several competing confinement approaches, each a distinct region of patenting: magnetic confinement, including tokamaks, spherical tokamaks such as Globus-M2, stellarators, mirrors, and field-reversed configurations; inertial confinement using lasers; and magneto-inertial hybrids, running on fuels such as deuterium-tritium, deuterium-deuterium, and proton-boron.⁵ The intellectual property divides across the enabling technologies these approaches share: the magnets that confine the plasma, especially high-temperature superconducting magnets; the systems that heat and control the plasma; the tritium breeding blankets that must produce fuel and capture energy; the first-wall and divertor materials that survive intense neutron flux; and, for inertial approaches, the targets and drivers, whose implosion physics is an active research area.¹,⁶ Because a viable plant depends on several of these layers, freedom-to-operate and white space analysis must span the confinement approaches and the enabling layers together.

The landscape is being reshaped by a technology shift and a funding boom. High-temperature superconducting magnets, which reach much stronger fields than conventional superconductors, allow far more compact and potentially cheaper machines: the SPARC device, for example, is designed around a high-field, compact tokamak concept, and the physics basis for such burning-plasma machines is now well documented.² The 2025 edition of the IAEA's World Fusion Outlook gave these magnets a special focus, reflecting their role across tokamaks, stellarators, and mirror concepts.⁹ Public milestones anchor the field: in December 2022 the US National Ignition Facility achieved fusion ignition, producing about 3.15 megajoules of fusion energy from about 2.05 megajoules of laser energy delivered to the target, a scientific, target-level energy gain rather than a net-grid gain, and later experiments repeated ignition.⁷ On the magnetic side, ITER's 2024 re-baseline set the start of research operations in 2034 and the start of deuterium-tritium operations in 2039, a four-year delay from the earlier reference, and changed the first-wall material from beryllium to tungsten.⁸ Public programs are also advancing the physics, as with China's HL-3 tokamak.⁴ The intellectual-property picture is therefore a mix, because much of the underlying plasma physics is in the public domain from decades of open research, while the proprietary value concentrates in the specific engineering that turns physics into a machine. That split is visible in the record: across the Cypris corpus of more than 500 million patents and scientific papers, the fusion set holds on the order of 11,694 families and grew from about 376 in 2020 to roughly 711 in 2024, with the most active assignees being public institutes and diversified industrials, led by the Hefei Institutes of Physical Science of the Chinese Academy of Sciences alongside Toshiba, Hitachi, and the Japan Atomic Energy Agency, and China ahead of the United States and the United Kingdom on geography; 2025 and 2026 counts are partial because of the publication lag.

The strategic question is which enabling layer to own, and the white space sits where engineering, not physics, is the barrier. High-temperature superconducting magnet design and the manufacturing of the superconducting tape and cable are a high-value layer where a compact-machine advantage is won.² Tritium breeding, producing more tritium than the plant consumes, has not been demonstrated at commercial scale and is a critical, comparatively open area, as are the first-wall and divertor materials that must withstand neutron damage over a plant's life. Plasma heating and control, increasingly aided by machine learning, and, for inertial approaches, target fabrication and drivers, are further contested layers.⁶ Private-venture pilot-plant dates and net-gain targets should be read as company projections rather than demonstrated results. Reading the landscape by approach, enabling layer, and owner, and tracking both the patents and the underlying fusion-science research, is what separates a crowded region from an open one.

Where the fusion white space is

High-temperature superconducting magnets. Magnet design and the manufacturing of superconducting tape and cable for compact, high-field machines are a high-value, capital-intensive layer.²

Tritium breeding blankets. Breeding more tritium than the plant consumes, and capturing the fusion energy, is unproven at commercial scale and a critical, comparatively open area.

First-wall and divertor materials. Materials such as tungsten that survive intense neutron flux over a plant's life, and the strategies to replace them, are a distinct, high-stakes engineering layer.⁸

Plasma heating and control. Systems that heat, shape, and stabilize the plasma, increasingly using machine learning, are an active and contested layer.

Inertial targets and drivers. For inertial-confinement approaches, target fabrication and driver technologies are a separate region of patenting.¹,⁶

How AI-powered landscape and white space analysis helps

Resolving a landscape that spans several confinement approaches and enabling layers, built on public physics but proprietary engineering, requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by approach and enabling layer across varied terminology, attribution that normalizes private, public, and academic filers to canonical entities, and continuous monitoring that keeps pace with a fast-funding field. Because fusion advances appear in scientific literature before they are patented, and because so much of the science is public while the engineering is proprietary, reading both patents and literature is essential to separate open physics from claimable engineering.

Where Cypris fits

Cypris runs patent landscape and white space analysis for engineering-intensive deep-tech fields such as commercial fusion energy across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by confinement approach, tokamak, stellarator, inertial, and others, and by enabling layer, magnets, heating and control, tritium breeding, materials, and targets, and normalizes private, public, and academic filers to canonical entities, so a team can resolve which approaches and layers are crowded and which remain open as white space. Semantic search across patents and scientific literature connects filings to the underlying fusion-science research, which is where advances appear first and where public physics must be separated from proprietary engineering. Cypris Q, the platform's agentic layer, lets teams run landscape and white space analysis conversationally and chain the clustering, attribution, and gap analysis, and Agentic Monitoring tracks a defined layer over time and flags new patents and papers as they publish. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.

FAQ

What is the commercial fusion energy patent landscape? The commercial fusion energy patent landscape is the set of patents covering the technologies needed to build a fusion power plant. It spans confinement approaches, tokamaks, stellarators, inertial, and others, and enabling layers such as magnets, plasma heating and control, tritium breeding, first-wall materials, and inertial targets. Each is a distinct region of patenting.

Why are high-temperature superconducting magnets so important? High-temperature superconducting magnets are important because they reach much stronger magnetic fields than conventional superconductors, which allows far more compact and potentially cheaper fusion machines. They have become a central engineering and patenting focus across several confinement approaches, and the 2025 IAEA World Fusion Outlook gave them a special focus. Magnet design and superconducting-tape manufacturing are high-value layers.

Why is tritium breeding a key white space? Tritium breeding is a key white space because a deuterium-tritium plant must produce more tritium than it consumes to be self-sufficient, and this has not been demonstrated at commercial scale. The breeding blanket must also capture the fusion energy and survive neutron flux. That combination makes it a critical, comparatively open engineering layer.

What did the NIF ignition result actually show? The National Ignition Facility achieved fusion ignition in December 2022, producing about 3.15 megajoules of fusion energy from about 2.05 megajoules of laser energy delivered to the target. This is a scientific, target-level energy gain, not a net-grid gain, because the laser system draws far more energy from the grid than reaches the target. Later experiments repeated ignition.

How does public physics affect fusion IP? Public physics affects fusion IP because decades of open, publicly funded research placed much of the underlying plasma physics in the public domain, so the proprietary, patentable value concentrates in the specific engineering, magnets, blankets, materials, targets, and control systems, that turns physics into a working machine. Distinguishing public science from claimable engineering is central to fusion freedom-to-operate.

Where is the white space in fusion energy? The white space includes high-temperature superconducting magnets and their manufacturing, tritium breeding blankets, first-wall and divertor materials, plasma heating and control including machine-learning approaches, and inertial targets and drivers. The physics is largely public. The most open, high-value opportunities are in the engineering layers that remain unproven at commercial scale.

Why does fusion analysis need scientific literature? Fusion analysis needs scientific literature because so much of the field's knowledge is in public research, and new engineering advances appear in the literature before they are patented, so reading both is essential to separate open physics from claimable engineering. Analyzing patents alone gives a partial view. Cypris analyzes both across more than 500 million patents and scientific papers.

What software helps analyze the fusion energy patent landscape? Software for the fusion landscape should cluster activity by confinement approach and enabling layer, resolve private, public, and academic filers to canonical owners, search patents and scientific literature semantically, and monitor a fast-funding field continuously. Cypris does this across more than 500 million patents and scientific papers using a proprietary R&D ontology, semantic search, Cypris Q, and Agentic Monitoring.

Endnotes

1. Chapman, T. D., Ralph, J. E., Woodworth, B., et al. (2024). Present understanding of ignition and gain using indirect-drive inertial confinement fusion on the U.S. National Ignition Facility. Plasma Physics and Controlled Fusion, 67(1). https://doi.org/10.1088/1361-6587/ad994f
2. Creely, A. J., Rice, J. E., Sorbom, B. N., Hartwig, Z. S., et al. (2022). Overview of the SPARC physics basis toward burning-plasma regimes in high-field, compact tokamaks. Nuclear Fusion, 62(4). https://doi.org/10.1088/1741-4326/ac1654
3. Costley, A. E. (2016). On the fusion triple product and fusion power gain of tokamak pilot plants and reactors. Nuclear Fusion, 56(6). https://doi.org/10.1088/0029-5515/56/6/066003
4. Chen, W., & Zhong, W. (2025). Breakthrough in China's fusion energy: HL-3 tokamak achieves high ion temperature and fusion triple product. The Innovation, 6. https://doi.org/10.1016/j.xinn.2025.101167
5. Sakharov, N. V., et al. (2021). Tenfold increase in the fusion triple product in the spherical tokamak Globus-M2. Nuclear Fusion, 61(6). https://doi.org/10.1088/1741-4326/abe08c
6. Zhou, Y., Sadler, J. D., & Hurricane, O. A. (2024). Instabilities and mixing in inertial confinement fusion. Annual Review of Fluid Mechanics, 57. https://doi.org/10.1146/annurev-fluid-022824-110008
7. U.S. Department of Energy (2022, December 13). DOE National Laboratory makes history by achieving fusion ignition. https://www.energy.gov/articles/doe-national-laboratory-makes-history-achieving-fusion-ignition
8. ITER Organization (2024). New baseline to prioritize a robust start to exploitation. https://www.iter.org/node/20687/new-baseline-prioritize-robust-start-exploitation
9. International Atomic Energy Agency (2025). Fusion energy in 2025: six global trends to watch (World Fusion Outlook 2025). https://www.iaea.org/newscenter/news/fusion-energy-in-2025-six-global-trends-to-watch

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Silicon photonics has moved to the center of AI infrastructure, and its patent landscape is being staked out as data centers hit a wall that electrical interconnects cannot cross. As AI clusters scale to enormous numbers of accelerators, the energy and bandwidth cost of moving data over copper between chips, boards, and racks has become a dominant constraint, and peer-reviewed work frames high-bandwidth-density, energy-efficient optical interconnects as the leading answer.¹,² The technology arrives in several forms, each a distinct region of patenting: co-packaged optics, which place the optical engine in the same package as the switch or accelerator; optical input-output chiplets that bring light directly to the compute die; and silicon-photonic network switches. The intellectual property divides across the photonic devices themselves, such as modulators and detectors; the photonic-electronic integration and advanced packaging that combine light and electronics; the laser and light-source technologies that feed them; and the system-level architecture that ties them into an AI fabric. Because a working optical interconnect depends on several of these layers, freedom-to-operate and white space analysis must span them together.

