What an MCP server is, how the Model Context Protocol connectsAI assistants to patent and scientific literature databases, and how Cyprisuses MCP to deliver R&D intelligence.

AnAI assistant cannot reach live patent data on its own. Every patent searchquestion requires manual work first: pull the patent family from a database,copy the claims into the chat, ask the question, copy the answer elsewhere. TheModel Context Protocol, or MCP, removes that manual step. MCP lets an AIassistant connect directly to external data sources during a conversation.

What MCP is

MCPis an open standard for connecting AI assistants to external data sources andtools. Anthropic introduced MCP in November 2024. A data source, such as apatent database or a scientific literature index, exposes itself through an MCPserver. Any MCP-compatible AI client, including Claude Desktop and ChatGPTDesktop, can connect to that server and use it directly.

BeforeMCP, connecting an AI assistant to a specific database required a customintegration for each assistant and each data source. MCP standardizes thatconnection. One server, built once, works with any MCP-compatible client.

AnMCP server exposes three things to a connected AI client: tools it can call,such as a patent search function; resources it can read, such as patent recordsor paper abstracts; and prompts that template common tasks. A connected AIassistant can call a tool mid-conversation, retrieve current data, and answerbased on that data. It does not have to rely only on what it learned duringtraining.

Why MCP matters for patent search

Patentand scientific literature data changes constantly. A patent landscape shiftswith every new filing. A freedom-to-operate risk can appear the week before aproduct launch. A relevant paper can publish while a literature review isunderway. An AI assistant reasoning only from training data cannot know aboutany of this. It also cannot flag that its answer might be incomplete.

Patentdata is structured and authoritative. Assignee, filing date, legal status, andclaim language are facts recorded in a system of record: USPTO, EPO, WIPO.These facts do not benefit from being paraphrased from a webpage that oncementioned them. MCP lets an AI assistant query the system of record directly.It can cite exactly what it found, inside the same conversation where theanalysis is happening.

How MCP is used in patent search and R&D workflows

Aresearcher using an MCP-connected AI client can describe an invention in plainlanguage. The assistant searches live patent and literature sources directly.No query translation step is required. An IP analyst can ask about a specificassignee's recent filing activity and get an answer sourced from a current APIcall, not from training data. A scientist reviewing a technology area can pullrecent papers, patents, and citation relationships into the same conversationwhere a landscape summary is being drafted.

TheAI assistant stops operating next to the data. It starts operating on the datadirectly. Output quality depends on what data the assistant can reach throughits connected MCP server.

Where open-source MCP servers are useful, and where they stop beingenough

Open-sourceMCP servers connect AI clients to major patent and literature sources: USPTOsearch and litigation APIs, EPO's Open Patent Services for European patentdata, Google Patents, and academic sources including arXiv, PubMed, andSemantic Scholar. For a team that needs one specific data source from onespecific AI client, these are frequently the right choice. Several are activelymaintained.

Theseconnectors answer one question against one source. They do not carry contextacross a decision. A prior art search, a white space analysis, afreedom-to-operate assessment, and a regulatory check are linked stages of thesame decision: whether an R&D program is worth pursuing. The result of onestage should inform how the next is read. A single-source MCP server accuratelyreturns what its database contains. It has no framework for connecting a priorart result to a freedom-to-operate risk rating, because it answers one kind ofquery, not a workflow.

How Cypris uses MCP

Cyprisis an R&D intelligence platform, reachable through MCP, built on a corpusof more than 500 million patents and scientific papers organized through aproprietary R&D ontology. A connected AI client using Cypris through MCPworks with structured domain context, not raw results from a single searchendpoint.

Theagents available through Cypris's MCP server map to the stage-gate decisions anR&D or IP team makes: prior art review, white space identification,freedom-to-operate risk assessment, and regulatory tracking. Cypris Q, theplatform's agentic layer, and enterprise API partnerships with OpenAI,Anthropic, and Google make Cypris accessible inside the AI environmentsenterprise R&D and IP teams already use. Cypris meets enterprise-gradesecurity requirements and serves hundreds of enterprise customers acrosspharmaceuticals, chemicals, advanced materials, energy, and other regulated,security-conscious industries.

