A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics
The quest for sustainable energy has led to significant advancements in photovoltaic (PV) technology. Traditional methods often lag, prompting the development of automated, high-throughput technologies. We introduce the MicroFactory, a self-driving digital twin that revolutionizes roll-to-roll (R2R)...
| Main Authors: | , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/178856 |
| _version_ | 1826119569187536896 |
|---|---|
| author | Ng, Leonard Wei Tat An, Na Gyeong Yang, Liu Zhou, Yinhua Chang, Dong Wook Kim, Jueng-Eun Sutherland, Luke J. Hasan, Tawfique Gao, Mei Vak, Doojin |
| author2 | School of Materials Science and Engineering |
| author_facet | School of Materials Science and Engineering Ng, Leonard Wei Tat An, Na Gyeong Yang, Liu Zhou, Yinhua Chang, Dong Wook Kim, Jueng-Eun Sutherland, Luke J. Hasan, Tawfique Gao, Mei Vak, Doojin |
| author_sort | Ng, Leonard Wei Tat |
| collection | NTU |
| description | The quest for sustainable energy has led to significant advancements in photovoltaic (PV) technology. Traditional methods often lag, prompting the development of automated, high-throughput technologies. We introduce the MicroFactory, a self-driving digital twin that revolutionizes roll-to-roll (R2R) printed PVs through high-throughput, closed-loop optimization. This platform combines printing-inspired automation with machine learning (ML) models to enhance PV device scalability and performance. By fabricating, characterizing, and analyzing 11,800 organic PV devices within 24 h, we leverage vast datasets to predict and refine fabrication parameters. Achieving a record 9.35% power conversion efficiency (PCE), a 1% improvement over just a single iteration, this approach exemplifies the potential of ML-driven designs and R2R processes, setting a new standard for sustainable energy, and demonstrates the transformative potential of integrating ML-driven designs with fabrication processes. |
| first_indexed | 2024-10-01T05:02:30Z |
| format | Journal Article |
| id | ntu-10356/178856 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:02:30Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1788562024-07-12T15:44:33Z A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics Ng, Leonard Wei Tat An, Na Gyeong Yang, Liu Zhou, Yinhua Chang, Dong Wook Kim, Jueng-Eun Sutherland, Luke J. Hasan, Tawfique Gao, Mei Vak, Doojin School of Materials Science and Engineering Engineering Machine learning Printed photovoltaics The quest for sustainable energy has led to significant advancements in photovoltaic (PV) technology. Traditional methods often lag, prompting the development of automated, high-throughput technologies. We introduce the MicroFactory, a self-driving digital twin that revolutionizes roll-to-roll (R2R) printed PVs through high-throughput, closed-loop optimization. This platform combines printing-inspired automation with machine learning (ML) models to enhance PV device scalability and performance. By fabricating, characterizing, and analyzing 11,800 organic PV devices within 24 h, we leverage vast datasets to predict and refine fabrication parameters. Achieving a record 9.35% power conversion efficiency (PCE), a 1% improvement over just a single iteration, this approach exemplifies the potential of ML-driven designs and R2R processes, setting a new standard for sustainable energy, and demonstrates the transformative potential of integrating ML-driven designs with fabrication processes. Ministry of Education (MOE) Nanyang Technological University Published version This work is supported by the Ministry of Education, Singapore, and the Nanyang Technological University’s College of Engineering International Postdoctoral Fellowship. This work was also supported by the Australian Center for Advanced Photovoltaics (ACAP) program funded by the Australian government through the Australian Renewable Energy Agency (ARENA). This work is also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A3A14039635). 2024-07-09T04:49:35Z 2024-07-09T04:49:35Z 2024 Journal Article Ng, L. W. T., An, N. G., Yang, L., Zhou, Y., Chang, D. W., Kim, J., Sutherland, L. J., Hasan, T., Gao, M. & Vak, D. (2024). A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics. Cell Reports Physical Science, 5(6), 102038-. https://dx.doi.org/10.1016/j.xcrp.2024.102038 2666-3864 https://hdl.handle.net/10356/178856 10.1016/j.xcrp.2024.102038 2-s2.0-85196315158 6 5 102038 en Cell Reports Physical Science © 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf |
| spellingShingle | Engineering Machine learning Printed photovoltaics Ng, Leonard Wei Tat An, Na Gyeong Yang, Liu Zhou, Yinhua Chang, Dong Wook Kim, Jueng-Eun Sutherland, Luke J. Hasan, Tawfique Gao, Mei Vak, Doojin A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics |
| title | A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics |
| title_full | A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics |
| title_fullStr | A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics |
| title_full_unstemmed | A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics |
| title_short | A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics |
| title_sort | printing inspired digital twin for the self driving high throughput closed loop optimization of roll to roll printed photovoltaics |
| topic | Engineering Machine learning Printed photovoltaics |
| url | https://hdl.handle.net/10356/178856 |
| work_keys_str_mv | AT ngleonardweitat aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT annagyeong aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT yangliu aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT zhouyinhua aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT changdongwook aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT kimjuengeun aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT sutherlandlukej aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT hasantawfique aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT gaomei aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT vakdoojin aprintinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT ngleonardweitat printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT annagyeong printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT yangliu printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT zhouyinhua printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT changdongwook printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT kimjuengeun printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT sutherlandlukej printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT hasantawfique printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT gaomei printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics AT vakdoojin printinginspireddigitaltwinfortheselfdrivinghighthroughputclosedloopoptimizationofrolltorollprintedphotovoltaics |