Data Provenance and Reproducibility for Carbon-Nanotube Desalination Membranes: Lifecycle Evidence and Accountable Decisions

Abstract

Reproducing a carbon-nanotube membrane result requires access not only to code, but also to nanotube source, geometry, functionalization, fabrication history, feed chemistry, measurement calibration, and the exact analytical model. This review treats provenance as the backbone of AI-assisted lifecycle evidence. It proposes a digital thread that links material batches and experimental records to preprocessing steps, learned representations, model checkpoints, uncertainty estimates, and later operational decisions. The synthesis distinguishes computational repeatability from scientific reproducibility and examines how incomplete metadata can create spurious confidence in cross-study learning. Federated or distributed analytics are considered where proprietary facilities cannot pool raw data, with provenance and access controls retained at the source. Model cards, dataset lineage, immutable decision records, and change-triggered revalidation are mapped to concrete membrane failure risks. No new dataset or desalination result is presented. The article's contribution is a reproducibility framework in which AI predictions can be audited back to physical specimens and measurement conditions, enabling accountable comparison, transfer, maintenance, and retirement decisions across the membrane lifecycle.