Lifecycle Reliability and Translation for Carbon-Nanotube Desalination Membranes: Failure Modes across the Evidence Chain

Abstract

The operational value of a carbon-nanotube desalination membrane depends on more than initial permeability and rejection: defects, fouling, chemical exposure, support-layer changes, and cleaning cycles reshape performance over time. This review traces these failure modes across material characterization, module fabrication, sensing, AI prediction, maintenance, and retirement. It examines how degradation modelling, multimodal anomaly detection, and remaining-useful-life estimation can combine membrane history with pressure, flux, and water-quality signals. Particular emphasis is placed on censored failures, batch effects, label uncertainty, and feedback bias introduced when maintenance actions alter the future data used for learning. A lifecycle digital thread is proposed to connect nanotube architecture and manufacturing records to model versions, interventions, and downstream outcomes. Predictive outputs are framed as decision support with calibrated uncertainty and escalation rules, not automatic evidence of durability. No new lifetime experiment or effect estimate is claimed. The synthesis offers a failure-aware AI framework for translating nanoscale membrane research into inspectable, updateable, and accountable reliability decisions.