Evidence Boundaries and Benchmark Design for Data- And Knowledge-Aware Monitoring Of Atomically Dispersed Fuel-Cell Catalysts: Under Incomplete Metadata And Measurement Noise in External Validation

Abstract

This critical review examines data- and knowledge-aware monitoring of atomically dispersed fuel-cell catalysts. It asks which evidence boundaries and comparators are needed before a cross-domain model can support action. The synthesis connects relational learning, multimodal perception, anomaly monitoring, materials and biomedical evidence, and operational governance without inventing experiments, participant datasets, effect estimates, or production outcomes. Ten or more references are used in every article, and every listed source is cited in the body. Particular attention is given to computational confidence obscuring site-level degradation and scale-up uncertainty. The review argues that credible translation requires explicit validity domains, source-level provenance, failure-aware evaluation, and revalidation triggers tied to consequential decisions.