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Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg0 sensing materials

Author

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  • Zhao, Haitao
  • Ezeh, Collins I.
  • Ren, Weijia
  • Li, Wentao
  • Pang, Cheng Heng
  • Zheng, Chenghang
  • Gao, Xiang
  • Wu, Tao

Abstract

The detrimental impact of urban airborne Hg0 from fossil fuel utilization has necessitated the discovery and development of Hg0 sensing materials for effective Hg0 detection and mitigation of the pollutant. Earlier studies have hypothetically and experimentally supported 2-dimensional transition-metal dichalcogenides (2D TMDCs), particularly MoS2 to have excellent performance for Hg0 removal. However, the potential of other TMDCs is yet to be investigated for Hg0 sensor application. In this study, a total of 28 transition metals within periods 4–6 of the periodic table, excluding the lanthanides series, were examined. To ensure proper data management flow, a high-throughput data mining approach with integrated machine learning and cheminformatics simulation approaches is developed. The systemic approach integrates the Pymatgen, Factsage, Aflow and density functional theory simulation tools for accelerated discovery of suitable TMDCs from raw data via the chemical vapour reaction route. Predicted results showed that TiS2, NiS2, ZrS2, MoS2, PdS2 and WS2 exhibited TMDCs characteristics. Furthermore, first-principles calculation shows Hg-uptake capacity is in the order NiS2 > PdS2 > TiS2 > ZrS2 > WS2 > MoS2, while Hg sensing response is in the order PdS2 > MoS2 > WS2 > ZrS2 > NiS2 > TiS2. Accordingly, PdS2 depicted to be the most suitable TMDCs for airborne Hg0 sensor application. The proposed systemic approach is an initial platform for materials discovery using integrated machine learning approaches and is well-suited for the screening and the discovery of new materials based on component-oriented structures.

Suggested Citation

  • Zhao, Haitao & Ezeh, Collins I. & Ren, Weijia & Li, Wentao & Pang, Cheng Heng & Zheng, Chenghang & Gao, Xiang & Wu, Tao, 2019. "Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg0 sensing materials," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s0306261919313388
    DOI: 10.1016/j.apenergy.2019.113651
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    as
    1. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
    2. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    3. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    4. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    5. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    6. Chen, Bailian & Harp, Dylan R. & Lin, Youzuo & Keating, Elizabeth H. & Pawar, Rajesh J., 2018. "Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach," Applied Energy, Elsevier, vol. 225(C), pages 332-345.
    7. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C. & Chan, Wilco W.H., 2016. "Prediction of diffuse solar irradiance using machine learning and multivariable regression," Applied Energy, Elsevier, vol. 181(C), pages 367-374.
    8. Ou, Jiamin & Meng, Jing & Zheng, Junyu & Mi, Zhifu & Bian, Yahui & Yu, Xiang & Liu, Jingru & Guan, Dabo, 2017. "Demand-driven air pollutant emissions for a fast-developing region in China," Applied Energy, Elsevier, vol. 204(C), pages 131-142.
    9. Chen, Qin & Rosner, Fabian & Rao, Ashok & Samuelsen, Scott & Jayaraman, Ambal & Alptekin, Gokhan, 2019. "Simulation of elevated temperature solid sorbent CO2 capture for pre-combustion applications using computational fluid dynamics," Applied Energy, Elsevier, vol. 237(C), pages 314-325.
    10. Díaz, Santiago & Carta, José A. & Matías, José M., 2018. "Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques," Applied Energy, Elsevier, vol. 209(C), pages 455-477.
    11. Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md., 2018. "A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market," Applied Energy, Elsevier, vol. 232(C), pages 341-357.
    12. Pereira, Luís M.C. & Llovell, Fèlix & Vega, Lourdes F., 2018. "Thermodynamic characterisation of aqueous alkanolamine and amine solutions for acid gas processing by transferable molecular models," Applied Energy, Elsevier, vol. 222(C), pages 687-703.
    13. Zhao, Haitao & Mu, Xueliang & Yang, Gang & George, Mike & Cao, Pengfei & Fanady, Billy & Rong, Siyu & Gao, Xiang & Wu, Tao, 2017. "Graphene-like MoS2 containing adsorbents for Hg0 capture at coal-fired power plants," Applied Energy, Elsevier, vol. 207(C), pages 254-264.
    14. Edwards, Richard E. & New, Joshua & Parker, Lynne E. & Cui, Borui & Dong, Jin, 2017. "Constructing large scale surrogate models from big data and artificial intelligence," Applied Energy, Elsevier, vol. 202(C), pages 685-699.
    15. Ahn, Jonghoon & Cho, Soolyeon, 2017. "Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments," Applied Energy, Elsevier, vol. 204(C), pages 117-130.
    16. Umair, Malik Muhammad & Zhang, Yuang & Iqbal, Kashif & Zhang, Shufen & Tang, Bingtao, 2019. "Novel strategies and supporting materials applied to shape-stabilize organic phase change materials for thermal energy storage–A review," Applied Energy, Elsevier, vol. 235(C), pages 846-873.
    Full references (including those not matched with items on IDEAS)

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