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Ion−conducting hydrogels show significant potential in plant growth monitoring. Nevertheless, traditional ionic hydrogel sensors experience substantial internal creep and inadequate sensitivity, hindering precise plant growth monitoring. In this study, we developed a flexible hydrogel sensor composed of polyvinyl alcohol and acrylamide. The hydrogel sensor exhibits low creep and high sensitivity. Polyvinyl alcohol, acrylamide, and glycerol are crosslinked to create a robust interpenetrating double network structure. The strong interactions, such as van der Waals forces, between the networks minimize hydrogel creep under external stress, reducing the drift ratio by 50% and the drift rate by more than 60%. Additionally, sodium chloride and AgNWs enrich the hydrogel with conductive ions and pathways, enhancing the sensor's conductivity and demonstrating excellent response time (0.4 s) and recovery time (0.3 s). When used as a sensor for plant growth monitoring, the sensor exhibits sensitivity to small strains and stability for long−term monitoring. This sensor establishes a foundation for developing plant health monitoring systems utilizing renewable biomass materials. [ABSTRACT FROM AUTHOR]

Nowadays, the massive industrial data has effectively improved the performance of the data-driven deep learning Remaining Useful Life (RUL) prediction method. However, there are still problems of assigning fixed weights to features and only coarse-grained consideration at the sequence level. This paper proposes a Transformer-based end-to-end feature-level mask self-supervised learning method for RUL prediction. First, by proposing a fine-grained feature-level mask self-supervised learning method, the data at different time points under all features in a time window is sent to two parallel learning streams with and without random masks. The model can learn more fine-grained degradation information by comparing the information extracted by the two parallel streams. Instead of assigning fixed weights to different features, the abstract information extracted through the above process is invariable correlations between features, which has a good generalization to various situations under different working conditions. Then, the extracted information is encoded and decoded again using an asymmetric structure, and a fully connected network is used to build a mapping between the extracted information and the RUL. We conduct experiments on the public C-MAPSS datasets and show that the proposed method outperforms the other methods, and its advantages are more obvious in complex multi-working conditions. [ABSTRACT FROM AUTHOR]

ABSTRACT Metal–organic framework (MOF) nanozymes have garnered widespread attention in the field of biomimetic catalysis due to their highly controllable porous structure and surface functionalization, enabling them to mimic the catalytic activity and specificity of natural enzymes while offering greater stability and reusability. In recent years, significant progress has been made in the theoretical computation studies of MOF nanozymes' catalytic reactions, providing deep insights into their catalytic and analytical mechanisms. This review comprehensively gathers the latest research progress of theoretical calculation–driven MOF nanozymes on their catalytic mechanisms and applications. First, the methods that can be used for theoretical calculations of MOF nanozymes, especially density functional theory (DFT), are reviewed to help deeply analyze the active site distribution, electron transfer pathways, and adsorption and activation mechanisms of reactants of MOF nanozymes. Subsequently, this review deeply studies the contribution of these theoretical calculation methods to revealing the catalytic reaction mechanism of MOF nanozymes (such as active sites and enzyme‐like activity), especially the specific role of electron transfer and reaction energy changes in key reaction steps. Finally, this review looks forward to the challenges and opportunities in the theoretical computation for the future design and application of MOF nanozymes, especially in accurately predicting catalytic activities, understanding complex reaction mechanisms, and guiding the synthesis of new nanozyme materials. By integrating theoretical calculations with experimental research, the study of MOF nanozymes is expected to forge new avenues in biomimetic catalysis and provide effective strategies for addressing challenges in the fields of sensing, biomedicine, and environmental protection. [ABSTRACT FROM AUTHOR]

The electrocatalytic conversion of carbon dioxide (CO 2 ) into valuable multicarbon (C 2+ ) compounds offers a promising approach to mitigate CO 2 emissions and harness renewable energy. However, achieving precise selectivity for specific C 2+ products, such as ethylene and ethanol, remains a formidable challenge. This study shows that incorporating elemental boron (B) into copper (Cu) catalysts provides additional adsorption sites for * CO intermediates, enhancing the selectivity of desirable C 2+ products. Additionally, using a nickel single-atom catalyst (Ni-SAC) as a * CO source increases local * CO concentration and reduces the hydrogen evolution reaction. In situ experiments and density functional theory (DFT) calculations reveal that surface-bound boron units adsorb and convert * CO more efficiently, promoting ethylene production, while boron within the bulk phase of copper influences charge transfer, facilitating ethanol generation. In a neutral electrolyte, the bias current density for ethylene production using the B-O-Cu2@Ni-SAC0.05 hybrid catalyst exceeded 300 mA cm -2 , and that for ethanol production with B-O-Cu5@Ni-SAC0.2 surpassed 250 mA cm -2 . This study underscores that elemental doping in Cu-based catalysts not only alters charge and crystalline phase arrangements at Cu sites but also provides additional reduction sites for coupling reactions, enabling the efficient synthesis of distinct C 2+ products., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Background: Understanding review comments holds significant importance within the realm of scientific discourse. This study aims to conduct an empirical analysis of factors associated with praise in peer review. Methods: The study involved manual labeling of "praise" in 952 review comments drawn from 301 articles published in the British Medical Journal, followed by regression analysis. Results: The study reveals that authors tend to receive longer praise when they share a cultural proximity with the reviewers. Additionally, it is observed that female reviewers are more inclined to provide praise Conclusions: In summary, these discoveries contribute valuable insights for the development of a constructive peer review process and the establishment of a more inclusive research culture.

The research on electrocatalytic carbon dioxide reduction (ECR) catalysts using renewable energy is particularly crucial in energy conversion studies, especially for viable hydrocarbon production. This study employs density functional theory calculations to screen a series of non-radioactive lanthanide two-dimensional metal-organic frameworks (MOFs) for product selectivity in ECR. Based on theoretical screening, our focus is on a lutetium (Lu)-based conducting MOF (Lu-HHTP), which exhibits a Faradaic efficiency of approximately 77% for methane (CH4) production and maintains a stable current density of -280 mA/cm2 at -1.1 V vs. RHE. In situ electrochemical experiments and material characterization demonstrate that the Lu sites possess high coordination stability and structural recoverability during catalytic CO2 reduction, attributed to the overlap between Lu's f-orbitals and the π*-orbitals of the ligand O, and the formation of back bonding orbitals between the f-orbitals of Lu and the π* orbitals of CO contribute increasing CH₄ selectivity and lowering the potential. This study leverages rare-earth MOF-type materials, offering a novel approach to addressing low conductivity and stabilizing rare-earth materials, thereby establishing a theoretical framework for the conversion of linearly adsorbed *CO into hydrocarbons., (© 2024 Wiley‐VCH GmbH.)