Asingle-source, open MCP server is the right tool for retrieval from one patentoffice or literature source inside one AI client. Cypris is built for adifferent need: an AI assistant that carries domain context across prior art,white space, freedom-to-operate, and regulatory decisions in the same workflow.

Setting up an MCP connection

Connectingan MCP-compatible AI client to a data source is a configuration step. Point theclient at the server. Authenticate if the source requires it. Its tools becomeavailable in conversation. Cypris is accessed through enterprise APIpartnerships rather than a self-hosted connection. This is what allows Cypristo meet enterprise security requirements while functioning as an MCP serverinside a team's existing AI client.

FAQ

**What is MCP?** MCP, theModel Context Protocol, is an open standard that lets an AI assistant connectdirectly to external data sources and tools during a conversation. Anthropicintroduced MCP in November 2024. MCP replaces custom, one-off integrations witha single protocol that works across MCP-compatible AI clients and MCP servers.

**Whatis an MCP server?** An MCP server is a connector, built on the Model ContextProtocol, that exposes a data source or tool to an MCP-compatible AI client.For patent search and R&D intelligence, an MCP server can expose patentdatabases, scientific literature indexes, or a broader intelligence platformlike Cypris to an AI assistant such as Claude Desktop or ChatGPT Desktop.

**How is MCP different froma standard API integration?** A standard integration is built once for oneapplication to connect to one data source. MCP standardizes the connection. AnyMCP-compatible AI client can use any MCP server without a new integration foreach pairing.

**Whydoes MCP matter for patent search?** Patent and scientific literature datachanges continuously. It is only useful when current and verifiable against asystem of record. An AI assistant reasoning from training data alone cannotreflect a recent filing. MCP lets the assistant query authoritative sourcesdirectly and answer based on what it retrieves.

**Does connecting to an MCPserver guarantee accurate patent search results?** No. MCP determines whetheran AI assistant can reach a data source in real time. It does not determine howcomplete that source is. A single-source MCP server accurately returns whatthat one source contains. That is not the same as complete patent landscapecoverage.

**Whatis the difference between an MCP connector and an R&D intelligence platformlike Cypris?** A connector answers one query against one data source. Cyprissupports a decision process where prior art, white space, freedom-to-operate,and regulatory findings inform each other. Cypris runs on a corpus of more than500 million patents and scientific papers organized through a proprietaryR&D ontology, delivered through an MCP server and enterprise APIpartnerships with OpenAI, Anthropic, and Google.

**Can Cypris be usedtogether with open-source MCP servers?** Yes. Teams often use open-source,single-source MCP connectors for specific databases alongside Cypris forworkflows that require reasoning across multiple linked patent and R&Ddecisions.

**Do I need to be a developer to use Cypris throughMCP?** No. Once Cypris is connected inside a compatible AI client, using it isa natural-language conversation. Cypris is accessed through enterprise APIpartnerships built to remove setup

Green ammonia has become a priority for industrial decarbonization, and its patent landscape is distinctive because low-carbon ammonia is being pursued through several competing routes, each with its own chemistry and process engineering. Ammonia is one of the highest-volume chemicals made, the foundation of nitrogen fertilizer, and a candidate hydrogen carrier and fuel, and its conventional production, reforming fossil methane for hydrogen and combining it with nitrogen in the high-temperature, high-pressure Haber-Bosch process, is carbon-intensive: the International Energy Agency attributes to ammonia production roughly 1.3 percent of energy-system carbon dioxide emissions and about 2 percent of total final energy consumption, with direct carbon dioxide emissions on the order of 450 million tonnes a year.¹ Decarbonizing it is therefore a climate priority, and the routes divide into distinct regions of patenting: renewable-powered Haber-Bosch, which replaces fossil hydrogen with hydrogen from water electrolysis and feeds a modified synthesis loop;⁴ direct electrochemical nitrogen reduction, which converts nitrogen to ammonia electrochemically under mild conditions;²,⁵ plasma-electrocatalysis; nitrogen-oxide reduction; and solid-oxide electrochemical cells. Cutting across the routes are the catalysts, the electrode and cell designs, and the process control that adapts synthesis to variable renewable power. Because a competitive process depends on several of these, freedom-to-operate and white space analysis must span the routes and the layers together.

