Operational Resilience and Revalidation for Lithium-Sulfur Separator Engineering: Traceable Translation under Uncertainty

Abstract

Transferring a lithium-sulfur separator model from controlled cells to variable production and use environments creates uncertainty that ordinary validation summaries rarely preserve. This review proposes a traceable route from material evidence to resilient AI-assisted operation. The framework records separator composition, nanopore structure, electrolyte, cell geometry, cycling protocol, sensors, and model configuration as a linked validation context. Domain-shift and change-point detectors monitor whether incoming data remain comparable to that context; calibrated uncertainty and mechanism-based checks determine whether the response should be continued use, restricted operation, new testing, or full revalidation. Particular attention is given to apparently benign software, preprocessing, or manufacturing changes that silently alter the model's meaning. Versioned datasets, model registries, decision logs, and rollback procedures make recovery evidence inspectable. This critical review offers no new electrochemical result. Its contribution is an AI governance pattern for lithium-sulfur separator engineering in which translation remains conditional, uncertainty reaches the decision maker, and resilience is demonstrated through controlled detection, fallback, and re-qualification rather than uninterrupted automation.