Results 161 to 170 of about 8,597,555 (289)

Dynamic Etching‐Induced Cl‐Terminated Ti3C2Clx/Ti3ZnC2 Heterostructure for Ammonia Electrosynthesis and Zinc‐Nitrogen Batteries

open access: yesAdvanced Science, EarlyView.
A stable Ti3C2Clx/Ti3ZnC2 heterostructure, fabricated by dynamic etching, exhibits outstanding electrocatalytic NRR activity (NH3 yield: 20.1 µg h−1 mg−1, FE: 38.1%) in alkaline media. The corresponding Zn‐N2 battery achieves a peak power density of 36.5 µW cm−2 and an NH3 yield of 13.1 µg h−1 mg−1, providing an advanced strategy for NH3 and ...
Yu Wang   +9 more
wiley   +1 more source

Economic Effects of Renewable Energy Expansion: A Model-Based Analysis for Germany [PDF]

open access: yes
Increasing utilization of renewable energy sources (RES) is a priority worldwide. Germany has been a forerunner in the deployment of RES and has ambitious goals for the future.
Wolf-Peter Schill   +3 more
core  

Insights Into CO2 Loss, pH Effects, and Tafel Kinetics in Ni Single Atom‐Driven Bicarbonate Electroreduction

open access: yesAdvanced Science, EarlyView.
Selective CO formation in bicarbonate electrolysis is achieved by retaining reactive CO2 and tuning pH, while the competing hydrogen evolution reaction occurs at the interface. The rate‐determining step couples the chemical equilibrium of bicarbonate/CO2 with electrochemical charge transfer, offering insight into optimizing reaction conditions for ...
Lin Li   +7 more
wiley   +1 more source

Economic Impacts from the Promotion of Renewable Energy Technologies - The German Experience [PDF]

open access: yes
The allure of an environmentally benign, abundant, and cost-effective energy source has led an increasing number of industrialized countries to back public financing of renewable energies.
Manuel Frondel   +3 more
core  

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

open access: yesAdvanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin   +12 more
wiley   +1 more source

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