The landscape is being pulled forward by decarbonization and by the sheer scale of demand, so even incremental efficiency and emission gains are valuable. The routes sit at very different stages: renewable-powered Haber-Bosch is the most commercially mature and holds the largest share of patent activity, with established engineering firms filing on integrating intermittent hydrogen supply, buffer storage, and dynamic synthesis-loop control, while direct electrochemical, plasma, and solid-oxide routes are earlier, advancing rapidly in catalyst design and device operation but still facing fundamental efficiency and selectivity limits. Theoretical analysis places the maximum energy efficiency of the leading lithium-mediated electrochemical process at roughly 28 percent, and scaling relations among reaction intermediates constrain how selective nitrogen-to-ammonia catalysts can be, which is why catalyst design is the central research problem for these routes.³,⁷ This shows in the record: across the Cypris corpus of more than 500 million patents and scientific papers, the green and electrochemical ammonia set holds on the order of 3,613 families and grew from about 155 in 2020 to roughly 523 in 2024, with the most active assignees led by established ammonia-technology licensors such as Topsoe and Casale alongside energy majors, and China well ahead of the United States and Denmark on geography; 2025 and 2026 counts are partial because of the publication lag.

The strategic question is which route and layer to back, and the white space sits where the chemistry is hardest. In renewable-powered Haber-Bosch, the open ground is in dynamic operation and process integration that let a plant follow variable renewable power. In the electrochemical routes, catalysts that raise ammonia yield and suppress the competing hydrogen-evolution reaction are the central problem, and they are comparatively open and high-value.²,⁵ Plasma-electrocatalysis and solid-oxide cells are earlier, less-crowded routes, and modular, decentralized designs are strategically important where distributed fertilizer and fuel production matter.⁶ Reading the landscape by route, catalyst, and process, and tracking both the patents and the underlying catalysis research, is what separates a crowded region from an open one.

Where the green-ammonia white space is

Nitrogen-reduction catalysts. Catalysts that raise ammonia yield and suppress the competing hydrogen-evolution reaction are the central problem for the electrochemical route and a comparatively open, high-value layer.²,⁵

Dynamic, flexible Haber-Bosch. Process control and loop designs that let a synthesis plant follow variable renewable power are a large, active layer in the most mature route.⁴

Plasma-electrocatalysis and solid-oxide cells. These earlier routes, including intermediate-temperature solid-oxide electrochemical cells, are less crowded and offer differentiated positions.

Nitrogen-oxide-mediated routes. Pathways that route through nitrogen-oxide intermediates are an emerging, distinct area of chemistry.

Modular, decentralized systems. Small-scale, modular ammonia production near renewable resources and demand is a strategically important system layer.⁶

How AI-powered landscape and white space analysis helps

Resolving a landscape that spans several synthesis routes, each with its own catalysts and process, requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by route, catalyst, and process across varied terminology, attribution that normalizes engineering-firm, startup, and academic filers to canonical entities, and continuous monitoring that keeps pace with a decarbonization-driven surge. Because ammonia-synthesis advances appear in scientific and catalysis literature before they are patented, reading both patents and literature gives the earliest signal of where scalable routes are emerging.

Where Cypris fits

Cypris runs patent landscape and white space analysis for multi-route chemical fields such as green ammonia across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by route, renewable Haber-Bosch, electrochemical nitrogen reduction, plasma, nitrogen-oxide, and solid-oxide, and by layer, catalyst, cell and electrode, and process control, and normalizes engineering-firm, startup, and academic filers to canonical entities, so a team can resolve which routes and layers are crowded and which remain open as white space, and can track new entrants as the field scales. Semantic search across patents and scientific literature connects filings to the underlying catalysis research, which is where green-ammonia advances appear first. 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. 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 green ammonia patent landscape? The green ammonia patent landscape is the set of patents covering low-carbon ammonia production. It divides across competing routes, renewable-powered Haber-Bosch, direct electrochemical nitrogen reduction, plasma-electrocatalysis, nitrogen-oxide reduction, and solid-oxide electrochemical cells, each with distinct catalysts and process IP. Each route is a distinct region of patenting.