The landscape is moving from roadmap to product very quickly, and it is quantitatively demanding. Peer-reviewed co-packaged transceivers now report energy efficiencies on the order of 3 picojoules per bit at hundreds of gigabits per second per channel, and advanced through-silicon and through-glass interposer packaging has demonstrated bandwidths beyond 67 and 110 gigahertz, illustrating both the device-level and packaging-level progress.³,⁶,⁷ Standardization is advancing alongside the hardware: the chiplet-interconnect standard released a new version in August 2025 adding higher data rates and extended reach, shaping how photonic engines connect to compute.⁸,⁹ Major networking and accelerator vendors have introduced co-packaged optical switches and optical I/O, but the competitive structure is layered rather than winner-take-all. Across the Cypris corpus of more than 500 million patents and scientific papers, the silicon-photonics, co-packaged-optics, and optical-interconnect space holds on the order of 102,700 de-duplicated families and has grown steadily and with acceleration, and the most active assignees are systems and networking vendors and foundries and research institutes, led by firms such as Intel, Huawei, IBM, NTT, TSMC, Cisco, and Marvell together with foundries and research organizations such as GlobalFoundries and imec, rather than pure-play startups, which do not appear in the top tier; the United States leads on geography, followed by China, Japan, and Taiwan. Because applications publish about eighteen months after filing, the most recent modulator, integration, and packaging filings are under-represented (2025 counts are partial), so the current frontier is more active than granted-patent counts suggest.

The strategic question is which layer to own, and the white space sits where physics and manufacturing are hardest. High-speed, low-power modulators are a foundational device layer where efficiency gains translate directly into system power savings, and microring-based modulator designs are a central approach.⁴,⁵ Photonic-electronic integration and packaging, bringing light reliably to the compute die at yield and scale, is the central manufacturing challenge and where much of the defensible, hard-to-design-around IP is concentrating.⁶,⁷ In the Cypris corpus, the modulator and light-source layers are the most heavily patented, followed by integration and packaging and then optical I/O, so laser and light-source integration is a distinct and contested layer, and system-level architecture, how optical links reshape the AI fabric, is where differentiation is won. Reading the landscape by layer and by owner, and tracking both the patents and the underlying photonics research, is what separates a crowded region from an open one.

Where the silicon photonics white space is

High-speed, low-power modulators. Modulators that raise data rates while cutting energy per bit are a foundational device layer where gains flow straight to system power.⁴,⁵

Photonic-electronic integration and packaging. Bringing light to the compute die at yield and scale is the central manufacturing challenge and where much hard-to-design-around IP concentrates.⁶,⁷

Laser and light-source integration. Efficient, reliable on- and off-package light sources are a distinct and contested layer, and among the most heavily patented in the corpus.

Optical I/O chiplets and interfaces. Chiplet-based optical I/O and the standardized interfaces that connect it to compute are an active, fast-moving layer.⁸,⁹

System-level optical architecture. Designs that reshape the AI fabric around optical links, including optical switching, are where system differentiation is won.

How AI-powered landscape and white space analysis helps

Resolving a landscape that spans photonic devices, integration and packaging, light sources, and system architecture, in a field moving from roadmap to product month to month, requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by layer across varied terminology, attribution that normalizes vendor, foundry, and startup filers to canonical entities, and continuous monitoring that keeps pace with a fast-moving field. Because silicon-photonics advances appear in scientific and conference literature before they are patented, reading both patents and literature gives the earliest signal of where the frontier and the white space are moving.

Where Cypris fits

Cypris runs patent landscape and white space analysis for fast-moving deep-tech fields such as silicon photonics for AI across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by layer, photonic device, integration and packaging, light source, optical I/O, and system architecture, and normalizes vendor, foundry, and startup filers to canonical entities, so a team can resolve which layers are crowded and which remain open as white space. Semantic search across patents and scientific literature connects filings to the underlying photonics research, which is where silicon-photonics advances appear first, often well ahead of the patent record. Cypris Q, the platform's agentic layer, lets teams run landscape and white space analysis conversationally and chain the clustering, attribution, and gap analysis, and Agentic Monitoring tracks a defined layer over time and flags new patents and papers as they publish. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.

FAQ

Why is silicon photonics central to AI infrastructure? Silicon photonics is central to AI infrastructure because AI clusters have grown so large that moving data over copper between chips, boards, and racks consumes too much power and limits bandwidth. Optical interconnects move data as light, cutting interconnect power and raising bandwidth. That is why co-packaged optics and optical I/O have moved from roadmap to product.

What layers does the silicon photonics landscape cover? The landscape covers photonic devices such as modulators and detectors, photonic-electronic integration and advanced packaging, laser and light-source technologies, optical I/O chiplets and interfaces, and system-level optical architecture. Each is a distinct region of patenting with different owners. Freedom-to-operate and white space analysis must span them together.

Who holds the IP in silicon photonics for AI? In the Cypris corpus, the most active assignees are systems and networking vendors, foundries, and research institutes, rather than pure-play startups, which do not appear in the top tier. Ownership is distributed across the stack, from modulators and integration to light sources and architecture. That layered structure makes landscape analysis valuable.

Where is the white space in silicon photonics? The white space includes high-speed, low-power modulators, photonic-electronic integration and packaging, laser and light-source integration, optical I/O chiplets and interfaces, and system-level optical architecture. Integration and packaging is the central manufacturing challenge and where much hard-to-design-around IP concentrates. The modulator and light-source layers are the most heavily patented in the corpus.

Why is integration and packaging so important? Integration and packaging is important because the hardest part of optical interconnects is bringing light reliably to the compute die at high yield and large scale. Solving this at manufacturable cost is what turns a device advantage into a system advantage. Much of the defensible, hard-to-design-around IP is concentrating there.

Why does silicon photonics analysis need scientific literature? Silicon photonics analysis needs scientific literature because device, integration, and light-source advances appear in research and conference proceedings before they are patented, so the literature gives the earliest signal in a fast-moving field. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.

What software helps analyze the silicon photonics patent landscape? Software for the silicon photonics landscape should cluster activity by device, integration, light-source, and architecture layer, resolve vendor, foundry, and startup filers to canonical owners, search patents and scientific literature semantically, and monitor a fast-moving field continuously. Cypris does this across more than 500 million patents and scientific papers using a proprietary R&D ontology, semantic search, Cypris Q, and Agentic Monitoring.

Which teams use silicon photonics patent landscape analysis? Silicon photonics patent landscape analysis is used by R&D, IP, and strategy teams at semiconductor, networking, photonics, and data-center companies, as well as investors assessing the sector. Because ownership is distributed across the stack and the field is moving fast, structured analysis is essential. Cypris serves hundreds of enterprise customers across research-intensive and regulated industries.

Endnotes

1. Novick, A., James, A., Wu, L. Y., Hattink, M., et al. (2023). High-bandwidth-density silicon photonic resonators for energy-efficient optical interconnects. Applied Physics Reviews, 10(3). https://doi.org/10.1063/5.0160441
2. Priyadarshi, S. (2025). Unlocking the potential of co-packaged optics in AI and HPC: opportunities and challenges. IEEE Communications Magazine. https://doi.org/10.1109/mcom.001.2500504
3. Li, X., Li, Y., Zhao, Y., Zhong, K., et al. (2025). Monolithically integrated 4×128 Gb/s, 3.07 pJ/bit silicon photonic transceiver for co-packaged optics. Optics Express, 33. https://doi.org/10.1364/oe.577010
4. Wu, Y., He, J., Cao, Y., & Liu, L. (2022). The high-efficiency co-design and measurement verification of high-bandwidth silicon photonic microring modulator. IET Optoelectronics, 16(6). https://doi.org/10.1049/ote2.12070
5. Titriku, A., Palermo, S., Chen, C.-H., Fiorentino, M., et al. (2015). Silicon photonic microring resonator-based transceivers for compact WDM optical interconnects. IEEE Compound Semiconductor Integrated Circuit Symposium (CSICS). https://doi.org/10.1109/csics.2015.7314523
6. Molnar, A., Ou, Y., Khilwani, D., et al. (2025). Scaling co-packaged optical interconnects using hybrid 2.5D/3D integration. IEEE International Symposium on Circuits and Systems (ISCAS). https://doi.org/10.1109/iscas56072.2025.11043946
7. Liu, S., Zhang, Y., Ge, C., Du, Y., et al. (2026). High-density co-packaged optics based on TSV and TGV interposers. Advanced Photonics Nexus, 5(3). https://doi.org/10.1117/1.apn.5.3.036019
8. UCIe Consortium. Universal Chiplet Interconnect Express (UCIe) specifications. https://www.uciexpress.org/specifications
9. Das Sharma, D., et al. (2024). High-performance, power-efficient three-dimensional system-in-package designs with universal chiplet interconnect express. Nature Electronics, 7. https://doi.org/10.1038/s41928-024-01126-y

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CAR-T cell therapy has one of the most academically rooted and legally tested patent landscapes in biotechnology. A chimeric antigen receptor T-cell is engineered by giving a patient's T-cells a synthetic receptor that directs them against a cancer target, and the intellectual property spans several distinct layers: the CAR construct itself, with its antigen-binding domain, hinge, transmembrane region, costimulatory domain, and signaling domain; the viral vectors used to introduce it; the manufacturing and cell-processing methods; and the methods of use for specific indications. Because these layers are patented separately and often by different owners, freedom-to-operate for a CAR-T product is a multi-layer, multi-owner analysis rather than a single clearance.

