[HTML][HTML] Multiscale modeling of electrolytes in porous electrode: From equilibrium structure to non-equilibrium transport

H Tao, C Lian, H Liu - Green Energy & Environment, 2020 - Elsevier
Understanding the mechanisms and properties of various transport processes in the
electrolyte, porous electrode, and at the interface between electrode and electrolyte plays a …

Advanced bioremediation by an amalgamation of nanotechnology and modern artificial intelligence for efficient restoration of crude petroleum oil-contaminated sites …

R Patowary, A Devi, AK Mukherjee - Environmental Science and Pollution …, 2023 - Springer
Crude petroleum oil spillage is becoming a global concern for environmental pollution and
poses a severe threat to flora and fauna. Bioremediation is considered a clean, eco-friendly …

An artificial neural network model for capacitance prediction of porous carbon-based supercapacitor electrodes

WZ Tawfik, SN Mohammad, KH Rahouma… - Journal of Energy …, 2023 - Elsevier
Among energy storage devices, the last decades have witnessed the rapid spread of usage
of carbon-based electrodes for electric double-layer capacitors (EDLCs) due to their large …

Removal of anthracene in water by MIL-88 (Fe), NH 2-MIL-88 (Fe), and mixed-MIL-88 (Fe) metal–organic frameworks

ZU Zango, K Jumbri, NS Sambudi, NHHA Bakar… - RSC …, 2019 - pubs.rsc.org
Three adsorbents based on the metal–organic frameworks (MOFs), viz.; MIL-88 (Fe), NH2-
MIL-88 (Fe), and mixed-MIL-88 (Fe) were synthesized using a microwave-assisted …

Insights into the estimation of capacitance for carbon-based supercapacitors

M Gheytanzadeh, A Baghban, S Habibzadeh… - RSC …, 2021 - pubs.rsc.org
Carbon-based materials are broadly used as the active component of electric double layer
capacitors (EDLCs) in energy storage systems with a high power density. Most of the …

Predicting the capacitance of carbon-based electric double layer capacitors by machine learning

H Su, S Lin, S Deng, C Lian, Y Shang, H Liu - Nanoscale Advances, 2019 - pubs.rsc.org
Machine learning (ML) methods were applied to predict the capacitance of carbon-based
supercapacitors. Hundreds of published experimental datasets are collected for training ML …

[HTML][HTML] Machine learning prediction of self-diffusion in Lennard-Jones fluids

JP Allers, JA Harvey, FH Garzon… - The Journal of Chemical …, 2020 - pubs.aip.org
Different machine learning (ML) methods were explored for the prediction of self-diffusion in
Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the …

Theoretical calculations, molecular dynamics simulations and experimental investigation of the adsorption of cadmium (ii) on amidoxime-chelating cellulose

L Zheng, S Zhang, W Cheng, L Zhang… - Journal of Materials …, 2019 - pubs.rsc.org
Amidoxime-chelating cellulose (ACCS) was obtained through comprehensive modification,
ie alkalization, etherification, amination and chelating; then, the adsorption of cadmium (II) …

Machine learning models for solvent effects on electric double layer capacitance

H Su, C Lian, J Liu, H Liu - Chemical Engineering Science, 2019 - Elsevier
The role of solvent molecules in electrolytes for supercapacitors, representing a fertile
ground for improving the capacitive performance of supercapacitors, is complicated and has …

Using spectral indices and terrain attribute datasets and their combination in the prediction of cadmium content in agricultural soil

PC Agyeman, V Khosravi, NM Kebonye, K John… - … and Electronics in …, 2022 - Elsevier
The continuous demand placed on farmland to yield optimal harvest is dependent on the
continuous application of agrochemicals and fertilizers to increase soil fertility and manage …