Why is green ammonia a decarbonization priority? Green ammonia is a decarbonization priority because ammonia is one of the largest-volume chemicals, the backbone of fertilizer, and a candidate fuel and hydrogen carrier, while its conventional production is fossil-fuel-based. The International Energy Agency attributes to it roughly 1.3 percent of energy-system carbon dioxide emissions and about 2 percent of final energy use. Decarbonizing it addresses both food and energy systems.

What routes does the green-ammonia landscape cover? The landscape covers renewable-powered Haber-Bosch, direct electrochemical nitrogen reduction, plasma-electrocatalysis, nitrogen-oxide reduction, and solid-oxide electrochemical cells. Each uses different chemistry and sits at a different maturity, with renewable Haber-Bosch the most commercially advanced. Freedom-to-operate and white space analysis must treat them separately.

Where is the white space in green ammonia? The white space includes nitrogen-reduction catalysts, dynamic and flexible Haber-Bosch operation, plasma-electrocatalysis and solid-oxide cells, nitrogen-oxide-mediated routes, and modular decentralized systems. Renewable Haber-Bosch is comparatively mature and holds the most patents. The most open, high-value opportunities are in electrochemical catalysts and the earlier routes.

Why are nitrogen-reduction catalysts so important? Nitrogen-reduction catalysts are important because the direct electrochemical route's viability depends on raising ammonia yield while suppressing the competing hydrogen-evolution reaction, which otherwise dominates, and because scaling relations among intermediates limit selectivity. Solving this is the central technical problem for that route. The catalyst compositions and cell designs that achieve it are foundational and defensible.

Why does green-ammonia analysis need scientific literature? Green-ammonia analysis needs scientific literature because catalyst and cell advances appear in chemistry 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 green ammonia patent landscape? Software for the green-ammonia landscape should cluster activity by route and process layer, resolve engineering-firm, startup, and academic filers to canonical owners, search patents and scientific literature semantically, and monitor a decarbonization-driven 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 green ammonia patent landscape analysis? Green ammonia patent landscape analysis is used by R&D, innovation, IP, and strategy teams at chemical, fertilizer, energy, and engineering companies, catalysis developers, and their partners, as well as investors and policymakers. It informs which route to back, where to file, and where competitors are concentrated. Cypris serves hundreds of enterprise customers across chemicals, energy, advanced materials, and other regulated industries.

Endnotes

1. International Energy Agency (2021). Ammonia Technology Roadmap. https://www.iea.org/reports/ammonia-technology-roadmap
2. Li, S., et al. (2021). Electrochemical ammonia synthesis: mechanistic understanding and catalyst design. Chem, 7(12). https://doi.org/10.1016/j.chempr.2021.01.009
3. Fu, X., Zhou, Y., Nørskov, J. K., & Chorkendorff, I. (2024). Electrochemical ammonia synthesis: the energy efficiency challenge. ACS Energy Letters, 9(12). https://doi.org/10.1021/acsenergylett.4c02954
4. Gu, Y., et al. (2024). Ambient electrochemical ammonia synthesis: from theoretical guidance to catalyst design. Advanced Science, 11. https://doi.org/10.1002/advs.202308979
5. Sankannavar, A., & Shetty, A. (2024). Exploring nitrogen reduction reaction mechanisms in electrochemical ammonia synthesis: a comprehensive review. Journal of Energy Chemistry, 92. https://doi.org/10.1016/j.jechem.2024.01.024
6. Chebrolu, V. T., et al. (2023). Overview of emerging catalytic materials for electrochemical green ammonia synthesis. Carbon Energy, 5. https://doi.org/10.1002/cey2.361
7. Tsai, C., Vojvodić, A., Montoya, J., & Nørskov, J. K. (2015). The challenge of electrochemical ammonia synthesis: nitrogen scaling relations. ChemSusChem, 8(13). https://doi.org/10.1002/cssc.201500322

