GCMembrane-LLM: An Evidence-Grounded Domain-Specific Large Language Model for Structure-Performance Reasoning in Graphene and Carbon Nanotube Separation Membranes.

Youyang Liu, Shuhan Liu, Yao He, Ziyi Yan, Yilu Zhao, Xinyu Zhang, Zhen Li, Ning Wei

Abstract read

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Article in Membranes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Youyang LiuJiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangsu Province Engineering Research Center of Micro-Nano Additive and Subtractive Manufacturing, Institute of Advanced Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.

Shuhan LiuJiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangsu Province Engineering Research Center of Micro-Nano Additive and Subtractive Manufacturing, Institute of Advanced Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.

Yao HeJiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangsu Province Engineering Research Center of Micro-Nano Additive and Subtractive Manufacturing, Institute of Advanced Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.

Ziyi YanJiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangsu Province Engineering Research Center of Micro-Nano Additive and Subtractive Manufacturing, Institute of Advanced Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.

Yilu ZhaoJiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangsu Province Engineering Research Center of Micro-Nano Additive and Subtractive Manufacturing, Institute of Advanced Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.

Xinyu ZhangJiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangsu Province Engineering Research Center of Micro-Nano Additive and Subtractive Manufacturing, Institute of Advanced Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.

Zhen LiJiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangsu Province Engineering Research Center of Micro-Nano Additive and Subtractive Manufacturing, Institute of Advanced Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.

Ning WeiJiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangsu Province Engineering Research Center of Micro-Nano Additive and Subtractive Manufacturing, Institute of Advanced Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.

Funding

Jiangnan University This work was supported by Key Laboratory of Special Protective Textiles of Ministry of Education (Jiangnan University) (Grant No. TZFH-24-004).

6 · The paper itself

Abstract

Graphene and carbon nanotube (CNT) membranes are promising for filtration, desalination, and water treatment, yet their performance requires the joint interpretation of their architecture, nanoconfined transport, selectivity, fouling, swelling, defects, stability, and operating conditions. Here, GCMembrane-LLM was developed as an evidence-grounded domain-specific large language model. A curated 582-paper corpus generated 12,208 cleaned membrane-specific question-answer pairs for Low-Rank Adaptation (LoRA)-based supervised fine-tuning of Llama-3.1-8B-Instruct, and retrieval-augmented generation provided article-title and page-level traceability. GCMembraneBench included 100 application-oriented questions on graphene oxide (GO) membranes, CNT membranes, GO/CNT hybrids, and cross-material reasoning. Under direct answering without retrieval context, the anonymized and shuffled automatic evaluation showed that GCMembrane-LLM achieved a mean weighted score of 4.237/5.0, exceeding Llama-3.1-8B-Instruct and Doubao-1.5-lite. A stratified 30-question blinded manual assessment showed the same ranking. The application cases further yielded membrane science conclusions: CNT-assisted GO/CNT transport should be evaluated with dispersion, interfacial compatibility, defects, and stability; GO desalination depends on swelling control, interlayer spacing, and defect suppression; and CNT high flux requires joint examination of pore diameter, entrance chemistry, hydration barriers, ion rejection, and operating conditions. GCMembrane-LLM supports source-traceable evidence organization and preliminary hypothesis formulation before experimental validation.

Indexed as

carbon nanotube membranesdesalinationdomain-specific large language modelgraphene/CNT hybrid membranesgraphene oxide membranesmembrane foulingnanofiltrationstructure–performance relationshipswelling stabilitywater treatment

Identifiers

PMID42346970

DOI10.3390/membranes16060214

PMCPMC13304321

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.