Results 191 to 200 of about 295,414 (245)

Carbon‐14 Perovskite Betavoltaics Reach Record 10.79% Efficiency

Carbon Energy, EarlyView.
A perovskite‐based betavoltaic cell incorporating carbon‐14 nanoparticles and formamidinium lead iodide achieves a record energy conversion efficiency of 10.79%. The device design enhances charge generation through an electron avalanche multiplication mechanism.
Hong Soo Kim, Ryan Rhee, Junho Lee, Muhammad Bilal Naseem, Jung Hwan Lee, Jong Hyeok Park, Su‐Il In   +6 more
wiley   +1 more source

Porous Carbon Materials for Carbon Dioxide Capture

Carbon Energy, EarlyView.
This work aims to address the current status and challenges associated with the regulation of pore structures, as well as the influence of pore structures on CO2 capture. Systematic quantitative analysis of structure–property relationships, combined with machine learning approaches, can effectively evaluate the contributions of structural ...
Zhifu Liu, Chuhan Fu, Tao Qi, Hui Zhou, Guangping Hao, Yalou Guo, Guoping Hu   +6 more
wiley   +1 more source

Key Challenges for Commercializing Perovskite–Silicon Tandem Solar Cells

Carbon Energy, EarlyView.
This review discusses the scientific and technological challenges in advancing perovskite–silicon tandem solar cells (PSTSCs) from lab‐scale to commercial viability, focusing on long‐term stability, scalability, and economic feasibility. Key issues include intrinsic and extrinsic degradation factors, installation conditions, environmental impacts, and ...
Bilal Mehmood, Yuanyuan Qin, Abdul Sattar, Rabeea Eman, Faheem Abbas, Shan Wu, Yipeng Xu, Xu Zheng, Jiangying Lu, Wei Jiang, Jiankai Liu, Shudi Lu, Shizhong Yue, Zhijie Wang, Kong Liu, Xiaopeng Zheng, Shengchun Qu   +16 more
wiley   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

A‐optimal model‐based design of experiments for processes with uncertain inputs

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Model‐based design of experiments (MBDoE) techniques are tools for selecting experimental conditions that enable accurate parameter estimation for mechanistic models. Most MBDoE approaches assume that the selected experimental conditions will be implemented perfectly, without uncertainties in the independent variables.
Bright Ofori, Iman Moshiritabrizi, Anh‐Duong Vo, Kimberley B. McAuley   +3 more
wiley   +1 more source

Clathrate hydrate crystallization

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Clathrate hydrate crystallization has been an area of interest to chemical engineers from the point of view of crystal structure, thermophysical properties, phase equilibria, kinetics, industrial applications, and environmental (climate change) implications.
Peter Englezos
wiley   +1 more source

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The Direct Simulation Monte Carlo Method

Computers in Physics, 1997
Numerical simulation of the hydrodynamics of gas flow and fluid flow is described using the Direct Simulation Monte Carlo method. (AIP) © 1997 American Institute of Physics.
Francis J Alexander, Alejandro L Garcia
exaly   +2 more sources

Analysis of discretization in the direct simulation Monte Carlo

Physics of Fluids, 2000
We propose a continuous-time formulation of the direct simulation Monte Carlo that allows the evaluation of the transport coefficient dependence on the time step through the use of the Green–Kubo theory. Our results indicate that the error exhibits quadratic dependence on the time step, and that for time steps of the order of one mean free time the ...
Nicolas G Hadjiconstantinou
exaly   +2 more sources

The Meshless Direct Simulation Monte Carlo method

Journal of Computational Physics, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yi Shen, Xiao Xu, Jun Zhang, Jing Liu, Zhaoming Zhang   +4 more
openaire   +1 more source

On an Axisymmetric Direct Simulation Monte Carlo Method

International Journal of Computational Fluid Dynamics, 2021
This study proposes an application of the variable time step (VTS) method to solve the non-conservation of fluxes encountered in the conventional direct simulation Monte Carlo (DSMC) method in axis...
Ming-Chung Lo, Chien-Yu Pan, Jong-Shinn Wu   +2 more
openaire   +1 more source