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The solid-state battery race is being decided at the electrolyte, and the patent landscape divides along three chemistries: sulfide, oxide, and polymer. A solid-state battery replaces the liquid electrolyte of a conventional lithium-ion cell with a solid one, which can improve safety and enable higher-energy electrode pairings. The central engineering problem is that no single solid electrolyte class simultaneously optimizes the three properties that matter, room-temperature ionic conductivity, stability at the electrode interfaces, and manufacturability, so each class represents a different set of trade-offs and a different region of the patent landscape. Understanding where filing activity concentrates by class, and where it does not, is how R&D and IP teams locate defensible positions in one of the fastest-moving areas of energy patenting.

The scale of that activity is documented in primary data. A joint analysis by the European Patent Office and the International Energy Agency found that international patent families in electricity storage grew from 1,029 in 2000 to more than 7,000 in 2018, at an average of 14 percent per year between 2005 and 2018, roughly four times the economy-wide average.¹ Within that, solid-state lithium-ion filings grew faster still, at around 25 percent per year since 2010, reaching 211 international patent families in 2018, with Japan the dominant country of origin, and solid-state electrolyte activity rose several-fold over the decade.¹ More recent analysis reports that energy storage now accounts for roughly 40 percent of all energy-related patenting, confirming that the field has continued to accelerate.² Because applications publish about eighteen months after filing, the most recent activity is under-represented, so these figures understate the current state.

The three electrolyte classes occupy distinct positions defined by their physics. Sulfide electrolytes reach the highest room-temperature ionic conductivities, on the order of 10 to the minus two siemens per centimeter, comparable to or exceeding liquid electrolytes, but they are chemically and electrochemically unstable at the electrode interfaces and sensitive to moisture, so the dominant patenting and research effort targets interfacial stabilization and dry-processing manufacture.³,⁴ Oxide electrolytes, principally garnet-type structures, offer good stability and a wide electrochemical window with intermediate conductivity, typically in the 10 to the minus four to 10 to the minus three siemens per centimeter range, but they are hard and brittle, which makes achieving low-resistance interfaces and scalable, thin, dense layers the central challenge.⁵ Polymer electrolytes are the most manufacturable, compatible with existing roll-to-roll processing, but historically suffered from low room-temperature conductivity, on the order of 10 to the minus seven siemens per centimeter for early systems, though engineered solid polymer electrolytes have since reached the milli-siemens-per-centimeter range, which is why manufacturability arguments increasingly favor them despite the historical conductivity gap.⁶

What the three classes trade off

Sulfide. Highest ionic conductivity, comparable to liquid electrolytes, but poor interfacial and moisture stability; patenting concentrates on interface engineering and dry manufacturing.³

Oxide. Good stability and a wide electrochemical window with intermediate conductivity, but brittleness and interfacial resistance dominate the technical and patenting effort.⁵

Polymer. Best manufacturability and compatibility with existing processes, historically limited by low room-temperature conductivity that engineered systems are now closing.⁶

Emerging classes. Halide and composite electrolytes are an active newer area that combines properties across classes, and the interfacial-engineering literature increasingly treats all classes together.⁷

How to analyze the electrolyte landscape and find white space

Scope the analysis by electrolyte class and by the property being improved, since sulfide, oxide, and polymer activity concentrate on different problems and should be assessed separately.

Aggregate to the patent-family level and attribute to organizations, so international coverage is not double-counted and activity is correctly assigned by country and assignee.

Map patents against the underlying materials research, because solid-electrolyte advances appear in scientific literature before they are patented, so literature coverage gives the earliest signal.

Identify dense and sparse regions within each class, distinguishing crowded problems, such as sulfide interface stabilization, from open white space, such as specific composite or processing approaches.

Correct for publication lag and monitor continuously, since the most recent activity is under-represented and the field moves quickly.

