Interfacial Regulation of Iron-Based Materials for Targeted Arsenic Speciation Control, Selective Recognition and Stability in Complex Aquatic Systems

Institute of NBC Defense, P.O. Box 1048, Beijing 102205, China

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Abstract

Iron-based materials have been widely recognized as one of the most promising platforms for arsenic remediation owing to their intrinsic affinity for arsenic species, low cost, and compatibility with existing water treatment infrastructures. Nevertheless, their translation from laboratory success to field-scale deployment remains severely limited in complex aquatic environments. This discrepancy arises primarily from three persistent challenges: the inefficient removal of highly toxic and mobile As(III), strong interference from coexisting anions such as phosphate and silicate, and insufficient long-term structural and chemical stability that raises concerns regarding secondary contamination. Accumulating evidence indicates that these limitations do not originate from insufficient adsorption capacity, but rather from inadequate regulation of interfacial microprocesses governing arsenic speciation, selectivity, and environmental fate. In this review, we argue that overcoming the laboratory field performance gap requires a paradigm shift from capacity-oriented material screening toward interface-centered rational design. Recent advances are critically examined from three interrelated perspectives: (i) construction of redox-active heterogeneous interfaces to steer arsenic speciation via in situ As(III) oxidation; (ii) electronic structure and microenvironment engineering to enable arsenic-selective recognition under intense ionic competition; and (iii) stabilization and fate-control strategies to ensure long-term performance and environmental safety. Meanwhile, the influences of natural organic matter, pH fluctuation, ionic strength, and hydrodynamic conditions on interfacial behaviors are systematically analyzed to bridge laboratory research and practical applications. Finally, emerging opportunities enabled by data-driven material discovery and intelligent process coupling are discussed, outlining a roadmap toward efficient, selective, and practically deployable arsenic remediation technologies.