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Intermetallic compounds formed from non-precious transition metals are promising cost-effective and robust catalysts for electrochemical hydrogen production. However, the development of monolithic nanoporous intermetallics, with ample active sites and sufficient electrocatalytic activity, remains a challenge. Here we report the fabrication of nanoporous Co₇Mo₆and Fe₇Mo₆intermetallic compounds via liquid metal dealloying. Along with the development of three-dimensional bicontinuous open porosity, high-temperature dealloying overcomes the kinetic energy barrier, enabling the direct formation of chemically ordered intermetallic phases. Unprecedented small characteristic lengths are observed for the nanoporous intermetallic compounds, resulting from an intermetallic effect whereby the chemical ordering during nanopore formation lowers surface diffusivity and significantly suppresses the thermal coarsening of dealloyed nanostructure. The resulting ultrafine nanoporous Co₇Mo₆exhibits high catalytic activity and durability in electrochemical hydrogen evolution reactions. This study sheds light on the previously unexplored intermetallic effect in dealloying and facilitates the development of advanced intermetallic catalysts for energy applications.

 

Abstract Glass transition is one of the unresolved critical issues in solid-state physics and materials science, during which a viscous liquid is frozen into a solid or structurally arrested state. On account of the uniform arrested mechanism, the calorimetric glass transition temperature ( T g ) always follows the same trend as the dynamical glass transition (or α -relaxation) temperature ( T α ) determined by dynamic mechanical analysis (DMA). Here, we explored the correlations between the calorimetric and dynamical glass transitions of three prototypical high-entropy metallic glasses (HEMGs) systems. We found that the HEMGs present a depressed dynamical glass transition phenomenon, i.e ., HEMGs with moderate calorimetric T g represent the highest T α and the maximum activation energy of α -relaxation. These decoupled glass transitions from thermal and mechanical measurements reveal the effect of high configurational entropy on the structure and dynamics of supercooled liquids and metallic glasses, which are associated with sluggish diffusion and decreased dynamic and spatial heterogeneities from high mixing entropy. The results have important implications in understanding the entropy effect on the structure and properties of metallic glasses for designing new materials with plenteous physical and mechanical performances. 

2D materials have exceptional physical and chemical characteristics, which makes them attractive for wearable technology. These characteristics include high carrier mobility, outstanding mechanical performance, abundant chemistry, and excellent electrostatic tunability. However, due to the high electron doping effect of interfacial charge impurities and intrinsic defects, most reported 2D materials are n‐type. Complementary electronic devices and high‐performance wearable sensors necessitate the development ofp‐type 2D semiconductors, which have superior electrocatalytic performance in oxidative processes compared to their n‐type counterparts. This review paper thoroughly accounts for recent advancements in 2Dp‐type semiconductor‐based wearable sensors, covering basic understandings, synthesis and fabrication, functional devices, and sensor performance insights. Finally, challenges and future opportunities for 2Dp‐type semiconductor‐based wearable sensors are discussed.

 

Mitochondria are an essential organelle in most eukaryotes. They not only play an important role in energy metabolism but also take part in many critical cytopathological processes. Abnormal mitochondria can trigger a series of human diseases, such as Parkinson's disease, multifactor disorder and Type-II diabetes. Protein submitochondrial localization enables the understanding of protein function in studying disease pathogenesis and drug design.

We proposed a new method, SubMito-XGBoost, for protein submitochondrial localization prediction. Three steps are included: (i) the g-gap dipeptide composition (g-gap DC), pseudo-amino acid composition (PseAAC), auto-correlation function (ACF) and Bi-gram position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features, (ii) Synthetic Minority Oversampling Technique (SMOTE) is used to balance samples, and the ReliefF algorithm is applied for feature selection and (iii) the obtained feature vectors are fed into XGBoost to predict protein submitochondrial locations. SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods. The prediction accuracies of the SubMito-XGBoost method on the two training datasets M317 and M983 were 97.7% and 98.9%, which are 2.8–12.5% and 3.8–9.9% higher than other methods, respectively. The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than the existing studies. The proposed method also achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets. SubMito-XGBoost also plays an important role in new drug design for the treatment of related diseases.

The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/SubMito-XGBoost/.

Supplementary data are available at Bioinformatics online.