Where Cypris fits

Cypris runs patent landscape and white space analysis for fast-moving fields such as solid-state batteries across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by electrolyte class and by the property being improved, and normalizes organizations to canonical entities, so a team can resolve which classes and problems are crowded and which remain open as white space. Semantic search across patents and scientific literature connects filings to the underlying materials research, which matters in solid-state batteries because advances appear in the literature before they are patented. Cypris Q, the platform's agentic layer, lets teams run landscape and white space analysis conversationally and chain the class-level scoping, attribution, and gap analysis, and Agentic Monitoring tracks a defined chemistry 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

What are the three main solid-state battery electrolyte classes?

The three main solid-state battery electrolyte classes are sulfide, oxide, and polymer. They trade off room-temperature ionic conductivity, stability at the electrode interfaces, and manufacturability, and no single class optimizes all three. Each occupies a distinct region of the patent landscape, with halide and composite electrolytes an emerging fourth area.

How do sulfide, oxide, and polymer electrolytes compare?

Sulfide electrolytes have the highest ionic conductivity, around 10 to the minus two siemens per centimeter, but poor interfacial and moisture stability. Oxide garnets offer good stability with intermediate conductivity but are brittle. Polymers are the most manufacturable but historically had low conductivity, which engineered systems are now improving.

How fast is solid-state battery patenting growing?

Solid-state battery patenting is growing quickly. Electricity-storage international patent families grew about 14 percent per year from 2005 to 2018, four times the economy-wide average, and solid-state lithium-ion filings grew around 25 percent per year since 2010. Energy storage now accounts for roughly 40 percent of all energy-related patenting.

Why does ionic conductivity differ so much between electrolyte classes?

Ionic conductivity differs between electrolyte classes because it is governed by the material's structure and ion-transport mechanism. Sulfides allow fast ion movement and reach conductivities comparable to liquids, oxides are intermediate, and polymers historically conducted far more slowly at room temperature. Engineering has narrowed the polymer gap substantially.

Which electrolyte class is winning?

No electrolyte class has decisively won, because each optimizes different properties. Sulfides lead on conductivity, oxides on stability, and polymers on manufacturability, and patenting concentrates on each class's specific weakness. The manufacturability advantage of polymers and the conductivity of sulfides are both driving heavy activity, with the outcome still open.

How do you find white space in the solid-state electrolyte landscape?

Finding white space in the solid-state electrolyte landscape means scoping by class and by the property being improved, mapping patents and scientific literature, and identifying the sparse regions within each class. Because advances appear in research first, literature coverage gives early signal. The white space is where a specific composition or processing approach is viable but few patents yet exist.

Why does solid-state battery analysis need scientific literature?

Solid-state battery analysis needs scientific literature because electrolyte and interface advances appear in materials research before they are patented, so the literature gives the earliest signal of a viable approach. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.

Why does publication lag matter in the battery patent landscape?

Publication lag matters because applications publish about eighteen months after filing, so the most recent solid-state activity is under-represented in current data. In a field growing this quickly, the latest figures understate the true state. Longer-window trends and continuous monitoring are more reliable.

Who uses solid-state battery patent landscape analysis?

Solid-state battery patent landscape analysis is used by R&D, innovation, IP, and strategy teams at battery makers, automotive and energy companies, materials developers, and their partners. It informs which electrolyte class to pursue, where to file, and where competitors are concentrated. Cypris serves hundreds of enterprise customers across energy, advanced materials, chemicals, and other regulated industries.

Endnotes

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. Richter, F. H. et al. (2020). Interfacial challenges for all-solid-state batteries based on sulfide solid electrolytes. Journal of Materiomics. https://doi.org/10.1016/j.jmat.2020.09.003
4. 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
5. Wei, Z. et al. (2024). Oxide Solid Electrolytes in Solid-State Batteries. Batteries & Supercaps. https://doi.org/10.1002/batt.202400667
6. Wei, Z., Guo, R., Li, C. & Peng, H. (2025). Why Will Polymers Win the Race for Solid-State Batteries? Advanced Science. https://doi.org/10.1002/advs.202510481
7. Chae, S. et al. (2026). Interfacial Engineering for Layered Oxide Cathodes in All-Solid-State Batteries. Batteries & Supercaps. https://doi.org/10.1002/batt.70366

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