Efficient and sustainable water electrolysis achieved by excess electron reservoir enabling charge replenishment to catalysts.

Suppressing the oxidation of active-Ir(III) in IrO catalysts is highly desirable to realize an efficient and durable oxygen evolution reaction in water electrolysis. Although charge replenishment from supports can be effective in preventing the oxidation of IrO catalysts, most supports have inherently limited charge transfer capability. Here, we demonstrate that an excess electron reservoir, which is a charged oxygen species, incorporated in antimony-doped tin oxide supports can effectively control the Ir oxidation states by boosting the charge donations to IrO catalysts.

Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property.

The use of machine learning (ML) is exploding in materials science as a result of its high predictive performance of material properties. Tremendous trainable parameters are required to build an outperforming predictive model, which makes it impossible to retrace how the model predicts well. However, it is necessary to develop a ML model that can extract human-understandable knowledge while maintaining performance for a universal application to materials science.

Wirelessly controlled, bioresorbable drug delivery device with active valves that exploit electrochemically triggered crevice corrosion.

Implantable drug release platforms that offer wirelessly programmable control over pharmacokinetics have potential in advanced treatment protocols for hormone imbalances, malignant cancers, diabetic conditions, and others. We present a system with this type of functionality in which the constituent materials undergo complete bioresorption to eliminate device load from the patient after completing the final stage of the release process. Here, bioresorbable polyanhydride reservoirs store drugs in defined reservoirs without leakage until wirelessly triggered valve structures open to allow release.