Efficient Polytelluride Anchoring for Ultralong-Life Potassium Storage: Combined Physical Barrier and Chemisorption in Nanogrid-in-Nanofiber

Metal tellurides(MTes) are highly attractive as promising anodes for high-performance potassium-ion batteries. The capacity attenuation of most reported MTe anodes is attributed to their poor electrical conductivity and large volume variation. The evolution mechanisms, dissolution properties, and corresponding manipulation strategies of intermediates(K-polytellurides, K-pTe_(x)) are rarely mentioned. Herein,we propose a novel structural engineering strategy to confine ultrafine CoTe_(2) nanodots in hierarchical nanogrid-in-nanofiber carbon substrates(CoTe_(2)@NC@NSPCNFs) for smooth immobilization of K-pTe_(x) and highly reversible conversion of CoTe_(2) by manipulating the intense electrochemical reaction process. Various in situ/ex situ techniques and density functional theory calculations have been performed to clarify the formation, transformation, and dissolution of K-pTe_(x)(K_(5)Te_(3) and K_(2)Te), as well as verifying the robust physical barrier and the strong chemisorption of K_(5)Te_(3) and K_(2)Te on S, N co-doped dual-type carbon substrates. Additionally, the hierarchical nanogrid-in-nanofiber nanostructure increases the chemical anchoring sites for K-pTe_(x), provides sufficient volume buffer space, and constructs highly interconnected conductive microcircuits, further propelling the battery reaction to new heights(3500 cycles at 2.0 A g^(-1)). Furthermore, the full cells further demonstrate the potential for practical applications. This work provides new insights into manipulating K-pTe_(x) in the design of ultralong-cycling MTe anodes for advanced PIBs.

Evaluation of fiber degree for fish muscle based on the edge feature attention net

The fiber mouthfeel of fish muscle is a highly sought-after goal for surimi gel products.The primary aim of research and development has been to quickly and accurately evaluate fiber degree for fish muscle.Therefore,based on the ResNet model,edge feature attentional mechanism was introduced to obtain the edge feature attention net (EFANet) to evaluate fiber degree for fish muscle.The EFANet was trained and tested on a dataset,which was made by collecting microscopic pictures of fish samples with different degrees of breakage.Compared with the three classic convolutional neural network (CNN) models,the EFANet emphasizes the learning of fiber texture information for fish muscle,reduces the effect of image color change,and significantly improves the detection accuracy.The average accuracy and specificity of the EFANet-50 on the testing dataset were 96.22% and 97.92%,respectively,which proved that it can effectively predict the fiber degree of fish muscle.

Molecular simulation-based research on antifreeze peptides:advances and perspectives

Antifreeze protein(AFP)can inhibit the growth of ice crystals to protect organisms from freezing damage,and demonstrates broad application prospects in food industry.Antifreeze peptides(AFPP)are specifi c peptides with functional domains showing antifreeze activity in AFP.Bioinformatics-based molecular simulation technology can more accurately explain the properties and mechanisms of biological macromolecules.Therefore,the binding stability of antifreeze peptides and antifreeze proteins(AFP(P))to ice and the molecular-scale growth kinetics of ice were analyzed by molecular simulation,which can make up for the limitations of experimental technology.This review concludes the molecular simulation-based research in the inhibition’s study of AFP(P)on ice growth,including sequence prediction,structure construction,molecular docking and molecular dynamics(MD)studies of AFP(P)on ice applications in growth inhibition.Finally,the review prospects the future direction of designing new antifreeze biomimetic materials through molecular simulation and machine learning.The information presented in this paper will help enrich our understanding of AFPP.