Enhancing the performance of 3D printed bio-photoelectrochemical cells through multi-objective Bayesian optimisation
Attaching thylakoid membranes (TM) on bio-photo-electrochemical cells (BPEC) enables energy harvesting through photoelectrode extraction. However, the attachment methods rely on thin coating methods such as dip-coating, drop-casting or electrospray deposition. We herein demonstrate the use of direct...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Virtual and Physical Prototyping |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17452759.2024.2449565 |
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| author | JongHyun Kim JaeHyoung Yun Mirkomil Sharipov Jinwook Moon WonHyoung Ryu |
| author_facet | JongHyun Kim JaeHyoung Yun Mirkomil Sharipov Jinwook Moon WonHyoung Ryu |
| author_sort | JongHyun Kim |
| collection | DOAJ |
| description | Attaching thylakoid membranes (TM) on bio-photo-electrochemical cells (BPEC) enables energy harvesting through photoelectrode extraction. However, the attachment methods rely on thin coating methods such as dip-coating, drop-casting or electrospray deposition. We herein demonstrate the use of direct ink writing in coating TM on BPEC cells, aiming for rapid prototyping and mass production of BPEC cells. As photoelectron extraction through high TM loadings is not feasible, we investigate previously reported conducting materials to be used in a mixture with TM. The conductive TM composite ink, referred to as BPEC ink in this study, is optimised through multi-objective Bayesian optimisation (MOBO) with the two objectives of maximising current density and maximising printability. Fourteen initial searches were taken, followed by 15 MOBO searches. We confirm that after MOBO, current density and printability were enhanced by 162% and 149%, respectively. Using the optimised BPEC ink, we demonstrate the 3D printing of fully integrated BPEC cells arranged in series. |
| format | Article |
| id | doaj-art-23c3efdca56847019379e8f28f85ef00 |
| institution | Kabale University |
| issn | 1745-2759 1745-2767 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Virtual and Physical Prototyping |
| spelling | doaj-art-23c3efdca56847019379e8f28f85ef002025-01-21T04:21:46ZengTaylor & Francis GroupVirtual and Physical Prototyping1745-27591745-27672025-12-0120110.1080/17452759.2024.2449565Enhancing the performance of 3D printed bio-photoelectrochemical cells through multi-objective Bayesian optimisationJongHyun Kim0JaeHyoung Yun1Mirkomil Sharipov2Jinwook Moon3WonHyoung Ryu4School of Mechanical Engineering, Yonsei University, Seoul, Republic of KoreaSchool of Mechanical Engineering, Yonsei University, Seoul, Republic of KoreaSchool of Mechanical Engineering, Yonsei University, Seoul, Republic of KoreaSchool of Mechanical Engineering, Yonsei University, Seoul, Republic of KoreaSchool of Mechanical Engineering, Yonsei University, Seoul, Republic of KoreaAttaching thylakoid membranes (TM) on bio-photo-electrochemical cells (BPEC) enables energy harvesting through photoelectrode extraction. However, the attachment methods rely on thin coating methods such as dip-coating, drop-casting or electrospray deposition. We herein demonstrate the use of direct ink writing in coating TM on BPEC cells, aiming for rapid prototyping and mass production of BPEC cells. As photoelectron extraction through high TM loadings is not feasible, we investigate previously reported conducting materials to be used in a mixture with TM. The conductive TM composite ink, referred to as BPEC ink in this study, is optimised through multi-objective Bayesian optimisation (MOBO) with the two objectives of maximising current density and maximising printability. Fourteen initial searches were taken, followed by 15 MOBO searches. We confirm that after MOBO, current density and printability were enhanced by 162% and 149%, respectively. Using the optimised BPEC ink, we demonstrate the 3D printing of fully integrated BPEC cells arranged in series.https://www.tandfonline.com/doi/10.1080/17452759.2024.2449565Photosynthetic electrons3D printingmulti-objective Bayesian optimisationenergy conversion device |
| spellingShingle | JongHyun Kim JaeHyoung Yun Mirkomil Sharipov Jinwook Moon WonHyoung Ryu Enhancing the performance of 3D printed bio-photoelectrochemical cells through multi-objective Bayesian optimisation Virtual and Physical Prototyping Photosynthetic electrons 3D printing multi-objective Bayesian optimisation energy conversion device |
| title | Enhancing the performance of 3D printed bio-photoelectrochemical cells through multi-objective Bayesian optimisation |
| title_full | Enhancing the performance of 3D printed bio-photoelectrochemical cells through multi-objective Bayesian optimisation |
| title_fullStr | Enhancing the performance of 3D printed bio-photoelectrochemical cells through multi-objective Bayesian optimisation |
| title_full_unstemmed | Enhancing the performance of 3D printed bio-photoelectrochemical cells through multi-objective Bayesian optimisation |
| title_short | Enhancing the performance of 3D printed bio-photoelectrochemical cells through multi-objective Bayesian optimisation |
| title_sort | enhancing the performance of 3d printed bio photoelectrochemical cells through multi objective bayesian optimisation |
| topic | Photosynthetic electrons 3D printing multi-objective Bayesian optimisation energy conversion device |
| url | https://www.tandfonline.com/doi/10.1080/17452759.2024.2449565 |
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