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Abstract Aqueous zinc batteries are attracting interest because of their potential for cost-effective and safe electricity storage. However, metallic zinc exhibits only moderate reversibility in aqueous electrolytes. To circumvent this issue, we study aqueous Zn batteries able to form nanometric interphases at the Zn metal/liquid electrolyte interface, composed of an ion-oligomer complex. In Zn||Zn symmetric cell studies, we report highly reversible cycling at high current densities and capacities (e.g., 160 mA cm −2 ; 2.6 mAh cm −2 ). By means of quartz-crystal microbalance, nuclear magnetic resonance, and voltammetry measurements we show that the interphase film exists in a dynamic equilibrium with oligomers dissolved in the electrolyte. The interphase strategy is applied to aqueous Zn||I 2 and Zn||MnO 2 cells that are charged/discharged for 12,000 cycles and 1000 cycles, respectively, at a current density of 160 mA cm −2 and capacity of approximately 0.85 mAh cm −2 . Finally, we demonstrate that Zn||I 2 -carbon pouch cells (9 cm 2 area) cycle stably and deliver a specific energy of 151 Wh/kg (based on the total mass of active materials in the electrode) at a charge current density of 56 mA cm −2 . 

The past decade witnessed rapid development in the measurement and monitoring technologies for food science. Among these technologies, spectroscopy has been widely used for the analysis of food quality, safety, and nutritional properties. Due to the complexity of food systems and the lack of comprehensive predictive models, rapid and simple measurements to predict complex properties in food systems are largely missing. Machine Learning (ML) has shown great potential to improve the classification and prediction of these properties. However, the barriers to collecting large datasets for ML applications still persists. In this paper, we explore different approaches of data annotation and model training to improve data efficiency for ML applications. Specifically, we leverage Active Learning (AL) and Semi-Supervised Learning (SSL) and investigate four approaches: baseline passive learning, AL, SSL, and a hybrid of AL and SSL. To evaluate these approaches, we collect two spectroscopy datasets: predicting plasma dosage and detecting foodborne pathogen. Our experimental results show that, compared to the de facto passive learning approach, advanced approaches (AL, SSL, and the hybrid) can greatly reduce the number of labeled samples, with some cases decreasing the number of labeled samples by more than half. 

Principles of interfacial chemical kinetics provide powerful guidelines for designing battery anodes. 

We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space. We propose a Gaussian process-based algorithm and establish its order-optimal regret performance (up to a poly-logarithmic factor). This is the first GP-based algorithm with an order-optimal regret guarantee. The proposed algorithm is rooted in the methodology of domain shrinking realized through a sequence of tree-based region pruning and refining to concentrate queries in increasingly smaller high-performing regions of the function domain. The search for high-performing regions is localized and guided by an iterative estimation of the optimal function value to ensure both learning efficiency and computational efficiency.