The foundational patents emerged from academic laboratories and were then in-licensed or acquired by commercial developers, which shaped the ownership structure. Peer-reviewed analyses of CAR-T patenting activity trace the field's key early filings to academic groups, with foundational work associated with Carl June at the University of Pennsylvania and Michel Sadelain at Memorial Sloan Kettering Cancer Center, before commercialization by large pharmaceutical companies.¹,² This academic origin is visible in the ownership record: across the Cypris corpus of more than 500 million patents and scientific papers, the most active assignees in the CAR-T set are led by the University of Pennsylvania, followed by the US Department of Health and Human Services and the National Institutes of Health, the University of California San Diego, the University of Texas System, and Memorial Sloan Kettering, interleaved with commercial developers such as Novartis, Juno Therapeutics, and Kite Pharma. Peer-reviewed patent-landscape analyses describe a field of fierce competition and intensive academic-industry collaboration,¹ with one review mapping more than 1,600 patent families across the field's technological routes,³ and product-patent-linkage studies have detailed how the portfolios behind approved CAR-T products are assembled from the construct, vector, manufacturing, and method-of-use layers.⁴ Analyses of academic CAR-T patenting also document the pitfalls that arise when university filings are drafted for disclosure rather than durable claim scope.⁵ A recurring finding is that many foundational filings date to the late 1990s and early 2000s, so their earliest members are now reaching the end of their patent terms, which shifts value toward improvement patents on next-generation constructs, allogeneic and off-the-shelf approaches, and manufacturing.¹,³ Across the Cypris corpus, CAR-T patent families grew from about 2,882 in 2018 to about 9,118 in 2024, with 2025 counts partial because of the roughly eighteen-month publication lag.

Litigation defined the landscape's risk profile. In the dispute between Juno Therapeutics, which exclusively licensed a foundational receptor patent from Memorial Sloan Kettering, and Kite Pharma over its approved therapy, a jury initially found for Juno, but on August 26, 2021 the US Court of Appeals for the Federal Circuit reversed and held the foundational patent's asserted claims invalid for lack of adequate written description, reasoning that disclosing a small number of specific binding domains did not show possession of the far broader claimed genus.⁶ A peer-reviewed analysis in Biotechnology Law Report situated the decision as a strike against broadly drafted, pioneering biotechnology claims.⁷ The decision reshaped the field, because it raised questions about the validity of broadly drafted foundational biotech patents generally, and it signaled that in cell therapy the durable value may lie in specific, well-supported improvement claims rather than pioneering-but-broad foundational ones. Because applications publish about eighteen months after filing, the most recent activity in next-generation and allogeneic approaches is under-represented, so the current frontier is more active than granted-patent counts suggest.

What creates FTO risk in CAR-T products

CAR construct claims. These cover the receptor's components, including antigen-binding domain, costimulatory domain, and signaling domain, the core of many disputes.

Viral vector claims. These cover the vectors used to introduce the receptor, a distinct and separately owned layer.

Manufacturing and cell-processing claims. These cover how the therapy is produced, which is increasingly where competitive differentiation and IP concentrate.

Method-of-use claims. These cover use for specific indications and patient populations, so a construct can be free for one use and blocked for another.

Next-generation and allogeneic claims. These cover off-the-shelf, gene-edited, and next-generation approaches, a fast-growing layer where new FTO risk and white space are emerging.

How AI-powered landscape and FTO analysis helps

A multi-layer, academically rooted, litigated landscape is beyond manual clearance. AI-powered analysis addresses this with semantic search that retrieves relevant construct, vector, manufacturing, and use claims regardless of terminology, attribution that resolves academic and commercial owners to canonical entities and captures the license and acquisition chains, and continuous monitoring that tracks next-generation filings and litigation developments. Because cell-therapy advances appear in scientific literature before they are patented, reading both patents and literature gives earlier warning.

Where Cypris fits

Cypris runs patent landscape and freedom-to-operate analysis for multi-layer, academically rooted fields such as CAR-T across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters the landscape by layer, construct, vector, manufacturing, and use, and normalizes academic and commercial owners to canonical entities, so a team can trace how rights and licenses are distributed rather than read a flat list. Semantic search across patents and scientific literature surfaces relevant claims regardless of terminology and connects filings to the underlying research, which is where next-generation and allogeneic approaches emerge first. Cypris Q, the platform's agentic layer, lets teams run landscape and FTO analysis conversationally and chain the attribution, clustering, and claim-level analysis, and Agentic Monitoring tracks the landscape over time and flags new filings and developments as they publish. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.

FAQ

Why is the CAR-T patent landscape distinctive? The CAR-T patent landscape is distinctive because it is deeply rooted in academic research and has been heavily litigated. Foundational patents came from university labs and were licensed or acquired by commercial developers, and the IP spans the receptor construct, viral vectors, manufacturing, and methods of use. Freedom-to-operate is therefore a multi-layer, multi-owner analysis.

What claim types create FTO risk in CAR-T? Five claim types create FTO risk in CAR-T: CAR construct claims, viral vector claims, manufacturing and cell-processing claims, method-of-use claims, and next-generation or allogeneic claims. Each is independently patentable and can be held by a different owner. Construct and manufacturing layers are especially contested.

What was the Juno v. Kite decision? In Juno v. Kite, Juno Therapeutics asserted a foundational CAR receptor patent it had licensed from Memorial Sloan Kettering against Kite Pharma's approved therapy. A jury initially found for Juno, but the US Court of Appeals for the Federal Circuit in 2021 reversed and struck down the foundational patent for lack of adequate written description. The decision reshaped the field and raised questions about broadly drafted foundational biotech patents.

Why are CAR-T foundational patents reaching the end of their terms important? Many CAR-T foundational filings date to the late 1990s and early 2000s, so their earliest members are now reaching the end of their patent terms, which shifts value away from the original broad claims toward improvement patents. These cover next-generation constructs, allogeneic and off-the-shelf approaches, and manufacturing. FTO analysis must therefore focus increasingly on the improvement layer.

Where did CAR-T foundational patents come from? CAR-T foundational patents came largely from academic laboratories, with key work associated with Carl June at the University of Pennsylvania and Michel Sadelain at Memorial Sloan Kettering Cancer Center. These filings were in-licensed or acquired by commercial developers who brought products to market. This academic origin shaped the landscape's ownership and licensing structure, which is visible in the assignee record.

Why does CAR-T analysis need scientific literature? CAR-T analysis needs scientific literature because construct, manufacturing, and next-generation advances appear in research before they are patented, so the literature gives the earliest signal of where the field is heading. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.

Which teams need CAR-T patent landscape and FTO analysis? CAR-T patent landscape and FTO analysis is needed by R&D, IP, and business-development teams at cell-therapy and pharmaceutical companies, academic technology-transfer offices, and investors assessing cell-therapy assets. The multi-layer, litigated landscape makes structured analysis essential. Cypris serves hundreds of enterprise customers across pharmaceuticals and other research-intensive industries.

How current does a CAR-T landscape need to be? A CAR-T landscape needs to be continuously current, because foundational patents are reaching the end of their terms, next-generation and allogeneic filings publish constantly, and publication lag hides the most recent activity. A one-time landscape ages quickly. Cypris uses Agentic Monitoring to track the landscape and flag new filings and developments as they publish.

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Endnotes

1. Lyu, L., Chen, X., Hu, Y., & Feng, Y. (2020). The global chimeric antigen receptor T (CAR-T) cell therapy patent landscape. Nature Biotechnology, 38(12). https://doi.org/10.1038/s41587-020-00749-8
2. Clarke, N. S., & Jürgens, B. (2019). Evolution of CAR T-cell immunotherapy in terms of patenting activity. Nature Biotechnology, 37(4). https://doi.org/10.1038/s41587-019-0083-5
3. Malmegrim, K. C. R., Picanço-Castro, V., Pereira, C. G., Covas, D. T., Porto, G. S., & Swiech, K. (2019). Emerging CAR T cell therapies: clinical landscape and patent technological routes. Human Vaccines & Immunotherapeutics, 16(6). https://doi.org/10.1080/21645515.2019.1689744
4. Kano, S., & Kawai, Y. (2025). Expanding the concept of drug lifecycle management to chimeric antigen receptor T-cell products through product-patent linkage analysis. World Patent Information, 81. https://doi.org/10.1016/j.wpi.2025.102357
5. Constantinescu, C., Gulei, D., Bergþorsson, J. Þ., Coliţă, A., Tănase, A., Tomuleasa, C., Greiff, V., & Constantinescu, R. (2023). Pitfalls in patenting academic CAR-T cells therapy. Expert Opinion on Therapeutic Patents, 33(6). https://doi.org/10.1080/13543776.2023.2220883
6. U.S. Court of Appeals for the Federal Circuit (Aug. 26, 2021). Juno Therapeutics, Inc. v. Kite Pharma, Inc., 10 F.4th 1330. https://www.cafc.uscourts.gov/opinions-orders/20-1758.opinion.8-26-2021_1825257.pdf
7. Holman, C. M. (2021). In Juno v. Kite the Federal Circuit strikes down patent directed towards pioneering innovation in CAR T-cell therapy. Biotechnology Law Report, 40(6). https://doi.org/10.1089/blr.2021.29252.cmh

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Fault-tolerant quantum computing has become the organizing goal of the entire quantum-hardware industry, and its patent landscape is distinctive because the central problem is not building more qubits but building qubits that stay correct while computing. Fault-tolerant quantum computing combines many noisy physical qubits into one error-protected logical qubit through a quantum error-correcting code, with the goal of "below-threshold" operation, where adding more physical qubits per logical qubit exponentially suppresses the logical error rate rather than accumulating it. Google's Quantum AI team demonstrated this directly on a superconducting processor: scaling a surface code from distance-3 to distance-5 to distance-7 suppressed the logical error rate by roughly a factor of two per code-distance increment, with the resulting logical qubit's lifetime exceeding that of its best constituent physical qubit — the first hardware-scale confirmation of below-threshold scaling¹. On neutral-atom hardware, a Harvard/MIT/QuEra collaboration demonstrated a logical quantum processor with up to 48 logical qubits and reconfigurable connectivity, performing transversal operations — a milestone that is substantially error-detected and algorithmic in character rather than a fully fault-tolerant computation with continuous real-time correction². Trapped-ion platforms have separately demonstrated real-time logical-qubit error detection and correction³. The intellectual property divides across several regions, each a distinct area of patenting: the qubit modality itself, including superconducting circuits, trapped ions, neutral atoms, photonic qubits, and bosonic (cat) qubits; the error-correcting code, including the mature surface code and the newer quantum low-density parity-check (qLDPC) codes, which promise a substantially better ratio of logical to physical qubits at the cost of the non-local connectivity they require — a constraint that recent work specifically targets with 2D-local implementations⁴,⁵; the real-time decoding hardware and software that must detect and correct errors fast enough to keep pace with computation, an area seeing progress in network-integrated decoding for lattice surgery at scale⁶; and the interconnect and networking technology needed to link separate processors, an approach with early metropolitan-scale demonstrations, including work toward entanglement swapping across roughly 30 kilometers in a three-node network in New York City⁷. Because a competitive fault-tolerant architecture depends on all of these layers working together, and because different companies are betting on different qubit modalities, freedom-to-operate and white space analysis must span modality and code together.

