Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
This critical review examines data provenance and reproducibility in thermowettability in graphene channels, with links to intelligent manufacturing and evidence governance. The organizing question is what provenance is necessary to reproduce a result across data, material batches, models, and operating environments. Ten or more scholarly and user-supplied records are synthesized through a mechanism-to-decision framework spanning system boundaries, measurement, representation, evaluation, translation, and governance. No experiment, participant dataset, effect estimate, or production result is invented. Particular attention is given to a reproducible computation being mistaken for a reproducible scientific conclusion. The review argues that credible translation requires source-level traceability, explicit validity domains, failure-aware evaluation, and revalidation triggers. Google Scholar-supplied records are retained in structured APA form, and no missing DOI or pagination field is completed by conjecture.
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