An interpretable hybrid Machine learning prediction of dielectric constant of alkali halide crystals

Deng, Jiandong; Jia, Guozhu

doi:10.1016/j.chemphys.2022.111457

Cited by 13 publications

(2 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Advanced Technologies Of Halub Conversion and Managementmentioning

confidence: 99%

See 1 more Smart Citation

Bioresource Upgrade for Sustainable Energy, Environment, and Biomedicine

Новоселов

et al. 2023

Nano-Micro Lett.

View full text Add to dashboard Cite

We conceptualize bioresource upgrade for sustainable energy, environment, and biomedicine with a focus on circular economy, sustainability, and carbon neutrality using high availability and low utilization biomass (HALUB). We acme energy-efficient technologies for sustainable energy and material recovery and applications. The technologies of thermochemical conversion (TC), biochemical conversion (BC), electrochemical conversion (EC), and photochemical conversion (PTC) are summarized for HALUB. Microalgal biomass could contribute to a biofuel HHV of 35.72 MJ Kg−1 and total benefit of 749 $/ton biomass via TC. Specific surface area of biochar reached 3000 m2 g−1 via pyrolytic carbonization of waste bean dregs. Lignocellulosic biomass can be effectively converted into bio-stimulants and biofertilizers via BC with a high conversion efficiency of more than 90%. Besides, lignocellulosic biomass can contribute to a current density of 672 mA m−2 via EC. Bioresource can be 100% selectively synthesized via electrocatalysis through EC and PTC. Machine learning, techno-economic analysis, and life cycle analysis are essential to various upgrading approaches of HALUB. Sustainable biomaterials, sustainable living materials and technologies for biomedical and multifunctional applications like nano-catalysis, microfluidic and micro/nanomotors beyond are also highlighted. New techniques and systems for the complete conversion and utilization of HALUB for new energy and materials are further discussed.

show abstract

Section: Advanced Technologies Of Halub Conversion and Managementmentioning

confidence: 99%

“…RF performed the best with the R 2 of 0.80 for the prediction of bio-oil yield, nitrogen in oil, and oil energy recovery. Therefore, the ML could be used to guide the experiments to produce bio-oil with a low N content [245]. Some progress has also been made in the application of ML in the synthesis of carbon materials from biomass.…”

Section: Machine Learningmentioning

confidence: 99%

Bioresource Upgrade for Sustainable Energy, Environment, and Biomedicine

Новоселов

et al. 2023

Nano-Micro Lett.

View full text Add to dashboard Cite

show abstract

Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

Yuan,

Luo,

Meng

2024

Adv Eng Mater

View full text Add to dashboard Cite

The traditional trial‐and‐error testing to develop high‐performance chemiresistive gas sensors is inefficient and fails to meet the high demand for sensors in various industries. Machine learning can address the limitations of trial‐and‐error testing and can be effectively utilized for enhancing, developing, and designing sensors. This review first discusses the prediction of critical mechanism parameters of gas‐sensitive materials by machine learning, including adsorption energy, band gap, thermal conductivity, and dielectric constant. Secondly, it proposes that machine learning can improve five performance indexes: selectivity, response/recovery time, stability, sensitivity, and accuracy. Machine learning also facilitates the development and structural design of gas‐sensitive new materials. In addition, the potential of machine learning to optimize the sensor arrays is investigated, including reducing the number of sensors, identifying the best array combination, and improving recognition and detection capabilities. Finally, this paper discusses the challenges and limitations of machine‐learning assisted chemiresistive gas sensors in practical applications and envisions their future development.This article is protected by copyright. All rights reserved.

show abstract