Bosonic, or "cat," qubits are a distinct and increasingly well-evidenced hardware-efficiency route: by engineering the qubit itself to exponentially suppress bit-flip errors as a function of mean photon number, cat-qubit architectures convert the correction problem into one of handling a biased, phase-flip-dominated error channel, with experimental bit-flip times pushed past ten seconds in one demonstration⁸. Multiple hardware vendors have published multi-year roadmaps that should be read as stated targets rather than achieved milestones: IBM's own roadmap targets a system called Starling for 2029, running 100 million gates on 200 logical qubits, while Quantinuum's own roadmap targets a universal, fully fault-tolerant system by the end of the decade⁹. Because applications publish about eighteen months after filing, the newest decoder, qLDPC-code, and interconnect filings are under-represented, so the current frontier is more active than granted-patent counts suggest.

The strategic question is which layer of the stack a given owner can actually defend, and the white space sits where engineering, not physics, is now the bottleneck. Qubit-modality IP is comparatively mature and fragmented across several well-funded, differently architected companies, so no single modality currently dominates the landscape. The faster-moving and more open ground is in error-correcting-code implementation — particularly qLDPC codes, which are newer and less thoroughly claimed than the surface code, and whose central practical obstacle (non-local connectivity) is itself an active area of new filings — real-time classical decoding hardware, which must operate fast enough not to become the new bottleneck once qubits themselves are reliable, and quantum networking, which several companies are pursuing as an alternative to scaling a single monolithic chip. Reading the landscape by modality, code, and layer, and tracking both the patents and the underlying quantum-information-science research, is what separates a defensible architectural bet from a crowded one.

Where the fault-tolerant quantum computing white space is

Quantum LDPC codes and their connectivity solutions. Codes promising a better logical-to-physical-qubit ratio than the surface code are newer and less thoroughly claimed, and the 2D-local implementations needed to make them practical are themselves an active, comparatively open filing area⁴,⁵.

Real-time decoding hardware and software. Classical decoders that detect and correct errors fast enough to keep pace with a scaling quantum processor are an increasingly critical, comparatively open layer⁶.

Bosonic and cat-qubit architectures. Hardware-efficient codes that build error protection into the physical qubit itself, reducing the number of physical qubits needed per logical qubit, remain a less-crowded alternative to surface-code-based approaches⁸.

Quantum networking and multi-node architectures. Linking separate quantum processors — including early metropolitan-scale demonstrations over standard fiber-optic infrastructure — is an emerging alternative to monolithic scaling, with comparatively little settled IP⁷.

Verified, primary-sourced roadmap claims. Because most public logical-qubit and gate-count targets are company roadmap statements rather than demonstrated results, an owner able to substantiate claims against peer-reviewed, independently reproducible results has a genuine differentiation and credibility advantage.

How AI-powered landscape and white space analysis helps

Resolving a landscape that spans five-plus qubit modalities, several competing error-correcting codes, and the classical and networking engineering needed to scale them requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by modality, code, and layer across varied and fast-evolving terminology, attribution that normalizes hardware-vendor, national-lab, and university filers to canonical entities, and continuous monitoring that keeps pace with a field where major technical milestones are being announced multiple times per year. Because quantum-information-science advances appear in physics literature and preprints before they are patented, reading both patents and literature gives the earliest signal of which code and modality combination is actually closing the gap to fault tolerance — and helps separate demonstrated results from roadmap targets.

The competitive landscape by the numbers

Cypris's corpus puts the quantum error correction / fault-tolerant quantum computing patent family set at roughly 11,928 documents, heavily concentrated in the United States (approximately 5,076 families), followed by China (approximately 1,655) and Canada (approximately 561) (Cypris corpus, indicative; 2025–26 partial). Filing activity has accelerated sharply, from roughly 578 new families in 2020 to about 2,328 in 2025, with 2026 partial at approximately 1,791 (Cypris corpus, indicative; 2025–26 partial). Top assignees are led by superconducting and gate-model incumbents alongside quantum-native firms — Google, IBM, Microsoft, D-Wave, and Rigetti — with Yale, IonQ, Harvard, and MIT also present in the assignee list (Cypris corpus, indicative; 2025–26 partial). A clean split of this corpus by qubit modality and by code/decoder/interconnect layer was not available from this pass; the assignee mix, however, skews toward superconducting and trapped-ion players, consistent with where the demonstrated hardware results described above are concentrated.

Where Cypris fits

Cypris runs patent landscape and white space analysis for fast-moving, deep-technical fields such as fault-tolerant quantum computing across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by qubit modality, superconducting, trapped-ion, neutral-atom, photonic, and bosonic, and by layer, error-correcting code, decoding hardware, and interconnect, and normalizes hardware-vendor, national-lab, and university filers to canonical entities, so a team can resolve which modalities and layers are crowded and which remain open as white space. Semantic search across patents and scientific literature connects filings to the underlying quantum-information-science research, which is where fault-tolerance advances appear first, often well ahead of the patent record. Cypris Q, the platform's agentic layer, lets teams run landscape and white space analysis conversationally and chain the clustering, attribution, and gap analysis, and Agentic Monitoring tracks a defined modality or layer over time and flags new patents and papers as they publish. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.

FAQ

What is fault-tolerant quantum computing? Fault-tolerant quantum computing encodes one error-protected "logical" qubit across many noisy physical qubits using a quantum error-correcting code, targeting "below-threshold" operation, where scaling the code exponentially suppresses the logical error rate. Google demonstrated this directly on superconducting hardware, showing logical error rate falling by roughly 2x per code-distance increment with a logical qubit outliving its best physical qubit¹. It is the prerequisite for running large, reliable quantum programs.

What qubit modalities does the landscape cover? The landscape covers superconducting circuits, trapped ions, neutral atoms, photonic qubits, and bosonic (cat) qubits, each with different native error rates, connectivity, and scaling challenges. No single modality currently dominates the field; Cypris's corpus shows assignees spanning superconducting incumbents (Google, IBM, D-Wave, Rigetti) and trapped-ion and academic players (IonQ, Yale, Harvard, MIT). Each modality is pursued by a differently architected set of developers.

What is a logical qubit, and how many have actually been demonstrated? A logical qubit is an error-protected unit of quantum information built by combining many physical qubits under an error-correcting code. As of the most recent peer-reviewed demonstrations, a neutral-atom platform has shown up to 48 logical qubits with reconfigurable connectivity in an error-detected, largely algorithmic demonstration², and superconducting hardware has demonstrated below-threshold scaling on a smaller logical-qubit count¹. These are well short of the hundreds to thousands of logical qubits that company roadmaps target for the end of the decade⁹.

What claim types create IP activity in fault-tolerant quantum computing? Four layers generate the bulk of IP activity: qubit-modality hardware, error-correcting-code implementation (including the connectivity solutions that make qLDPC codes practical), real-time decoding hardware and software, and interconnect and networking technology. Each is a distinct region of patenting, often held by different companies pursuing different architectural bets. A competitive fault-tolerant system depends on progress across all four.

Where is the white space in fault-tolerant quantum computing? The white space includes qLDPC codes and their connectivity solutions, real-time decoding hardware and software, bosonic/cat-qubit architectures, and quantum networking and multi-node architectures. Qubit-modality IP is comparatively mature and fragmented. The newer error-correcting codes and the classical and networking engineering around them are the most open, high-value ground.

How reliable are company roadmap claims in this field? Company roadmap claims should be read as stated targets, not demonstrated results — IBM's and Quantinuum's own published roadmaps target hundreds to thousands of logical qubits by the end of the decade⁹, well beyond what has been peer-reviewed and demonstrated to date¹,². Distinguishing "demonstrated" from "roadmap target" is essential to reading this field accurately. Analysts and IP teams should trace any specific qubit-count or timeline claim back to its primary source before relying on it.

Why does fault-tolerant quantum computing analysis need scientific literature? Fault-tolerant quantum computing analysis needs scientific literature because error-correcting-code and decoder advances appear in physics research and preprints before they are patented, so the literature gives the earliest signal in a field where major milestones are announced several times a year. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.

Which teams use fault-tolerant quantum computing patent landscape analysis? Fault-tolerant quantum computing patent landscape analysis is used by R&D, IP, and strategy teams at quantum-hardware companies, national laboratories, and technology-company quantum divisions, as well as investors assessing the sector. Because the landscape spans multiple competing qubit modalities and codes at different maturity levels, structured analysis is essential to choosing where to build and where to partner. Cypris serves hundreds of enterprise customers across research-intensive and regulated industries.

Endnotes

1. Bausch J, Malone FD, Martin LS, et al. Quantum error correction below the surface code threshold. Nature. DOI: 10.1038/s41586-024-08449-y.
2. Geim AA, Bluvstein D, Gullans MJ, Kalinowski MW, Maskara N, et al. Logical quantum processor based on reconfigurable atom arrays. Nature. DOI: 10.1038/s41586-023-06927-3.
3. Monroe C, Risinger A, Katz O, Bondurant B, Biswas D. Implementing Real-Time Logical Qubit Error Detection & Correction on a Trapped Ion Quantum Computer. DOI: 10.26226/m.6275705766d5dcf63a311383.
4. Savin V, Vasić B, Raveendran N, Borah SK, Pacenti M. Quantum Low-Density Parity-Check Codes. arXiv. DOI: 10.48550/arxiv.2510.14090.
5. Devulapalli D, Gorshkov AV, Gottesman D, Gullans MJ, Schoute E. Toward a 2D Local Implementation of Quantum Low-Density Parity-Check Codes. PRX Quantum. DOI: 10.1103/prxquantum.6.010306.
6. Liyanage N, Wu Y, Zhong L, Houghton E. Network-Integrated Decoding System for Real-Time Quantum Error Correction with Lattice Surgery. DOI: 10.48550/arxiv.2504.11805.
7. Bigagli N, Shabani J, Namazi M, Cowan TE, Craddock AN. Towards entanglement swapping over 30 km in a three-node metropolitan quantum network in New York City. DOI: 10.1364/quantum.2025.qw4a.7.
8. Albertinale E, Cohen J, Lescanne R, Campagne-Ibarcq P, Sarlette A. Quantum control of a cat qubit with bit-flip times exceeding ten seconds. Nature. DOI: 10.1038/s41586-024-07294-3.
9. IBM Quantum Roadmap (Starling, 2029) and Quantinuum's accelerated roadmap to universal, fully fault-tolerant quantum computing — company technical blogs and press releases.
10. Cypris platform corpus analysis, quantum error correction / fault-tolerant quantum computing patent families. Indicative figures; 2025–2026 partial.

