[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126458-en":3,"doc-seo-126458-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126458,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Molecular Dynamics and Machine Learning-based Investigation of Low-carbon Fuel Combustion - Doctoral Dissertation","Low-carbon fuels, including biofuels and electrofuels (e.g., biodiesel, dimethyl ether, methanol, and oxymethylene ethers), together with zero-carbon fuels such as ammonia and hydrogen, are key to cutting emissions in hard-to-abate sectors like heavy-duty transport and industry. Detailed knowledge of chemical reaction mechanisms and efficient combustion optimisation methods are therefore essential. Reactive molecular dynamics simulations with reactive force fields examine representative low-carbon fuel blends, clarifying how molecular structure links to emission characteristics under combustion-relevant conditions.","Molecular Dynamics and Machine Learning-based Investigation of Low-carbon Fuel Combustion  \nZhihao Xing  \nSupervisor: Prof. Xi Jiang  \nSchool of Engineering and Materials Science Queen Mary University of London  \nThis dissertation is submitted for the degree of Doctor of Philosophy  \nJune 2025  \nStatement of originality  \nI, Zhihao Xing, acknowledge the ethical use of Generative Artificial Intelligence to support my editing, proofreading and/or reference list generation in this thesis. I confirm that I have kept and can provide (if requested) detailed records of my input into Generative Artificial Intelligence tools, the outputs I received, and how I used these outputs. I used the following GenAI programme(s): ChatGPT.  \nI accept that Queen Mary University of London has the right to use plagiarism detection software to check the electronic version of the thesis.  \nI confirm that this thesis has not been previously submitted for the award of a degree by this or any other university.  \nThe copyright of this thesis rests with the author and no quotation from it, or information derived from it may be published without the prior written consent of the author.  \nSignature:  \nDate: June 2025  \nDetails of collaboration and publications:  \n[1] Z. Xing, R.S.M. Freitas, X. Jiang*, Machine learning-driven multi-objective optimisation of ammonia co-firing with highly reactive fuels, Energy Convers. Manag. 341 (2025) 120071. [https://doi.org/10.1016/j.enconman.2025.120071](https://doi.org/10.1016/j.enconman.2025.120071) .  \n[2] Z. Xing, X. Jiang*, Neural network potential-based molecular investigation of pollutant formation of ammonia and ammonia-hydrogen combustion, Chem. Eng. J. 489 (2024) 151492. [https://doi.org/10.1016/j.cej.2024.151492](https://doi.org/10.1016/j.cej.2024.151492) .  \n[3] Z. Xing, R.S.M. Freitas, X. Jiang*, Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia, Energy and AI 18 (2024) 100454. [https://doi.org/10.1016/j.egyai.2024.100454](https://doi.org/10.1016/j.egyai.2024.100454) .  \n[4] Z. Xing, X. Jiang*, R.F. Cracknell, Investigation of the chemical mechanism of pollutant formation in co-firing of ammonia and biomass lignin, Int. J. Hydrog. Energy 77 (2024) 126-137. [https://doi.org/10.1016/j.ijhydene.2024.06.171](https://doi.org/10.1016/j.ijhydene.2024.06.171) .  \n[5] Z. Xing, C. Chen, X. Jiang*, A molecular investigation on the mechanism of copyrolysis of ammonia and biodiesel surrogates, Energy Convers. Manag. 289 (2023) 117164. [https://doi.org/10.1016/j.enconman.2023.117164](https://doi.org/10.1016/j.enconman.2023.117164) .  \n[6] Z. Xing, M. Yu, C. Chen, X. Jiang*, A molecular investigation on the effects of OMEX addition on soot inception of diesel pyrolysis, Fuel 346 (2023) 128357. [https://doi.org/10.1016/j.fuel.2023.128357](https://doi.org/10.1016/j.fuel.2023.128357) .  \n[7] R.S.M. Freitas*, Z. Xing, F.A. Rochinha, R.F. Cracknell, D. Mira, N. Karimi, X. Jiang, Pathways to sustainable fuel design from a probabilistic deep learning perspective, Adv. Appl. Energy. 19 (2025) 100226. [https://doi.org/10.1016/j.adapen.2025.100226](https://doi.org/10.1016/j.adapen.2025.100226) .  \n[8] M. Yu, C. Chen, Z. Xing, X. Jiang*, ReaxFF molecular dynamics simulation of nickel catalysed gasification of cellulose in supercritical water, Int. J. Hydrog. Energy (2022) S036031992204438X. [https://doi.org/10.1016/j.ijhydene.2022.09.202](https://doi.org/10.1016/j.ijhydene.2022.09.202) .  \n[9] C. Chen, D. Mira, Z. Xing, X. Jiang*, Thermophysical property prediction of biodiesel mixtures at extreme conditions using molecular dynamics simulation, J. Mol. Liq. (2022) 120423. [https://doi.org/10.1016/j.molliq.2022.120423](https://doi.org/10.1016/j.molliq.2022.120423) .  \nAcknowledgements  \nFirst of all, I want to say thank you to myself. Life as a PhD student has been far more challenging than I ever imagined, and I am grateful that I have managed to persevere through it all","cbCaivAiWp8ZUjQU","https://ap.wps.com/l/cbCaivAiWp8ZUjQU","pdf",16433368,7,1,296,"English","en",105,"# Statement of originality\n## Details of collaboration and publications\n# Acknowledgements\n# Abstract","[{\"question\":\"What is the dissertation’s research focus?\",\"answer\":\"It investigates low-carbon fuel combustion by linking chemical reaction mechanisms to emission characteristics using reactive molecular dynamics and machine learning approaches.\"},{\"question\":\"Which fuels are discussed as targets in the work?\",\"answer\":\"The abstract highlights low-carbon fuels such as biodiesel, dimethyl ether, methanol, and oxymethylene ethers, and zero-carbon fuels including ammonia and hydrogen.\"},{\"question\":\"How does the dissertation study combustion-related emissions?\",\"answer\":\"Reactive molecular dynamics simulations using reactive force fields are performed on representative fuel blends to examine relationships between molecular structure and emission outcomes.\"}]","Molecular Dynamics and Machine Learning-based Investigation of Low-carbon Fuel Combustion - 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