Data Provenance and Reproducibility for Tapered Graphene Desalination: Validity Domains and Revalidation Triggers

Abstract

Reproducible AI for tapered graphene desalination requires a chain from simulation parameters and material geometry to measurements, preprocessing, model versions, and final decisions. This review specifies that chain and the conditions under which it must be rebuilt. Provenance records include force fields or transport assumptions, channel dimensions, fabrication batches, feed chemistry, sensor calibration, training splits, hyperparameters, and uncertainty estimates. The synthesis shows how missing lineage can make a repeated computation appear to validate a result that actually depends on a different physical or statistical domain. Validity is monitored with mechanism-based residuals, data-drift tests, and calibration checks; changes in geometry, chemistry, equipment, preprocessing, or intended decision trigger graded revalidation. Distributed analysis is considered where data remain at separate facilities, with auditable model updates rather than detached aggregate results. No new experiment or simulation is introduced. The article contributes a provenance-centered AI assurance framework that makes graphene desalination evidence inspectable, supports controlled rollback, and ties continued use to explicit physical and computational validity domains.