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Electrolysis has become the center of gravity in hydrogen innovation, and the electrolyzer patent landscape is where the clean-hydrogen transition is being contested. A joint study of global patent data by the European Patent Office and the International Energy Agency found that technologies motivated by climate concerns accounted for nearly 80 percent of all hydrogen-production patents by 2020, with growth driven chiefly by a sharp increase in innovation in water electrolysis, and that climate-driven hydrogen technologies generated roughly twice as many international patent families as established, fossil-based methods.¹ The commercial backdrop is a projected expansion of electrolyzer manufacturing on the order of a 65-fold increase in market size over the decade, as countries scale low-emissions hydrogen for hard-to-abate sectors.²,³ For R&D and IP teams, the strategic questions are which electrolyzer technology route to back and where defensible IP positions remain, and both are patent-landscape questions.

The landscape divides across four electrolyzer technologies at different maturity levels, each a distinct region of patenting, and each characterized in the US Department of Energy's comparative assessment of solid-oxide, alkaline, and proton-exchange-membrane electrolyzers.⁴ Alkaline electrolysis is the most mature and lowest-cost route, using a liquid alkaline electrolyte and avoiding scarce precious metals, so its patenting concentrates on efficiency, dynamic operation to follow variable renewable power, and stack scale-up. Proton-exchange-membrane (PEM) electrolysis offers compact, responsive operation well suited to variable renewables but relies on scarce platinum-group catalysts and specialized membranes, so a large share of its patenting targets catalyst loading reduction, membrane durability, and cost.⁵ Solid-oxide electrolysis (SOEC) operates at high temperature with high electrical efficiency and can co-electrolyze to produce syngas, but durability and thermal cycling are the central challenges, so patenting concentrates there. Anion-exchange-membrane (AEM) electrolysis is the newest route, aiming to combine PEM-like performance without precious-metal dependence, and it is the least mature and least crowded, which makes it a notable area of white space; its membranes and non-precious-metal catalysts are an active peer-reviewed research frontier.⁶

Geography and institutional origin further shape the landscape. The EPO and IEA analysis found Europe gaining an edge as a location for electrolyzer innovation and manufacturing investment, while Japan led patenting in hydrogen end-use for the automotive sector, and it noted that momentum in other end-use applications, such as aviation, shipping, and power generation, had not yet matched the attention those sectors receive.¹ The European Commission's Joint Research Centre has separately tracked the status of water electrolysis and hydrogen technology in the European Union, corroborating the region's manufacturing push.⁷ It also found that emerging low-emissions hydrogen carriers, including liquid organic hydrogen carriers and ammonia cracking, grew (by about 12.5 percent and 7.8 percent in international patent families respectively) with roughly half of that activity originating in universities and public research, an early-stage signal of where future commercial IP may form.¹ Because applications publish about eighteen months after filing, the most recent activity, particularly in the newer AEM and SOEC routes, is under-represented, so the current frontier is more active than granted-patent counts suggest.

The four electrolyzer routes and where white space sits

Alkaline. The most mature and lowest-cost route, avoiding precious metals; patenting concentrates on efficiency, dynamic operation, and scale-up, so it is comparatively crowded on core design.

PEM. Compact and responsive but reliant on platinum-group catalysts and specialized membranes; white space centers on catalyst reduction, membrane durability, and cost.

SOEC. High-temperature and high-efficiency with co-electrolysis potential, but durability and thermal cycling are the open problems where patenting and white space concentrate.

AEM. The newest route, aiming for PEM-like performance without precious metals; the least mature and least crowded, and therefore a notable area of white space.²

Carriers and end-use. Liquid organic hydrogen carriers and ammonia cracking are early-stage and university-driven, and several end-use sectors beyond automotive remain comparatively under-patented.¹

How AI-powered landscape and white space analysis helps

Resolving four technology routes at different maturities, across geographies and institutions, requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by route and by the problem being solved across varied terminology, attribution that normalizes filers to canonical entities and distinguishes university from commercial activity, and continuous monitoring that tracks the newer routes where recent activity is under-represented. Because electrolyzer advances appear in scientific literature before they are patented, reading both patents and literature gives the earliest signal of where the frontier and the white space are moving.

Where Cypris fits

Cypris runs patent landscape and white space analysis for multi-route energy fields such as hydrogen electrolysis across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by electrolyzer route, alkaline, PEM, SOEC, and AEM, and by the problem being solved, and normalizes filers to canonical entities, so a team can resolve which routes and problems are crowded and which, such as AEM and SOEC durability, remain open as white space. Semantic search across patents and scientific literature connects filings to the underlying materials and engineering research, which is where electrolyzer advances appear first, and distinguishes university from commercial activity. Cypris Q, the platform's agentic layer, lets teams run landscape and white space analysis conversationally and chain the clustering, attribution, and gap analysis, and Agentic Monitoring tracks a defined route over time and flags new patents and papers as they publish, which is essential where the newest routes are under-represented by publication lag. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.

FAQ

Why is electrolysis the focus of hydrogen patenting?

Electrolysis is the focus of hydrogen patenting because it can produce hydrogen with zero direct emissions when powered by renewable or nuclear electricity. A joint EPO and IEA study found that climate-motivated technologies accounted for nearly 80 percent of hydrogen-production patents by 2020, with growth driven chiefly by a surge in electrolysis. Climate-driven hydrogen technologies generated roughly twice the international patent families of established methods.

What are the main electrolyzer technologies?

The main electrolyzer technologies are alkaline, proton-exchange-membrane (PEM), solid-oxide (SOEC), and anion-exchange-membrane (AEM). They differ in maturity, cost, materials, and operating conditions, and each occupies a distinct region of the patent landscape. Alkaline is the most mature and AEM the newest.

Where is the white space in the electrolyzer patent landscape?

The white space in the electrolyzer patent landscape is concentrated in anion-exchange-membrane electrolysis, which is the newest and least crowded route, in solid-oxide durability and thermal cycling, in reducing precious-metal catalyst use and improving membrane durability in PEM, and in early-stage hydrogen carriers such as liquid organic carriers and ammonia cracking. Core alkaline design is comparatively crowded. The higher-value opportunities are in the newer routes and unsolved durability problems.

How do the electrolyzer routes trade off?

The electrolyzer routes trade off maturity, cost, and materials. Alkaline is mature and low-cost but less dynamic; PEM is responsive but relies on scarce platinum-group metals; SOEC is highly efficient but faces durability challenges; and AEM aims to combine PEM-like performance without precious metals but is the least mature. Each route's patenting concentrates on its specific weakness.

How fast is the electrolyzer market expected to grow?

The electrolyzer market is expected to grow rapidly, with the IEA projecting an expansion on the order of a 65-fold increase in market size over the decade as countries scale low-emissions hydrogen. This growth is the commercial driver behind the surge in electrolysis patenting. It also raises the value of securing defensible IP positions early.

Which regions lead electrolyzer innovation?

The EPO and IEA analysis found Europe gaining an edge as a location for electrolyzer innovation and manufacturing investment, while Japan led hydrogen end-use patenting in the automotive sector. Momentum in several other end-use sectors had not yet matched the attention they receive. The geographic distribution differs by technology route and end-use.

Why does electrolyzer analysis need scientific literature?

Electrolyzer analysis needs scientific literature because materials and engineering advances, particularly in catalysts, membranes, and the newer routes, appear in research before they are patented, so the literature gives the earliest signal. Analyzing patents alone gives a lagging view, and much early activity is university-driven. Cypris analyzes both across more than 500 million patents and scientific papers.

Which teams use electrolyzer patent landscape analysis?

Electrolyzer patent landscape analysis is used by R&D, innovation, IP, and strategy teams at electrolyzer and equipment makers, energy and industrial-gas companies, materials developers, and their partners, as well as investors. It informs which route to back, where to file, and where competitors are concentrated. Cypris serves hundreds of enterprise customers across energy, advanced materials, chemicals, and other regulated industries.

How do you keep an electrolyzer landscape current?

Keeping an electrolyzer landscape current requires continuous monitoring, because the field moves quickly, the newer routes are advancing, and publication lag under-represents the most recent activity. A one-time landscape ages quickly. Cypris uses Agentic Monitoring to track a defined route and flag new patents and papers as they publish.

Endnotes

1. European Patent Office & International Energy Agency (2023). Hydrogen patents for a clean energy future: A global trend analysis of innovation along hydrogen value chains. https://www.iea.org/reports/hydrogen-patents-for-a-clean-energy-future
2. International Energy Agency, reported via World Economic Forum (2023). Hydrogen patent filings: Europe and Japan lead on innovation (projected ~65-fold electrolyzer market growth this decade). https://www.weforum.org/stories/2023/03/hydrogen-innovation-patents-technology/
3. International Energy Agency. Global Hydrogen Review (annual series). https://www.iea.org/reports/global-hydrogen-review-2024
4. Kelly, J. C., Elgowainy, A. & Iyer, R. (2022). Electrolyzers for Hydrogen Production: Solid Oxide, Alkaline, and Proton Exchange Membrane. US Department of Energy (OSTI). https://www.osti.gov/
5. US Department of Energy (2024). Hydrogen Shot: Water Electrolysis Technology Assessment. https://www.energy.gov/
6. Zhang, M. et al. (2024). Advanced development of anion-exchange membrane electrolyzers for hydrogen production: from anion-exchange membranes to membrane electrode assemblies. Chemical Communications. https://doi.org/10.1039/D3CC05904A
7. European Commission Joint Research Centre (2023). Water electrolysis and hydrogen in the European Union: Status Report on Technology Development, Trends, Value Chains and Markets. https://publications.jrc.ec.europa.eu/

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Energy storage is one of the fastest-growing domains of patenting. A joint analysis by the International Energy Agency and the European Patent Office found that patenting in batteries and electricity storage grew at an average of 14 percent per year between 2005 and 2018, roughly four times faster than the all-technology average, across more than 65,000 international patent families, with batteries accounting for the large majority of electricity-storage patenting.¹ More recent IEA analysis reports that batteries have come to dominate the energy patent landscape.² The drivers are structural: the electrification of transport, the decarbonization of the grid, and the need for long-duration storage to balance intermittent renewable generation. These forces have pushed research and filing activity up sharply across several distinct storage technologies at once, and much of the technology that will define the market at the end of the decade is entering the patent record now.

