{"682748":{"#nid":"682748","#data":{"type":"event","title":"PhD Proposal by Honglin Liu","body":[{"value":"\u003Cp\u003E\u003Cbr\u003EHonglin Liu\u003Cbr\u003EAdvisor: Dr. Youjiang Wang, Co- Advisor: Dr. Karl I. Jacob\u003Cbr\u003E\u003Cbr\u003Ewill propose a doctoral thesis entitled,\u003Cbr\u003E\u003Cbr\u003EPrediction and Optimization of Graphene Aerogel for Membrane Distillation Using Experimental, Molecular Dynamics, and Machine Learning Approaches\u003Cbr\u003E\u003Cbr\u003E\u003Cbr\u003EOn\u003Cbr\u003E\u003Cbr\u003EWednesday, June 25 at 9:30 a.m. (EDT)\u003Cbr\u003E\u003Cbr\u003EVirtually via MS Teams\u0026nbsp;\u003Cbr\u003E[Meeting link]\u003C\/p\u003E\u003Cp\u003EMeeting ID: 215 050 562 617 6\u003Cbr\u003EPasscode: vH3QQ2Vy\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003ECommittee\u003Cbr\u003EDr. Youjiang Wang \u2013 \u0026nbsp;School of Materials Science and Engineering (advisor)\u003Cbr\u003EDr. Karl I. Jacob \u2013 School of Materials Science and Engineering, George W. Woodruff School of Mechanical Engineering (co-advisor)\u003Cbr\u003EDr. Donggang Yao \u2013 School of Materials Science and Engineering\u0026nbsp;\u003Cbr\u003EDr. Hamid Garmestani \u2013 School of Materials Science and Engineering\u0026nbsp;\u003Cbr\u003EDr. S. Mostafa Ghiaasiaan \u2013 George W. Woodruff School of Mechanical Engineering\u003Cbr\u003E\u003Cbr\u003EAbstract\u003Cbr\u003EThe growing global freshwater scarcity has been driving the development of advanced desalination technologies, with membrane distillation (MD) recognized as a promising next-generation approach due to its ability to utilize solar or low-grade thermal (LoT) energy and its insensitivity to high-salinity water. This dissertation work adds to that growing literature in this area by investigating the performance prediction and optimization of graphene aerogel (GA)-based membranes for cost-effective membrane distillation through a systematic approach, integrating experimental, molecular simulations, and machine learning methodologies. This study encompasses three key components: (1) design and construction of a fully automated direct contact membrane distillation (DCMD) testing platform using 3D printing and Python scripting to evaluate the influence of operational parameters on permeate flux. The results highlight the significant impacts of flow rate, feed temperature, membrane material, and long-term inorganic scaling phenomena. (2) use of fully-atomistic molecular dynamics simulations to quantitatively elucidate the relationships among structural parameters (average length of graphene sheets and dummmy inclusions\u0027 distance to zero potential), graphene aerogel\u2019s morphological characteristics (pore channel diameter, density, thickness, porosity, specific surface area, and tortuosity), and its heat and mass transfer properties (water molecule diffusivity, permeate flux, thermal conductivity, and localized phonon transport) in a vacuum membrane distillation (VMD) process. A predictive equation set with high accuracy and strong physical interpretability for transfer performance was also developed. (3) Development of a novel Transformer-enhanced 3D convolutional neural network (TRM-CNN) model, leveraging a scalable dataset generated from high-throughput molecular dynamics simulations, to predict graphene aerogel\u2019s morphological and heat and mass transfer properties, achieving superior computational efficiency and predictive accuracy compared to conventional convolutional neural network models. The findings from this work collectively form a scalable and reliable end-to-end framework for predicting and optimizing graphene aerogel\u2019s structural, mass, and heat transfer properties, providing actionable guidance for designing energy-efficient membrane distillation processes.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EPrediction and Optimization of Graphene Aerogel for Membrane Distillation Using Experimental, Molecular Dynamics, and Machine Learning Approaches\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Prediction and Optimization of Graphene Aerogel for Membrane Distillation Using Experimental, Molecular Dynamics, and Machine Learning Approaches"}],"uid":"27707","created_gmt":"2025-06-10 18:01:17","changed_gmt":"2025-06-10 18:01:48","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-06-25T09:30:00-04:00","event_time_end":"2025-06-25T11:00:00-04:00","event_time_end_last":"2025-06-25T11:00:00-04:00","gmt_time_start":"2025-06-25 13:30:00","gmt_time_end":"2025-06-25 15:00:00","gmt_time_end_last":"2025-06-25 15:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtually via MS Teams ","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}