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. These valves operate through an electrochemical mechanism of geometrically accelerated corrosion induced by passage of electrical current from a wireless, bioresorbable power-harvesting unit. Evaluations in cell cultures demonstrate the efficacy of this technology for the treatment of cancerous tissues by release of the drug doxorubicin. Complete in vivo studies of platforms with multiple, independently controlled release events in live-animal models illustrate capabilities for control of blood glucose levels by timed delivery of insulin.

A brief summary of the HP Ni–P for efficient overall water electrolysis.

Here, we introduce structurally ordered platinum-manganese nanoparticles on a carbon support (Pt3Mn intermetallic/C) as a highly efficient and durable catalyst for oxygen reduction reaction (ORR). Using a facile method, Pt3Mn...

Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in electrochemical environments. Their construction based on density functional theory (DFT) is, however, prohibitively expensive for real-scale systems, such as several nanometer-size nanoparticles (NPs) involving at least thousands of noble metal atoms, and this limitation calls for machine learning (ML)-driven approaches. Herein, with the aim of accelerating the accurate prediction of adsorption energies for a wide range of surface coverages on large-size NPs, we developed a bond-type embedded crystal graph convolutional neural network (BE-CGCNN) model in which four bonding types were treated differently. Owing to the much enhanced accuracy of the bond-type embedding approach compared to the original CGCNN, we demonstrate the construction of reliable Pourbaix diagrams for very large-size NPs involving up to 6,525 atoms (approximately 4.8 nm in diameter), which enables the exploration of electrochemical stability over various NP sizes and shapes. We reveal that ML-based Pourbaix diagrams well reproduce the experimental observations with increasing NP size, such as the increasing O- to OH-covered phase ratio and the decreasing Pt dissolution phase in the diagrams. This work suggests a new method for accelerated Pourbaix diagram construction for real-scale and arbitrarily shaped NPs, which would significantly open up an avenue for electrochemical stability studies.

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. In this study, we developed a deep learning model that can interpret the correlation between surface electronic density of states (DOSs) of materials and their chemisorption property using the attention mechanism that provides which part of DOS is important to predict adsorption energies. The developed model constructs the well-known d-band center theory without any prior knowledge. This work shows that human-interpretable knowledge can be extracted from complex ML models.