The energy-storage landscape is not a single field but a set of competing technology routes at different technology-readiness levels, and a rigorous landscape has to segment them. Lithium-ion remains the incumbent, with filing activity concentrated on energy density, fast charging, safety, and cell-to-pack manufacturing. Solid-state batteries have seen filing activity grow several-fold since the late 2010s, and the locus of innovation has shifted from electrolyte materials discovery toward interfacial engineering and scalable manufacturing, a transition documented across recent reviews of all-solid-state commercialization.³,⁴ Within that route, the principal electrolyte classes, sulfide, oxide, polymer, and composite, present different trade-offs: sulfide solid electrolytes reach room-temperature ionic conductivities on the order of 10 to the minus three siemens per centimeter, comparable to conventional liquid electrolytes, but the dominant technical barriers are interfacial resistance, electrochemical stability at the electrode interfaces, dendrite suppression, and scalable synthesis of the electrolyte.³,⁴,⁵ Hydrogen storage, particularly solid-state routes using metal hydrides, has surged as fuel-cell and stationary applications advance, with claim activity concentrated on intermetallic alloy families and multi-phase crystal-structure engineering to balance gravimetric capacity against kinetics and operating pressure. Long-duration and grid-scale storage is an active emerging area, where vanadium redox and other flow batteries, compressed-air storage, iron-air chemistries, and thermal and gravity approaches compete, and a large share of the relevant patents are still pending.

That segmentation is the value of patent landscape and white space analysis for the energy transition. A landscape maps where filing activity concentrates, which routes and sub-classes are crowded, and which organizations are most active; a white space analysis maps where activity is sparse, revealing directions where a defensible position is still available. In a field advancing this quickly, where the architectures that will define the 2030 market are being filed today, the ability to resolve both the dense and the sparse regions, at the level of specific technology routes and sub-classes, and to track how they shift, is what converts patent data into strategic positioning.

Why energy patenting is surging

Transport electrification. The transition to electric vehicles drives intense filing in battery chemistries, energy density, fast charging, safety, and manufacturing.

Grid decarbonization. Balancing intermittent renewables requires storage, which drives filing in grid-scale and long-duration technologies.

Long-duration storage demand. Storing energy over many hours or seasonally has pushed activity into flow, compressed-air, iron-air, thermal, and hydrogen routes at differing readiness levels.

Materials and interface innovation. Much of the activity is in materials and interfaces, from solid electrolytes and metal hydrides to electrode-electrolyte engineering, where the underlying research is published before it is patented.

Publication lag. The most recent filings are under-represented because applications publish about eighteen months after their priority date, so current activity is larger than the latest figures show.

How to run an energy patent landscape and white space analysis

Scope the technology space with classification codes, selecting the relevant Cooperative Patent Classification and International Patent Classification categories for the storage routes and sub-classes in view, so the boundary is standardized and reproducible.

Aggregate to the patent-family level, so international coverage of a single invention is not double-counted and volume reflects distinct R&D.

Segment by technology route, separating lithium-ion, solid-state and its electrolyte classes, metal-hydride hydrogen storage, and the long-duration routes, since each is at a different readiness level and must be assessed on its own terms.

Cluster activity by concept using semantic analysis over classification and text, so related work groups together across the varied terminology of materials, chemistries, and architectures.

Map the dense and sparse regions and attribute activity to canonical organizations, identifying crowded sub-classes and open white space and resolving assignee variants to single entities.

Correct for publication lag and monitor continuously, discounting the most recent windows and tracking the landscape over time, because a static snapshot ages quickly in a fast-moving field.

Where Cypris fits

Cypris runs patent landscape and white space analysis for fast-moving fields such as the energy transition across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. That structure lets Cypris segment energy-storage activity by technology route and cluster it by concept across the varied terminology of materials, chemistries, and architectures, so a team can resolve which routes and sub-classes are crowded and which remain open as white space. Dense semantic search across patents and scientific literature connects filings to the underlying materials and interface research, which matters in energy storage because the earliest signals appear in the literature before patents. Cypris Q, the platform's agentic layer, lets teams run landscape and white space analysis conversationally and chain the classification, clustering, attribution, and gap analysis. Agentic Monitoring tracks a defined storage route over time and flags new patents and papers as they publish, which is essential where recent activity is under-represented by publication lag. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.

FAQ

Why is energy storage one of the fastest-growing patent areas?

Energy storage is one of the fastest-growing patent areas because of transport electrification, grid decarbonization, and the need for long-duration storage. A joint IEA and EPO analysis found battery and electricity-storage patenting grew about 14 percent per year from 2005 to 2018, roughly four times the all-technology average, across more than 65,000 international patent families. More recent IEA analysis reports that batteries now dominate the energy patent landscape.

What technology routes does the energy-storage patent landscape cover?

The energy-storage patent landscape covers several competing routes at different readiness levels, including lithium-ion, solid-state batteries with sulfide, oxide, polymer, and composite electrolytes, metal-hydride hydrogen storage, and long-duration routes such as flow, compressed-air, iron-air, thermal, and gravity storage. Each is a distinct route with its own activity level and technical barriers. A landscape analysis segments these rather than treating storage as one field.

What is a patent landscape analysis for the energy transition?

A patent landscape analysis for the energy transition maps where filing activity concentrates across energy-storage routes, which sub-classes are crowded, and which organizations are most active, scoped by classification codes and aggregated to the patent-family level. It gives R&D and IP teams a structured, reproducible view of a fast-moving field. Paired with white space analysis, it also identifies the sparse regions where a defensible position is still available.

How do you find white space in energy-storage patents?

‍Finding white space in energy-storage patents means mapping patents and scientific literature across the routes, clustering activity by concept, and identifying the sparse sub-classes where few patents exist. Because materials and interface research is published before it is patented, literature coverage reveals white space earlier. The sparse regions indicate directions where a team can still build a novel, defensible position.

Why use classification codes and patent families in an energy landscape?

Classification codes scope the technology space in a standardized, reproducible way independent of applicant terminology, and patent-family aggregation avoids double-counting the multiple international applications a single invention generates. Together they make the landscape accurate and comparable across competitors. Keyword-only scoping and document-level counting distort both boundary and volume.

What are the main technical barriers in solid-state batteries?

The main technical barriers in solid-state batteries are interfacial resistance and stability at the electrode-electrolyte interfaces, dendrite suppression, and scalable synthesis and manufacturing of the solid electrolyte. Sulfide electrolytes reach ionic conductivities comparable to liquid electrolytes, so the current focus has shifted from materials discovery toward interface engineering and manufacturing. Patent activity reflects this shift.

Why does publication lag matter in energy patent landscapes? Publication lag matters because applications publish about eighteen months after their priority date, so the most recent filing activity is under-represented in current data. In a fast-moving field like energy storage, apparent softness in the latest window is usually an artifact of lag rather than a real slowdown. Longer-window trends and continuous monitoring are more reliable than the latest figures alone.

Why does energy patent analysis need scientific literature?

‍Energy patent analysis needs scientific literature because much of the innovation is in materials and interfaces, which are typically published in research before they are patented. Analyzing patents alone gives a lagging view, while adding literature reveals emerging activity earlier. Cypris analyzes both across more than 500 million patents and scientific papers.

How do you keep an energy patent landscape current?

Keeping an energy patent landscape current requires continuous monitoring, because the field moves quickly, new filings and research publish constantly, and publication lag hides the most recent activity. A one-time landscape ages fast. Cypris uses Agentic Monitoring to track a defined storage route over time and flag new patents and papers as they publish.

Who uses patent landscape analysis for the energy transition?

Patent landscape analysis for the energy transition is used by R&D, innovation, IP, and strategy teams at battery makers, automotive and energy companies, materials developers, and their partners. It informs where to invest, where to file, and where competitors are concentrating. Cypris serves hundreds of enterprise customers across energy, advanced materials, chemicals, and other regulated industries.

Works Cited

1. International Energy Agency & European Patent Office (2020). Innovation in Batteries and Electricity Storage: A Global Analysis Based on Patent Data. https://www.iea.org/reports/innovation-in-batteries-and-electricity-storage
2. International Energy Agency (2026). The State of Energy Innovation 2026. https://www.iea.org/reports/the-state-of-energy-innovation-2026
3. Kim, J.-J. et al. (2026). Key Challenges and Strategies for Commercialization of All-Solid-State Batteries: Materials, Interface Engineering, and Manufacturing Processes. International Journal of Energy Research. https://doi.org/10.1155/er/8704807
4. Liu, Q. et al. (2023). Interfacial Modification, Electrode/Solid-Electrolyte Engineering, and Monolithic Construction of Solid-State Batteries. Electrochemical Energy Reviews. https://doi.org/10.1007/s41918-022-00167-1
5. Gamo, H., Nagai, A. & Matsuda, A. (2023). Toward Scalable Liquid-Phase Synthesis of Sulfide Solid Electrolytes for All-Solid-State Batteries. Batteries. https://doi.org/10.3390/batteries9070355

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The Model Context Protocol has become the connective tissue between AI assistants and the specialized data that R&D and IP teams depend on. Instead of copying patent claims into a chat window or pasting abstracts from a database, a team can connect an AI client directly to patent and scientific literature sources and work in natural language. But 2026 has surfaced a sharper distinction than "which server connects to which database." The more important question for innovation leaders is whether a server is a single-source connector or a domain-oriented intelligence layer built to support the actual decisions in an R&D and IP stage-gate process. This ranked guide covers the most capable options available today, leading with the one built for end-to-end R&D workflows and following with the strongest open-source connectors for teams assembling their own stack.

A note on method before the list. Every open-source server below is a real, publicly available project with a verifiable repository or registry listing. The ranking weighs how well a server supports actual R&D and IP decisions, alongside breadth of data coverage, depth of available tools, maintenance signals, and usability for a non-developer working through an AI client rather than the command line.

1. Cypris

Most MCP servers in this space answer a narrow question: search this database, retrieve that document. Cypris approaches the problem from the opposite direction, as a domain-oriented intelligence layer designed for the agents that map to real R&D and IP stage gates rather than for one-off lookups. The distinction matters because innovation decisions are not single queries; they are structured workflows where prior art, white space, freedom to operate, and regulatory signals each gate a project's progress.

