Results 181 to 190 of about 18,824 (262)

Anticancer Ruthenium Complexes with HDAC Isoform Selectivity. [PDF]

Molecules, 2020
Cross JM, Blower TR, Kingdon ADH, Pal R, Picton DM, Walton JW.   +5 more
europepmc   +1 more source

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

Streamlined synthetic assembly of α-chiral CAAC ligands and catalytic performance of their copper and ruthenium complexes. [PDF]

Chem Sci
Madron du Vigné A, Cramer N.
europepmc   +1 more source

Selective Electrolysis of Water Under Artificial Seawater Conditions Using Transition Metal Borate Anodes

Advanced Energy Materials, EarlyView.
The borate‐based cobalt‐iron catalyst promotes selective oxidation of water rather than chlorine evolution reaction during electrolysis of seawater. When integrated into a zero‐gap electrolyzer prototype, this catalyst supports robust splitting of artificial seawater into oxygen and hydrogen on an extended timescale.
Ananta R. Fareza, Hank Garg, Luis Miguel Azofra, Darcy Simondson, Tam D. Nguyen, Mohamed R. Rizk, Khang N. Dinh, Rosalie K. Hocking, Douglas R. MacFarlane, Hoang‐Long Du, Alexandr N. Simonov   +10 more
wiley   +1 more source

Recent Advances in Nano‐Microstructured Catalysts for Electrochemical Seawater Electrocatalysis

Advanced Energy Materials, EarlyView.
This review highlights advances in nano‐ and microstructured catalysts for electrochemical seawater conversion. It elucidates design principles, mechanistic understanding, and machine‐learning‐assisted discovery, and outlines key challenges and future opportunities toward efficient, selective, and durable seawater electrocatalysis.
Xiaodong Shao, Mengfang Liang, Xiaotong Han, Xu Yu, Huan Pang, Ho Seok Park   +5 more
wiley   +1 more source

Ruthenium complexes with triazenide ligands bearing an N-heterocyclic moiety, and their catalytic properties in the reduction of nitroarenes. [PDF]

RSC Adv
Romero-Soto CA, Iglesias AL, Velázquez-Ham AF, Camarena-Díaz JP, Correa-Ayala E, Gomez-Lopez JL, Chávez D, Ochoa-Terán A, Aguirre G, Rheingold AL, Grotjahn DB, Parra-Hake M, Miranda-Soto V.   +12 more
europepmc   +1 more source

Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries

Advanced Energy Materials, EarlyView.
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu, Huaping Zhao, Lizhuang Chen, Weidong Shi, Guosheng Shao, Yong Lei   +5 more
wiley   +1 more source

Ethylenediamine Complexes of Ruthenium

Platinum Metals Review, 1964
F. M. Lever, C. W. Bradford
openaire   +1 more source

Consumer Preferences for Craft Beer: The Interplay of Localness and Advertising Language

Agribusiness, EarlyView.
ABSTRACT This study explores the influence of the language of the label, origin of production, and origin of brewing ingredients on Croatian consumers' preferences and willingness to pay for organic craft beer. Employing an online survey and a choice experiment among 223 Croatian alcohol consumers, we find that while there's a willingness to pay a ...
Marija Cerjak, Claudia Bazzani, Andreas C. Drichoutis   +2 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source