Results 141 to 150 of about 160,060 (268)

From Materials to Systems: Challenges and Solutions for Fast‐Charge/Discharge Na‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review systematically analyzes the key characteristics limiting the fast‐charge/discharge capability of Na‐ion batteries (SIBs) from a multi‐scale perspective encompassing electrode materials, the electrode‐electrolyte interface, and the system. Furthermore, it presents practical solution strategies for the fundamental issues arising at each scale,
Bonyoung Ku, Sangbin Park, Sunha Hwang, Sunghyun Lim, Won‐Sub Yoon, Jongsoon Kim   +5 more
wiley   +1 more source

Going with the flow: endothelial dysfunction in type 2 diabetes. [PDF]

Function (Oxf)
Dunaway LS.
europepmc   +1 more source

Sulfide‐Based Electrolytes for All‐Solid‐State Sodium Batteries

Advanced Energy Materials, EarlyView.
This review covers the structural features and synthesis strategies of sulfide‐based solid electrolytes, as well as critical challenges related to conductivity, interfacial and moisture stability, and scaling‐up for practical application in Sodium‐based All Solid‐State Batteries.
Han Yang, Luca Weckelmann, Baolin Wu, Chih‐Long Tsai, Luc Raijmakers, Rüdiger‐A. Eichel, Anna Windmüller   +6 more
wiley   +1 more source

Under-Balcony Acoustic Diagnosis Using FOA-Based Directional Metrics: Early-Late Entropy and Vertical-Energy Discrepancy at 125 Hz, 1 kHz, and 4 kHz. [PDF]

Sensors (Basel)
Ting PC, Liu YC.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Preparation, Component Identification, and Antioxidant Activity of Anthocyanins From Punica granatum “Tunisia”

AgriFood: Journal of Agricultural Products for Food, EarlyView.
The preparation for Tunisian pomegranate anthocyanin extract. The molecular mechanisms underlying the antioxidant effects of these compounds were investigated using network pharmacology and molecular docking. Antioxidant activity was evaluated through DPPH, ABTS, and FRAP assays, whereas the effects on oxidative stress and inflammation were examined in
Jing Zhang, Xu‐han Yang, Meng‐qi Zhang, Xu Guo, Ning‐yang Li, Fang‐yu Long, Chao Liu   +6 more
wiley   +1 more source

Concert

, 2000
Peer ...
openaire   +1 more source

Wine Tourism as a Catalyst for Sustainable Performance: The Mediating Role of Corporate Legitimacy and Green Innovation

Agribusiness, EarlyView.
ABSTRACT This study aims to explore the influence of Wine Tourism (WT) on the Sustainable Performance (SP) of wineries in Spain. It particularly investigates how Corporate Social Legitimacy (CSL) and Green Innovation (GI) may act as intermediary factors in this relationship.
Javier Martínez‐Falcó, Eduardo Sánchez‐García, Bartolomé Marco‐Lajara, Luis A. Millán‐Tudela   +3 more
wiley   +1 more source

Mycobiome of low maintenance iconic landscape plant boxwood under repeated treatments of contact and systemic fungicides. [PDF]

Sci Rep
Li X, Hemmings G, Omolehin O, Tseng HT, Taylor A, Taylor C, Kong P, Gouker F, Hong C.   +8 more
europepmc   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source