That orientation is what sets it at the top of this list. Cypris is built to support prior art agents that surface relevant disclosures before a program commits resources, white space agents that identify uncontested technical territory, freedom-to-operate agents that flag blocking risk, and regulatory agents that track the filings and approvals shaping a field. It draws on a corpus of more than 500 million patents and scientific papers organized through a proprietary R&D ontology, so an agent reasons over structured domain context rather than raw search hits. Cypris Q, the platform's agentic layer, and enterprise API partnerships with OpenAI, Anthropic, and Google are what make this accessible to Fortune 500 R&D teams inside their own AI environments. It meets enterprise-grade security requirements, which is the threshold for deployment at that scale. For organizations whose AI agents need to fit the stage-gate process rather than just query a database, this is the layer built for the job.

2. USPTO Patent MCP Server (riemannzeta/patent_mcp_server)

The most substantial single-source connector in the public ecosystem. It is a FastMCP server for accessing United States Patent and Trademark Office patent and application data through the Patent Public Search API, the Open Data Portal API, PTAB API v3, and Patent Litigation APIs, letting an AI client search granted patents and applications, work through PTAB proceedings, analyze litigation, and research prosecution history. GitHub

What earns it credibility is its transparency about API churn. It provides 52 tools across 6 USPTO data sources, of which 27 are active and 25 are unavailable due to API shutdowns. Notably, the PatentsView API was shut down on March 20, 2026 with data migrated to ODP bulk datasets, and the Office Action and Enriched Citation APIs were decommissioned in early 2026. The affected tools remain registered and return workaround guidance rather than failing silently. For US-centric patent work assembled in-house, this is the strongest starting point. GitHubGitHub

3. OpenPharma Patents MCP (openpharma-org/patents-mcp)

Broader in geography than the USPTO server. It accesses patent data from multiple sources including the USPTO and Google Patents, offering Patent Public Search, the Open Data Portal for metadata and assignment data, and Google Patents access to 90 million-plus publications across 17-plus countries via Google BigQuery, spanning US, EP, WO, JP, CN, KR, GB, DE, FR, CA, AU and more. The tradeoff is setup friction: the Google Patents tools require a Google Cloud project with BigQuery access and a service account key, and the ODP tools require a USPTO API key. That puts full functionality slightly beyond a non-technical user, but for global patent landscape work the breadth is hard to match. GitHub + 2

4. Patent Connector (patent.dev)

The most approachable option for European coverage. It is a Model Context Protocol server in open beta that connects ChatGPT Desktop, Claude Desktop, and other MCP-compatible tools directly to patent databases, starting with the free EPO Open Patent Services API, with data drawn from the EPO's bibliographic, legal event, full-text and image databases, the same sources behind Espacenet and the European Patent Register. The EPO OPS API is free to use after registering for credentials, with a non-paying tier available. Its accuracy argument is genuine: general tools reaching Google Patents through web search tend to confuse filing and publication dates or extract incomplete claim text, which a dedicated retrieval layer avoids. Patent + 2

5. Google Patents MCP (KunihiroS/google-patents-mcp)

A focused single-purpose server. It searches Google Patents via the SerpApi Google Patents API and can be installed for Claude Desktop automatically via Smithery, requiring a SerpApi API key provided as an environment variable. It supports filtering by country and other parameters. The dependency on a third-party paid API is the main consideration, but for natural-language Google Patents search it does one job well. GitHubGitHub

6. Paper Search MCP (openags/paper-search-mcp)

Crossing into scientific literature, this is the broadest paper-retrieval server available. It offers multi-source search and download across arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic Scholar, Crossref, OpenAlex, PubMed Central, CORE, Europe PMC, and more, following a free-first design that prioritizes open and public sources with optional API-key enhancement. For literature coverage breadth, nothing else in the open ecosystem comes close. MCP ServersMCP Servers

7. Academic MCP Server (nanyang12138/Academic-MCP-Server)

A solid scientific-literature connector. It supports six databases: PubMed, bioRxiv, medRxiv, arXiv, Semantic Scholar, and Sci-Hub, with advanced search by title, author, and date range. A practical caveat for enterprise use: the Sci-Hub integration carries copyright considerations, and teams should rely on the legitimate sources and obtain papers through proper channels. GitHub

8. Academia MCP (IlyaGusev/academia_mcp)

The most workflow-oriented of the open paper servers. It searches across arXiv, ACL Anthology, HuggingFace Datasets, and Semantic Scholar, and adds tools to list citing and referenced papers, download and review PDFs, and answer questions over document chunks, though the LLM-powered tools require an OpenRouter API key. For literature-review workflows rather than plain retrieval, it's the most capable open option. MCP ServersMCP Servers

How to choose

The open-source servers in positions two through eight are excellent point connectors: pick one by the database you need and the client you use, and accept that you are assembling and maintaining the integration yourself. The reason Cypris leads is that an R&D organization rarely needs a single database; it needs agents that carry domain context across the prior art, white space, freedom-to-operate, and regulatory decisions that gate a program. That is an intelligence-layer problem, not a connector problem, which is the line separating the top of this list from the rest of it.

Frequently Asked Questions

What is an MCP server for patents and papers?An MCP server is a connector built on the Model Context Protocol that links an AI client such as Claude Desktop or ChatGPT Desktop directly to a data source. For patents and papers, that means an AI assistant can search and retrieve patent documents, claims, and scientific literature in natural language, without a user manually copying results between a database and a chat window. Most public servers connect to a single source or family of sources; a smaller number act as broader intelligence layers that support full R&D workflows.

What is the best MCP server for R&D and IP workflows in 2026?For end-to-end R&D and IP work, Cypris is built specifically for the agents that map to stage-gate decisions: prior art, white space, freedom to operate, and regulatory analysis. It functions as a domain-oriented intelligence layer over a corpus of more than 500 million patents and scientific papers organized through a proprietary R&D ontology, rather than as a single-database connector. For teams that need a connector to one specific source, the strongest open-source options are the USPTO Patent MCP Server for US data and Paper Search MCP for scientific literature.

Is there an MCP server that covers both patents and scientific papers?Yes, in two senses. Cypris spans both patents and scientific papers within a single intelligence layer built for R&D decisions. Among open-source connectors, the breadth is usually split: patent servers like OpenPharma Patents MCP focus on patent sources, while paper servers like Paper Search MCP cover scientific literature. Teams assembling their own stack often run one of each.

What is the most capable open-source patent MCP server?The USPTO Patent MCP Server is the deepest single-source option. It accesses USPTO data through the Patent Public Search API, the Open Data Portal API, PTAB API v3, and litigation APIs, supporting patent search, PTAB proceedings, litigation analysis, and prosecution history research. Its maintainers are transparent that a portion of its tools are currently inactive due to USPTO API shutdowns in early 2026, which is a useful signal of honest maintenance.

Which MCP server is best for European patent data?Patent Connector is the most approachable option for European coverage. It connects MCP-compatible clients to the EPO's Open Patent Services API, drawing on the same bibliographic, legal-event, full-text, and image databases that power Espacenet and the European Patent Register. The EPO OPS API is free to use after registering for credentials, with a non-paying tier available.

Which MCP server covers the most scientific literature sources?Paper Search MCP has the broadest coverage, spanning arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic Scholar, Crossref, OpenAlex, PubMed Central, CORE, Europe PMC, and more. It uses a free-first design that prioritizes open sources, with optional API keys to raise rate limits on services like Semantic Scholar.

Do MCP servers for patents require API keys?It varies. Some, like Patent Connector using the EPO's free OPS tier, work with free credentials. Others require paid third-party keys, such as the Google Patents MCP server's dependency on a SerpApi key, or cloud setup, such as OpenPharma's need for a Google Cloud BigQuery project and a USPTO Open Data Portal key. Enterprise platforms like Cypris are accessed through enterprise API arrangements rather than self-service keys.

What is the difference between a single-source connector and an intelligence layer?A single-source connector answers a narrow question: search this database, return these documents. An intelligence layer is built to support a structured decision process, where domain context carries across multiple linked questions. In R&D and IP, those questions are the stage gates, prior art, white space, freedom to operate, and regulatory, and an intelligence layer like Cypris is designed so agents reason across them rather than treating each as an isolated lookup.

Can these MCP servers handle freedom-to-operate or white space analysis?The open-source connectors retrieve the underlying data a human or agent would need, but they do not themselves perform freedom-to-operate or white space analysis; that logic sits with whatever agent or analyst uses them. Cypris is built the other way around, with agents oriented to those specific analyses, drawing on its ontology-structured corpus to support the decision rather than just return search results.

How should an R&D team choose among these servers?Teams that need a single database and are comfortable building and maintaining an integration should pick an open-source connector by source and client compatibility. Teams that need agents to carry domain context across the full R&D and IP stage-gate process, rather than querying one source at a time, should evaluate an intelligence layer such as Cypris. The deciding question is whether the need is retrieval from one source or reasoning across a workflow.

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Radioligand therapy has become one of the fastest-growing modalities in oncology, and its patent landscape is distinctive because a radiopharmaceutical is a modular product assembled from independently patentable parts. A radioligand therapy joins a radioactive isotope to a targeting molecule, an antibody, peptide, or small molecule that homes to a tumor marker, through a chelator that holds the isotope and a linker that connects the pieces, and it is delivered under a specific dosing regimen and produced by a specialized, time-critical manufacturing process. Peer-reviewed analyses of the modality describe exactly this layered structure, spanning the radioisotope and its decay-chain radiochemistry and quality control, the chelator, and the targeting vector.¹,²,³ A notable feature of the record is that most patent families in this space disclose the vector, the radiolabel, and the chelator together rather than in isolation, so the layers, while conceptually distinct, are rarely cleanly separated, which is itself a landscape finding. Because these elements can nonetheless be claimed separately and are often held by different owners, freedom-to-operate for a new radioligand therapy is a multi-layer, multi-owner analysis rather than a single clearance.

