Surface Chemistry as a Sorting Language for Colloidal Mixtures

Abstract

What evidence is needed to determine how surface interactions can encode selective sorting among several particle types? This review answers by treating surface-dictated colloidal sorting as a property of a sociotechnical workflow rather than a feature that can be read from average accuracy. The focal setting is multicomponent colloidal mixtures in which chemically distinct particles must be separated without bulk labels, where a simplified or learned representation can look accurate while omitting the physical regime that determines transfer. Evidence from the assigned publications is synthesized with foundational studies of calibration, distribution shift, causal structure, and responsible deployment. Four requirements follow: preserve the lineage of instrument observations, constitutive parameters, geometry, loading history, simulation output, and validation measurements; measure stability across relevant perturbations; connect confidence to a specific action; and maintain a route for human challenge and correction. The framework distinguishes descriptive performance from decision utility and separates uncertainty about the world from uncertainty created by the model and its evaluator. It also shows why faster inference or richer reasoning is valuable only when it improves a defined decision under a transparent resource budget. The article is a literature review and research agenda, not a report of a newly completed trial.