Unlocking enhanced thermal conductivity in polymer blends through active learning DOI Creative Commons
Jiaxin Xu, Tengfei Luo

npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)

Published: April 16, 2024

Abstract Polymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics has become a vital field for efficiently designing and developing polymeric materials. However, focus predominantly centered on single-component polymers, leaving vast chemical space blends relatively unexplored. This study employs high-throughput molecular dynamics (MD) simulation combined with active (AL) uncover enhanced thermal conductivity (TC) compared constituent polymers. Initially, TC about 600 amorphous polymers 200 varying blending ratios are determined through MD simulations. The optimal representation method is identified, which involves weighted sum approach that extends existing blends. An AL framework, combining ML, employed explore approximately 550,000 unlabeled framework proves highly effective accelerating discovery high-performance transport. Additionally, we delve into relationship between TC, radius gyration ( R g ), hydrogen bonding, highlighting roles inter- intra-chain interactions transport A significant positive association improvement indirect contribution H-bond interaction enhancement revealed log-linear model odds ratio calculation, emphasizing impact increasing enhancing blend TC.

Language: Английский

Recent progress in thermal energy recovery from the decoupled photovoltaic/thermal system equipped with spectral splitters DOI

Wenpeng Hong,

Boyu Li, Haoran Li

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2022, Volume and Issue: 167, P. 112824 - 112824

Published: Aug. 9, 2022

Language: Английский

Citations

31
Aggregation-induced emission materials: a platform for diverse energy transformation and applications DOI Open Access
Xue Li, Hao Yang, Ping Zheng

et al.

Journal of Materials Chemistry A, Journal Year: 2023, Volume and Issue: 11(10), P. 4850 - 4875

Published: Jan. 1, 2023

Motivated by the advantages of AIEgens in diversifying energy species and modulating transformation, application based on conversion solar, chemical, mechanical, electrical energies are summarized.

Language: Английский

Citations

22
Characterisation of soiling on glass surfaces and their impact on optical and solar photovoltaic performance DOI Creative Commons

Tarik Alkharusi,

Gan Huang, Christos N. Markides

et al.

Renewable Energy, Journal Year: 2023, Volume and Issue: 220, P. 119422 - 119422

Published: Oct. 6, 2023

Photovoltaic (PV) module soiling, i.e., the accumulation of soil deposits on surface a PV module, directly affects amount solar energy received by cells in that and can also give rise to additional heating, leading significant power generation losses. In this work, we present results from an extensive outdoor experimental testing campaign apply detailed characterisation techniques, consider resulting Soil sixty low-iron glass coupons was collected at various tilt angles over study period 12 months capture monthly, seasonal annual variations. Transmittance measurements showed horizontal experienced highest degree soiling. The wet-season, dry-season full-year samples relative transmittance decrease 65 %, 68 64 respectively, which corresponds predicted 67 70 66 % electrical generation. An analysis soiling matter using X-ray diffractometer scanning electron microscope presence particulate with diameters <10 μm (PM10), most prevalent studied region.

Language: Английский

Citations

21
Seasonal heat regulation in photovoltaic/thermal collectors with switchable backplate technology: Experiments and simulations DOI

Siyan Chan,

Bin Zhao,

Qiongwan Yu

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: 224, P. 120139 - 120139

Published: Feb. 13, 2024

Language: Английский

Citations

7
Unlocking enhanced thermal conductivity in polymer blends through active learning DOI Creative Commons
Jiaxin Xu, Tengfei Luo

npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)

Published: April 16, 2024

Abstract Polymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics has become a vital field for efficiently designing and developing polymeric materials. However, focus predominantly centered on single-component polymers, leaving vast chemical space blends relatively unexplored. This study employs high-throughput molecular dynamics (MD) simulation combined with active (AL) uncover enhanced thermal conductivity (TC) compared constituent polymers. Initially, TC about 600 amorphous polymers 200 varying blending ratios are determined through MD simulations. The optimal representation method is identified, which involves weighted sum approach that extends existing blends. An AL framework, combining ML, employed explore approximately 550,000 unlabeled framework proves highly effective accelerating discovery high-performance transport. Additionally, we delve into relationship between TC, radius gyration ( R g ), hydrogen bonding, highlighting roles inter- intra-chain interactions transport A significant positive association improvement indirect contribution H-bond interaction enhancement revealed log-linear model odds ratio calculation, emphasizing impact increasing enhancing blend TC.

Language: Английский

Citations

7