Size‑, Shape‑, and Composition-Dependent Model for Metal Nanoparticle Stability Prediction

Although tremendous applications for metal nanoparticles have been found in modern technologies, the understanding of their stability as related to morphology (size and shape) and chemical ordering (e.g., in bimetallics) remains limited. First-principles methods such as density functional theory (DFT) are capable of capturing accurate nanoalloy energetics; however, they are limited to very small nanoparticle sizes (<2 nm in diameter) due to their computational cost. Herein, we propose a bond-centric (BC) model able to capture cohesive energy trends over a range of monometallic and bimetallic nanoparticles and mixing behavior (excess energy) of nanoalloys, in great agreement with DFT calculations. We apply the BC model to screen the energetics of a recently reported 23 196-atom FePt nanoalloys (Yang et al. Nature 2017, 542, 75−79), offering insights into both segregation and bulk-chemical ordering behavior. Because the BC model utilizes tabulated data (diatomic bond energies and bulk cohesive energies) and structural information on nanoparticles (coordination numbers), it can be applied to calculate the energetics of any nanoparticle morphology and chemical composition, thus significantly accelerating nanoalloy design

TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion

Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use

Metal–Metal Oxide Catalytic Interface Formation and Structural Evolution: A Discovery of Strong Metal–Support Bonding, Ordered Intermetallics, and Single Atoms

In-depth investigation of metal–metal oxide interactions and their corresponding evolution is of paramount importance to heterogeneous catalysis as it allows the understanding and maneuvering of the structure of catalytic motifs. Herein, using a series of core/shell metal/iron oxide (M/FeOx, M = Pd, Pt, Au) nanoparticles and through a combination of in situ and ex situ electron and X-ray investigations, we revealed anomalous and dissimilar M–FeOx interactions among different systems under reducing conditions. Pd interacts strongly with FeOx after high-temperature reductive treatment, featured by the formation of Pd single atoms in the FeOx matrix and increased Pd–Fe bonding, while Pt transforms into ordered PtFe intermetallics and Pt single atoms immediately upon the coating of FeOx. In contrast, Au does not manifest strong bonding with FeOx. As a proof of concept of tailoring metal–metal oxide interactions for catalysis, optimized Pd/FeOx demonstrates 100% conversion and 86.5% selectivity at 60 °C for acetylene semihydrogenation

Flexible Capacitive Pressure Sensor with High Sensitivity and Wide Range Based on a Cheetah Leg Structure via 3D Printing

Flexible pressure sensors can be used in human–computer interaction and wearable electronic devices, but one main challenge is to fabricate capacitive sensors with a wide pressure range and high sensitivity. Here, we designed a capacitive pressure sensor based on a bionic cheetah leg microstructure, validated the benefits of the bionic microstructure design, and optimized the structural feature parameters using 3D printing technology. The pressure sensor inspired by the cheetah leg shape has a high sensitivity (0.75 kPa–1), a wide linear sensing range (0–280 kPa), a fast response time of roughly 80 ms, and outstanding durability (24,000 cycles). Furthermore, the sensor can recognize a finger-operated mouse, monitor human motion, and transmit Morse code information. This work demonstrates that bionic capacitive pressure sensors hold considerable promise for use in wearable devices

Flexible Capacitive Pressure Sensor with High Sensitivity and Wide Range Based on a Cheetah Leg Structure via 3D Printing

Flexible pressure sensors can be used in human–computer interaction and wearable electronic devices, but one main challenge is to fabricate capacitive sensors with a wide pressure range and high sensitivity. Here, we designed a capacitive pressure sensor based on a bionic cheetah leg microstructure, validated the benefits of the bionic microstructure design, and optimized the structural feature parameters using 3D printing technology. The pressure sensor inspired by the cheetah leg shape has a high sensitivity (0.75 kPa–1), a wide linear sensing range (0–280 kPa), a fast response time of roughly 80 ms, and outstanding durability (24,000 cycles). Furthermore, the sensor can recognize a finger-operated mouse, monitor human motion, and transmit Morse code information. This work demonstrates that bionic capacitive pressure sensors hold considerable promise for use in wearable devices

Flexible Capacitive Pressure Sensor with High Sensitivity and Wide Range Based on a Cheetah Leg Structure via 3D Printing

Flexible pressure sensors can be used in human–computer interaction and wearable electronic devices, but one main challenge is to fabricate capacitive sensors with a wide pressure range and high sensitivity. Here, we designed a capacitive pressure sensor based on a bionic cheetah leg microstructure, validated the benefits of the bionic microstructure design, and optimized the structural feature parameters using 3D printing technology. The pressure sensor inspired by the cheetah leg shape has a high sensitivity (0.75 kPa–1), a wide linear sensing range (0–280 kPa), a fast response time of roughly 80 ms, and outstanding durability (24,000 cycles). Furthermore, the sensor can recognize a finger-operated mouse, monitor human motion, and transmit Morse code information. This work demonstrates that bionic capacitive pressure sensors hold considerable promise for use in wearable devices

Flexible Capacitive Pressure Sensor with High Sensitivity and Wide Range Based on a Cheetah Leg Structure via 3D Printing

Flexible pressure sensors can be used in human–computer interaction and wearable electronic devices, but one main challenge is to fabricate capacitive sensors with a wide pressure range and high sensitivity. Here, we designed a capacitive pressure sensor based on a bionic cheetah leg microstructure, validated the benefits of the bionic microstructure design, and optimized the structural feature parameters using 3D printing technology. The pressure sensor inspired by the cheetah leg shape has a high sensitivity (0.75 kPa–1), a wide linear sensing range (0–280 kPa), a fast response time of roughly 80 ms, and outstanding durability (24,000 cycles). Furthermore, the sensor can recognize a finger-operated mouse, monitor human motion, and transmit Morse code information. This work demonstrates that bionic capacitive pressure sensors hold considerable promise for use in wearable devices