Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
Research on carbon-nanotube desalination spans molecular transport, membrane fabrication, fouling, module operation, and water-quality assessment, making it difficult to preserve the conditions attached to each claim. This review proposes an AI-enabled but source-grounded knowledge architecture. Scientific language models identify candidate relations among nanotube diameter, alignment, functionalization, pressure, permeability, rejection, and degradation; a provenance-aware knowledge graph then links every relation to the supporting passage, specimen, method, and operating domain. Graph reasoning is used to surface contradictions and missing links between mechanism evidence and deployment decisions, while confidence labels keep extracted statements separate from causal interpretation. The synthesis discusses retrieval-based verification, human adjudication, and version control when publications or metadata change. It also shows how the graph can expose whether two reported improvements share a valid comparator rather than merely similar terminology. The article introduces no new membrane data and does not treat model-generated relations as discoveries. Its contribution is a traceable AI workflow that turns a fragmented literature into reviewable evidence for design, validation, and operational decision making.
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