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The dynamic structure evolution of heterogeneous catalysts during reaction has gained great attention recently. However, controllably manipulating dynamic process and then feeding back catalyst design to extend the lifetime remains challenging. Herein, we proposed an entropy variation strategy to develop a dynamic CuZn-Co/HEOs catalyst, in which the non-active Co nano-islands play a crucial role in controlling thermal effect via timely capturing and utilizing reaction heat generated on the adjacent active CuZn alloys, thus solving the deactivation problem of Cu-based catalysts. Specifically, heat sensitive Co nano-islands experienced an entropy increasing process of slowly redispersion during the reaction. Under such heat dissipation effect, the CuZn-Co/HEOs catalyst exhibited 95.7 % ethylene selectivity and amazing long-term stability (>530 h) in the typical exothermic acetylene hydrogenation. Aiming at cultivating it as a catalyst with promising industrial potential, we proposed a simple regeneration approach via an entropy decreasing process.
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http://dx.doi.org/10.1002/anie.202412637 | DOI Listing |
Nat Mater
September 2025
Department of Physics and Organic and Carbon Electronics Laboratories (ORaCEL), North Carolina State University, Raleigh, NC, USA.
The number of polymeric and small-molecular acceptors for organic photovoltaics has exploded in the past decade. As a result, physical insights and efforts aiming at elucidating the coupling between composition and behaviour are required more than ever. Here we present an encompassing study into the phase behaviour of 55 polymer:small-molecular acceptor blends, pivotal in determining device performance and stability.
View Article and Find Full Text PDFBiol Psychiatry
September 2025
Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China. Electronic address:
Background: Major depressive disorder (MDD) has been increasingly understood as a disorder of network-level functional dysconnectivity. However, previous brain connectome studies have primarily relied on node-centric approaches, neglecting critical edge-edge interactions that may capture essential features of network dysfunction.
Methods: This study included resting-state functional MRI data from 838 MDD patients and 881 healthy controls (HC) across 23 sites.
Chaos
September 2025
MEMOTEF, Sapienza University of Rome, Roma 00161, Italy.
In this study, we construct surrogate stochastic processes that are challenging to distinguish from ordinary Brownian motion using a method based on the Schauder representation. Specifically, by assuming non-Gaussian (beta and uniform) distributions for the Schauder coefficients, we generate sample paths that preserve key properties of Brownian motion-such as quadratic variation, covariance structure, pointwise Hölder regularity, uncorrelated increments, as well as Gaussian marginal distributions. However, a deeper analysis relying on entropy-based measures and sliding-window spectral variance reveals that only the Gaussian-based construction preserves the expected randomness and the consistent spectral behavior of Brownian motion over time.
View Article and Find Full Text PDFBehav Neurol
September 2025
Department of Electronics and Communication Engineering, Chettinad Academy of Research and Education, Manamai Campus, Chennai, Tamil Nadu, India.
Temporary disturbances in brain function are caused by epilepsy, a chronic disorder resulting from sudden abnormal firing of brain neurons. This research introduces an innovative real-time methodology representing detecting epileptic spasms from electroencephalogram (EEG) data. It employs a support vector machine (SVM) alongside embedded zero tree wavelet (EZW) transform.
View Article and Find Full Text PDFClin Oral Investig
September 2025
Department of Oral & Maxillofacial Radiology, Faculty of Dentistry, Ege University, Bornova, İzmir, Türkiye.
Objectives: To evaluate the diagnostic potential of surface texture features extracted from clinical images in objectively differentiating benign from malignant oral lesions, and to validate classification performance of a Support Vector Machine (SVM) model using these features.
Materials And Methods: This study included 275 intraoral photographs of oral mucosal lesions with biopsy-confirmed diagnoses, sourced from both institutional archives and a public dataset. Lesion areas were manually annotated and converted into 3D surface plots to extract grayscale-based texture features.