Evidencing microdroplet H2O2 redox chemistry: from detection to mechanistic attribution

Aqueous microdroplet interfaces are increasingly recognized as chemically active environments that can generate hydrogen peroxide (H 2 O 2 ), produce transient reactive oxygen species (ROS), and reshape redox kinetics in ways that matter for atmospheric multiphase chemistry, interfacial oxidation, and selective oxidant synthesis. Accumulated evidence supports the view that aqueous microdroplets can indeed generate H 2 O 2 , and that this reactivity is an intrinsic feature of pristine air–water interfaces rather than a purely artifactual consequence of specific experimental platforms. Yet this field remains mechanistically unresolved, because similar H 2 O 2 or ROS observations are still interpreted through platform-specific models. Here we present an Analysis that links droplet-generation methods to operative boundary conditions, separates detection from net H 2 O 2 production and interfacial attribution through an evidence ladder, and distils a controlled-variable checklist for experiments designed to resolve apparently conflicting results across studies. This paper makes three points explicit: (i) spontaneous H 2 O 2 formation is an intrinsic capability of pristine air–water microdroplet interfaces, interfacial electrostatics are chemically consequential, and the dominant pathway expressed in a given experiment is strongly shaped by boundary conditions; (ii) the assignments of transient radical intermediates, apparent oxygen dependence, and some mechanistic conclusions drawn from instrument-sensitive readouts remain method-dependent; (iii) the measurements most urgently needed for progress are contact-minimized baseline experiments, explicit control and reporting of O 2 exposure, humidity, charge-state evolution, residence time, and orthogonal validation across multiple analytical techniques. In this way, this Analysis provides an experimentally grounded framework for making microdroplet redox chemistry more reproducible, comparable, and mechanistically interpretable across platforms.