Operational Resilience and Revalidation for Lithium-Sulfur Separator Engineering: Failure Modes across the Evidence Chain

Abstract

Lithium-sulfur separator performance can deteriorate through polysulfide transport, pore blockage, material delamination, batch variation, and changes in cycling protocol. This review treats those mechanisms as potential breaks in an AI evidence chain rather than as isolated material defects. It examines how electrochemical signals, impedance, temperature, imaging, and manufacturing metadata can support multimodal health estimation and drift detection. A resilience architecture links each prediction to separator composition, cell configuration, preprocessing, and model version, then assigns revalidation triggers for chemistry changes, equipment updates, novel degradation signatures, or loss of calibration. The analysis distinguishes a recoverable sensor or software fault from physical deterioration that requires restricted operation or replacement. Graph-based representations and uncertainty-aware models are discussed as decision aids, with human escalation whenever the model leaves its validated regime. The article synthesizes cited evidence and does not report a new cell test. Its contribution is a failure-oriented AI assurance framework that makes separator monitoring traceable, supports rollback and controlled recovery, and prevents nominal model availability from being mistaken for operational reliability.