Results 131 to 140 of about 253 (210)

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Modulation of Local Environment for Selective Bicarbonate Conversion to Multi‐Carbon Products

Advanced Energy Materials, EarlyView.
Highly porous Cu electrode partially coated with an ionomer/carbon composite enables efficient conversion of bicarbonate to multi‐carbon products. By regulating the local reaction microenvironment, this strategy overcomes selectivity and reaction‐rate limitations, achieving Faradaic efficiencies of 43% for ethylene and 60% for total multi‐carbon ...
Tai Nguyen, Anh N. Nguyen, Guorui Gao, Jackson Crane, Thành Trần‐Phú, Gelson Tavares da Silva, Aldo Gonzalez‐Lopez, Hung Lai, Khac Huy Dinh, Duc‐Minh Phan‐Pham, Martin Guay, Carlos Escobedo, Rosalie K. Hocking, Jonathan Raisin, Lucia H. Mascaro, Cao‐Thang Dinh   +15 more
wiley   +1 more source

Simultaneous Electronic and Crystallographic Modulation of Cs3Bi2Br9 by Single‐Element Sn Substitution for Enhanced CO2 Photoreduction

Advanced Energy Materials, EarlyView.
Aliovalent B‐site substitution with Sn2+ in lead‐free perovskite, Cs3Bi2Br9, is employed to simultaneously induce lattice contraction and bromine vacancy through charge imbalance and ionic radius mismatch. The resulting structural distortion modulates the electronic structure and improves charge separation, leading to enhanced visible‐light‐driven CO2 ...
Justin Khor, Jiale Lee, Xin Ying Kong, Qixin Zhou, Yongfa Zhu, Jingxiang Low, Siang‐Piao Chai, Lling‐Lling Tan   +7 more
wiley   +1 more source

Electric Double Layer Engineering Induces O‐Down Interfacial Water Reorientation for Efficient and Long‐Term Seawater Electrolysis

Advanced Energy Materials, EarlyView.
The additive EDTMPS inhibits hydroxides deposition by chelating with Ca2+/Mg2+. It also disrupts the hydrogen bond network of interfacial water and expands the thickness of electric double layer, promoting more free water to flip into adsorbable O‐down configuration.
Tongzhou Li, Yutong Zhao, Hongjie Zhang, Yangkai Han, Zhiwei Ren, Feng Xie, Zongping Shao, Zhigang Shao   +7 more
wiley   +1 more source

Multifunctional Matrix‐Activated Electrodes Enabling Efficient Proton Transport and Durable Low‐Pt Operation for High‐Temperature Proton Exchange Membrane Fuel Cells

Advanced Energy Materials, EarlyView.
This work introduces a CeHP‐enabled multifunctional matrix‐activated electrode (MME) for high‐temperature PEM fuel cells, forming an electrochemically and mechanically functional matrix within the catalyst layer. The design increases peak power density by 38% and reduces voltage decay by 62% at 240 °C, while using only 40% of the Pt loading of a ...
Gyeongseok Gwak, Sunjin Park, Joonbyum Kim, Hena Lee, Jihyeon Ok, Chae Young Lee, JunHwa Kwon, Jong Geun Seong, Sung Pil Yoon, Suk‐Woo Nam, Hansung Kim, Katie Heeyum Lim, So Young Lee   +12 more
wiley   +1 more source

Surface‐Engineered Ti3C2Tx MXene/Cu2O Photocathodes for Highly Selective Photoelectrocatalytic CO2 Reduction to Ethanol

Advanced Energy Materials, EarlyView.
Surface‐engineering Ti3C2Tx MXenes with an in situ‐grown TiO2 layer on the surface in combination with visible‐light‐absorbing Cu2O significantly enhances charge separation and carrier extraction while improving Cu2O resistance to photocorrosion. The resulting Cu2O/TiO2‐MXene photocathodes enable highly selective, solar‐driven ethanol production ...
Luis A. M. Carrascosa, Magdaléna Linková, Monika Remzová, Tomáš Hrbek, Pavla Eliášová   +4 more
wiley   +1 more source

Surface‐Functionalized MXene for Enhanced Photocatalytic CO2 Reduction

Advanced Energy Materials, EarlyView.
Poly(catechol/p‐cresol)‐functionalized MXene (f‐MXene) enhances oxidation stability while preserving conductivity, enabling efficient interfacial electron transfer in a Cu/f‐MXene/reduced TiO2 photocatalyst. Strong Cu–f‐MXene synergy promotes CO2 activation and selective CH4 production, achieving a CH4 yield of 18.1 µmol g−1 (>200 × vs.
Dongyun Kim, Young Ho Park, Dong Gyu Lee, Kyuseok Lee, Junho Lee, Chaelin Shin, Jaeyoung Kim, B. Yamunasree, Soomin Han, Eunhee Gong, Hyeonjong Jeong, Min Gyu Kim, Chang‐Hee Cho, Seung Jun Lee, Soorathep Kheawhom, G. Murali, Tae Kyung Lee, Insik In, Su‐Il In   +18 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source