[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124210-en":3,"doc-seo-124210-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124210,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning-Assisted 3D Printing of Thermoelectric Materials of Ultrahigh Performances at Room Temperature","Thermoelectric energy conversion enables electricity generation from waste heat and solid-state cooling, but conventional manufacturing is costly and restricts device geometries. This work introduces an extrusion printing approach to fabricate high-performance thermoelectric materials with complex 3D architectures. High-throughput experimentation combined with Bayesian optimization accelerates the joint search for optimal ink formulation and printing parameters. A Gaussian process regression model predicts thermoelectric power factor from process variables, yielding printed BiSbTe materials with an ultrahigh room-temperature zT of 1.3. The ML-guided ink-based strategy is broadly generalizable for functional materials and devices.","Received 00th January 20xx, Accepted 00th January 20xx  \nDOI: 10. 1039/x0xx00000x  \nMachine Learning-Assisted 3D Printing of Thermoelectric Materials of Ultrahigh Performances at Room Temperature  \nKaidong Song,‡a Guoyue Xu,‡a A. N. M. Tanvir,‡a Ke Wang,b Md Omarsany Bappy,a Haijian Yang,c Wenjie Shang,a Le Zhou,c Alexander Dowling,b Tengei Luo *a and Yanliang Zhang*a  \nThermoelectric energy conversion is an attractive technology for generating electricity from waste heat and using electricity for solid-state cooling. However, conventional manufacturing processes for thermoelectric devices are costly and limited to simple device geometries. This work reports an extrusion printing method to fabricate high-performance thermoelectric materials with complex 3D architectures. By integrating high-throughput experimentation and Bayesian optimization (BO), our approach significantly accelerates the simultaneous search for the optimal ink formulation and printing parameters that deliver high thermoelectric performances while maintaining desired shape fidelity. A Gaussian process regression (GPR) -based machine learning model is employed to expeditiously predict thermoelectric power factor as a function of ink formulation and printing parameters. The printed bismuth antimony telluride (BiSbTe)-based thermoelectric materials under the optimized conditions exhibit an ultrahigh room temperature zT of 1.3, which is by far the highest in the printed thermoelectric materials. The machine learning-guided ink-based printing strategy can be highly generalizable to a wide range of functional materials and devices for broad technological applications.  \nIntroduction  \nThermoelectric devices (TEDs) are solid-state energy converters that generate electricity when subjected to an external temperature gradient or create a temperature difference and act as solid-state coolers when provided with electric current. The ability of TEDs to convert heat into electricity and vice versa has sparked tremendous research interest in developing highefficiency devices for waste heat recovery and solid-state cooling in the past two decades.1–12 Two-thirds of the world's energy consumption remains dissipated as waste heat, and harnessing this wasted energy more efficiently can produce 15 terawatts of electrical power in the US alone.13 Meanwhile, cooling and thermal management are essential to human comfort in buildings and vehicles, as well as to the reliable operation and longevity of electronic and medical devices. The solid-state nature of thermoelectrics makes it an attractive environmentally friendly technology for energy harvesting and cooling because it does not require moving parts or environmentally harmful refrigerants.14  \nThe efficiency of thermoelectric materials is determined by the dimensionless figure of merit zT = S2σκ-1T, where S denotes the Seebeck coefficient, σ is the electrical conductivity, κ is the thermal conductivity, and T is the absolute temperature.15 Achieving high zT requires improving the thermoelectric power  \na. Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. E-mail: [tluo@nd.edu](tluo@nd.edu); [yzhang45@nd.edu](yzhang45@nd.edu)  \n[b.](b. Department of Chemical and Biomolecular Engineering)[ Department of Chemical and Biomolecular Engineering](b. Department of Chemical and Biomolecular Engineering), University of Notre Dame, Notre Dame, IN 46556, USA.  \nc. Department of Mechanical Engineering, Marquette University, Milwaukee, WI 53233, USA.  \n‡ These authors contributed equally to this work  \n†Electronic Supplementary Information (ESI) available. See DOI: 10. 1039/x0xx00000x  \nfactor S2σ while reducing the thermal conductivity. 15,16 Despite recent progress in increasing the zT values, the reported high zT materials still rely on conventional manufacturing methods, including hot pressing, arc melting, zone melting, and spark plasma sintering, which can only produce simple bulk","cbCaihX6pXrq855v","https://ap.wps.com/l/cbCaihX6pXrq855v","pdf",3223324,1,10,"English","en",105,"# Introduction\n## Thermoelectric devices and energy/cooling motivation\n## Figure of merit zT and material efficiency targets\n## Limits of conventional manufacturing\n## 3D printing and extrusion/DIW for thermoelectrics\n## Need for ML-accelerated ink and parameter optimization","[{\"question\":\"What problem does the paper address in manufacturing thermoelectric devices?\",\"answer\":\"Conventional thermoelectric manufacturing is expensive and mainly limited to simple geometries, and converting bulk materials into usable devices is also lengthy and costly.\"},{\"question\":\"How does the proposed method improve optimization of thermoelectric printing?\",\"answer\":\"It combines high-throughput experimentation with Bayesian optimization to jointly search for optimal ink formulation and printing parameters while maintaining printability and shape fidelity.\"},{\"question\":\"What performance result is reported for the printed thermoelectric material?\",\"answer\":\"The optimized printed BiSbTe-based thermoelectric materials achieve an ultrahigh room-temperature zT of 1.3, reported as the highest among printed thermoelectric materials.\"}]","Machine Learning-Assisted 3D Printing of Thermoelectric Materials of Ultrahigh Performances at Room Temperature | 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problem does the paper address in manufacturing thermoelectric devices?","Question",{"text":75,"@type":76},"Conventional thermoelectric manufacturing is expensive and mainly limited to simple geometries, and converting bulk materials into usable devices is also lengthy and costly.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method improve optimization of thermoelectric printing?",{"text":80,"@type":76},"It combines high-throughput experimentation with Bayesian optimization to jointly search for optimal ink formulation and printing parameters while maintaining printability and shape fidelity.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance result is reported for the printed thermoelectric material?",{"text":84,"@type":76},"The optimized printed BiSbTe-based thermoelectric materials achieve an ultrahigh room-temperature zT of 1.3, reported as the highest among printed thermoelectric 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