Uncertainty-Aware Decision Support for Forward-Osmosis Hydrogel Systems: A Mechanism-to-Decision Synthesis

Abstract

Selecting and operating a hydrogel draw agent involves uncertain trade-offs among osmotic driving force, water flux, solute leakage, mechanical stability, regeneration, and fouling. This review constructs a mechanism-to-decision pathway for AI-assisted support under that uncertainty. Physics-informed probabilistic models relate formulation and operating conditions to transport behavior, while Bayesian updating incorporates new swelling, flux, leakage, and regeneration measurements without erasing prior evidence. The synthesis separates uncertainty due to variable samples from uncertainty due to sparse knowledge and asks which additional measurement would most change a formulation, qualification, or operating decision. Predictions outside the supported chemistry or protocol domain are flagged for abstention rather than forced ranking. A provenance layer links each recommendation to source studies, hydrogel batches, preprocessing, assumptions, and model versions. No new material experiment or effect estimate is introduced. The article contributes an uncertainty-aware decision architecture in which AI prioritizes evidence collection, exposes trade-offs, and communicates when a hydrogel conclusion is robust enough for action and when it remains a bounded hypothesis.