Multimodal Measurement and Diagnosis for Forward-Osmosis Hydrogel Systems: Measurement and Comparator Design

Abstract

The state of a forward-osmosis hydrogel cannot be inferred reliably from water flux alone. Swelling, osmotic pressure, solute leakage, mechanical deformation, feed composition, temperature, fouling, and regeneration history provide partially independent evidence. This review proposes a multimodal AI measurement design that aligns those signals while preserving disagreement as a diagnostic cue. Mechanism-informed representations constrain the relation between hydrogel state and transport; fusion models estimate latent condition; uncertainty-aware anomaly detection separates material degradation from sensor or protocol error. The synthesis compares single-modality, early-fusion, late-fusion, and physics-informed baselines under matched data access and explicitly tests missing or conflicting modalities. Evaluation links calibration and error categories to decisions such as additional sampling, regeneration, formulation change, or rejection of a batch. Source, specimen, preprocessing, and model provenance are retained for every result. The article does not report a new desalination experiment. Its contribution is a comparator-ready AI framework showing how multimodal evidence can support diagnosis without allowing dominant flux measurements to hide consequential changes in leakage or mechanical integrity.