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Solar-driven H2O2 production is a promising approach for addressing both the energy and environmental crises. However, H2O2 photosynthesis is still restricted by the insufficient electron-hole separation efficiency (ηsep) and sluggish...

Solar-driven CO 2 reduction reaction (CO 2 RR) is largely constrained by the sluggish mass transfer and fast combination of photogenerated charge carriers. Herein, we find that the photocatalytic CO 2 RR efficiency at the abundant gas-liquid interface provided by microdroplets is two orders of magnitude higher than that of the corresponding bulk phase reaction. Even in the absence of sacrificial agents, the production rates of HCOOH over WO 3 • 0.33H 2 O mediated by microdroplets reaches 2536 μmol h À 1 g À 1 (vs. 13 μmol h À 1 g À 1 in bulk phase), which is significantly superior to the previously reported photocatalytic CO 2 RR in bulk phase reaction condition. Beyond the efficient delivery of CO 2 to photocatalyst surfaces within microdroplets, we reveal that the strong electric field at the gas-liquid interface of microdroplets essentially promotes the separation of photogenerated electron-hole pairs. This study provides a deep understanding of ultrafast reaction kinetics promoted by the gas-liquid interface of microdroplets and a novel way of addressing the low efficiency of photocatalytic CO 2 reduction to fuel.

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The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing lowquality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.