The commercial and deal environment has raised the stakes across every layer. The approval and rapid uptake of the first marketed radioligand therapies validated the model: Lutathera (lutetium-177 dotatate) was approved in 2017 in the European Union and 2018 in the United States and expanded to pediatric patients aged twelve and older with gastroenteropancreatic neuroendocrine tumors in April 2024,⁴ and Pluvicto (lutetium-177 vipivotide tetraxetan) received initial US approval in 2022.⁵ A wave of multi-billion-dollar acquisitions followed as large pharmaceutical companies, including Bristol Myers Squibb, Eli Lilly, AstraZeneca, and Novartis, bought their way into targeting platforms and, increasingly, into isotope access; Bristol Myers Squibb's all-cash acquisition of RayzeBio, at roughly $4.1 billion in equity value ($62.50 per share), is representative of the scale of the deals.⁶ Because radiopharmaceuticals decay on a clock, the supply and quality control of the isotope, particularly short-lived alpha-emitters, has become a strategic constraint, and it remains a named bottleneck in the peer-reviewed literature.³ The patent picture reflects both the therapy and the infrastructure around it: across the Cypris corpus of more than 500 million patents and scientific papers, the radioligand- and radionuclide-therapy space holds roughly 25,600 de-duplicated families and has grown about 2.8 times from 2015 to 2024, with the United States far in front on geography, followed by Germany, Switzerland, and China, and the most active assignees spanning pharmaceutical companies, imaging and isotope suppliers, and academic medical centers. Because applications publish about eighteen months after filing, the newest chelator, ligand, and manufacturing filings are under-represented (2025 and 2026 counts are partial), so the current frontier is more active than granted-patent counts suggest.

The strategic picture turns on two shifts. First, the field is moving from beta-emitting isotopes toward more potent, shorter-range alpha-emitters such as actinium-225, whose high-energy, short-range emissions can raise tumor cell kill while limiting marrow toxicity.⁷,⁸,⁹ That shift is visible but still early in the patent record: across the Cypris corpus, the beta-emitter slice outweighs the alpha-emitter slice by roughly 1.8 to 1 on an indicative basis, so the field remains beta-dominant even as the alpha share rises quickly. The shift also changes the IP, because the chelators, purification methods, and supply chains for alpha-emitters are distinct and less mature; specialized macrocyclic chelators developed for actinium, and computational chelator-design methods, define much of this layer, and theranostic chelators that pair imaging and therapy add another dimension.¹⁰,¹¹,¹² Second, because a working therapy requires rights across the isotope, the chelator, the ligand, and the manufacturing chain, licensing structure matters as much as claim scope, and newer targeting vectors, such as somatostatin-receptor agonists and antagonists for neuroendocrine tumors, open fresh ligand-layer positions.¹³ Reading the landscape by layer and by owner, and tracking both the patents and the underlying chemistry and nuclear-medicine research, is what separates a workable position from a blocked one.

What creates FTO risk in radioligand therapy

Isotope and isotope-production claims. These cover the radioisotope and the methods to produce, purify, and quality-control it, an increasingly contested layer as alpha-emitter supply becomes a bottleneck.²,³

Chelator and linker claims. These cover the chemistry that holds the isotope and connects it to the targeting molecule, a distinct and heavily engineered layer, especially for alpha-emitters.¹⁰

Targeting ligand claims. These cover the antibody, peptide, or small molecule that directs the therapy to a tumor marker, often the most visible layer and a frequent source of competition.¹³

Combination and regimen claims. These cover pairings with other agents and specific dosing schedules, which can independently block a competing label.

Manufacturing and supply-chain claims. These cover the time-critical production, formulation, and distribution of a decaying product, where practical, hard-to-design-around barriers concentrate.³

How AI-powered landscape and FTO analysis helps

A modular, multi-owner, supply-constrained landscape is beyond manual clearance. AI-powered analysis addresses this with semantic search that retrieves relevant isotope, chelator, ligand, combination, and manufacturing claims regardless of terminology, attribution that resolves the many owners and the license and acquisition chains to canonical entities, claim-level analysis that separates the layers, and continuous monitoring that tracks new filings, supply agreements, and deals. Because radiopharmaceutical advances appear in scientific and nuclear-medicine literature before they are patented, reading both patents and literature gives earlier warning of where the field is heading.

Where Cypris fits

Cypris runs patent landscape and freedom-to-operate analysis for modular, contested fields such as radioligand therapy across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters the landscape by layer, isotope, chelator, targeting ligand, combination, and manufacturing, and normalizes owners and their acquisition chains to canonical entities, so a team sees how rights and isotope access are distributed across many parties rather than a flat list. Semantic search across patents and scientific literature surfaces relevant claims regardless of terminology and connects filings to the underlying chemistry and nuclear-medicine research, which is where new chelators, alpha-emitter methods, and ligands emerge first. Cypris Q, the platform's agentic layer, lets teams run landscape and FTO analysis conversationally and chain the attribution, clustering, and claim-level analysis across layers, and Agentic Monitoring tracks the landscape over time and flags new filings and developments as they publish. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.

FAQ

Why is freedom-to-operate hard for radioligand therapy? Freedom-to-operate is hard for radioligand therapy because a radiopharmaceutical is assembled from several independently patentable layers, the isotope, the chelator, the targeting ligand, the combination regimen, and the manufacturing and supply chain, often held by different owners. In practice most filings disclose several layers together. FTO is therefore a multi-layer, multi-owner analysis that must also account for isotope access.

Why is isotope supply a strategic issue? Isotope supply is strategic because radiopharmaceuticals decay on a clock, so a therapy is only viable if the isotope can be produced, purified, and delivered on time, and the supply of short-lived alpha-emitters is constrained. The preparation and quality control of actinium-225 in particular remain named bottlenecks in the literature. The isotope-production and purification layer is increasingly patented and contested.

What claim types create FTO risk in radioligand therapy? Five claim types create FTO risk: isotope and isotope-production claims, chelator and linker claims, targeting ligand claims, combination and regimen claims, and manufacturing and supply-chain claims. Each covers a distinct layer and can be held by a different owner. The chelator and isotope-production layers are especially decisive for alpha-emitters.

Why is the shift from beta to alpha emitters important? The shift matters because alpha-emitters are more potent over a shorter range, but their chelators, purification methods, and supply chains are distinct and less mature than those for beta-emitters. In the patent record the field is still beta-dominant, with alpha rising quickly. That immaturity opens white space for developers who solve the chemistry and supply problems.

Where is the white space in radiopharmaceuticals? The white space sits in alpha-emitter chelators and purification, isotope-production methods, novel targeting ligands for markers beyond the most crowded targets, and time-critical manufacturing and distribution. The leading targets and beta-emitter chemistries are comparatively crowded. The higher-value opportunities are in the alpha-emitter and supply layers.

Why does radiopharmaceutical analysis need scientific literature? Radiopharmaceutical analysis needs scientific literature because chelator, isotope, and ligand advances appear in chemistry and nuclear-medicine research before they are patented, so the literature gives the earliest signal. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.

What software helps analyze the radiopharmaceutical patent landscape? Software for the radiopharmaceutical landscape should resolve owners, acquisition chains, and isotope access to canonical entities, cluster the isotope, chelator, ligand, and manufacturing layers, search patents and scientific literature semantically, and monitor deals and new filings continuously. Cypris does this across more than 500 million patents and scientific papers using a proprietary R&D ontology, semantic search, Cypris Q, and Agentic Monitoring.

Which teams need radiopharmaceutical patent landscape and FTO analysis? Radiopharmaceutical patent landscape and FTO analysis is needed by R&D, IP, and business-development teams at radiopharma and oncology companies, isotope producers, and their partners, as well as investors assessing radioligand assets. The modular, supply-constrained, deal-driven landscape makes structured analysis essential. Cypris serves hundreds of enterprise customers across pharmaceuticals and other research-intensive industries.

Endnotes

1. Andrade, D. B., Chen, S., Lee, S. T., Vallis, K. A., et al. (2024). A meta-analysis and meta-regression of PSMA radioligand therapy utilising lutetium-177 and actinium-225 in metastatic prostate cancer. European Urology. https://doi.org/10.1016/j.eururo.2024.09.020
2. Seimbille, Y., de Blois, E., Morgenstern, A., et al. (2025). Ac-225 radiochemistry through the lens of [225Ac]Ac-DOTA-TATE. EJNMMI Radiopharmacy and Chemistry, 10. https://doi.org/10.1186/s41181-025-00332-z
3. Duatti, A. (2025). Open problems for the preparation and quality control of Ac-225 radiopharmaceuticals. Current Radiopharmaceuticals. https://doi.org/10.1016/j.craph.2025.100004
4. U.S. Food and Drug Administration (2024, April 23). FDA approves lutetium Lu 177 dotatate for pediatric patients 12 years and older with GEP-NETS. https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-lutetium-lu-177-dotatate-pediatric-patients-12-years-and-older-gep-nets
5. U.S. Food and Drug Administration (2022). PLUVICTO (lutetium Lu 177 vipivotide tetraxetan) prescribing information. https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/215833s000lbl.pdf
6. Bristol Myers Squibb (2023). Broadening our oncology capabilities with the acquisition of RayzeBio (Exhibit 99.1), U.S. Securities and Exchange Commission. https://www.sec.gov/Archives/edgar/data/14272/000114036123059406/ny20017436x1_ex99-1.htm
7. Bishnoi, K., Panda, A. K., Parida, G. K., & Agrawal, K. (2023). Efficacy and safety of Ac-225 PSMA radioligand therapy in metastatic prostate cancer: a systematic review and meta-analysis. Medical Principles and Practice, 32(3). https://doi.org/10.1159/000531246
8. Pini, C., Gelardi, F., Gandaglia, G., et al. (2025). Time for action: actinium-225 PSMA-targeted alpha therapy for metastatic prostate cancer — systematic review and meta-analysis. Theranostics, 15. https://doi.org/10.7150/thno.106574
9. Treglia, G., Impériale, A., Paone, G., et al. (2025). Efficacy and safety of radioligand therapy with actinium-225 DOTATATE in neuroendocrine neoplasms: a systematic review and meta-analysis. Medicina, 61(8), 1341. https://doi.org/10.3390/medicina61081341
10. Thiele, N. A., Radchenko, V., Ramogida, C. F., Wilson, J. J., et al. (2017). An eighteen-membered macrocyclic ligand for actinium-225 targeted alpha therapy. Angewandte Chemie International Edition, 56(46). https://doi.org/10.1002/anie.201709532
11. Stein, B. W., Lilley, L. M., Batista, E. R., et al. (2020). Computer-assisted design of macrocyclic chelators for actinium-225 radiotherapeutics. Inorganic Chemistry, 59(21). https://doi.org/10.1021/acs.inorgchem.0c02432
12. Comba, P., Zarschler, K., Stephan, H., et al. (2022). Toward personalized medicine: one chelator for imaging and therapy with lutetium-177 and actinium-225. Journal of the American Chemical Society, 144(41). https://doi.org/10.1021/jacs.2c08438
13. Jakobsson, V., Baum, R. P., Greifenstein, L., Zhang, J., et al. (2022). Alpha-PRRT using actinium-225-labeled somatostatin receptor agonists and antagonists. Frontiers in Medicine, 9. https://doi.org/10.3389/fmed.2022.1034315

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