{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/389999"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/389999","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Streamlining the development of biophotoelectrodes: from rational design to rapid screening","abstract":"Photosynthetic microorganisms provide an abundant source of scalable and sustainable biocatalysts. Interfacing these microorganisms with a biophotoelectrode has emerged as a promising platform to harness solar energy for power generation or chemical production. Nonetheless, the development of biophotoelectrodes has been a significant challenge, limiting the performance of such platforms under theoretical estimates. This challenge stems from the inherent difficulty of establishing structure-activity relationships within their three-dimensional (3D) hierarchical architectures, as well as the lack of efficient methods for prototyping and identifying the best-performing biophotoelectrode. This research aimed to develop high-performing biophotoelectrodes through streamlined design, fabrication and screening processes. To achieve these objectives, biophotoelectrodes have been developed through both rational design (i.e., relying on detailed knowledge of structure-activity relationship to fabricate targeted designs) and semi-rational design approaches (i.e., adding empirical insights to generate random designs that could capture unforeseen benefits). Firstly, in the initial stage of employing a rational design approach, the pore sizes of 3D porous electrodes were systematically adjusted using a Monte-Carlo optical simulation. Electrodes optimised for light irradiation showed the highest current output when normalised by biocatalyst loading, but their overall output was limited by lower biocatalyst loading. Conversely, electrodes with the highest overall output performed poorly when normalised. This suggested the need to balance both properties for optimal performance. Despite the successful validation of such designs through simulations, the fabrication process remained time-consuming and labour-intensive, restricting the exploration of electrode structures. Moreover, optimising one property risks overlooking other critical effects. Subsequently, to enable the transition to a theoretically more flexible and efficient semi-rational approach, the 3D printing method, aerosol jet printing, was employed to rapidly prototype diverse electrode structures, such as pillar, cone, arborescent and honeycomb shapes. This method significantly accelerated the fabrication process and expanded the range of design parameters. By allowing rapid prototyping of diverse electrode libraries, it established the groundwork for subsequent stages of screening and optimisation. Finally, a high-throughput screening platform for biophotoelectrochemical measurements was developed in two stages, allowing the simultaneous evaluation of 4 electrodes and, later, 24 electrodes. Concurrently, 4 electrode libraries, each consisting of 24 electrodes, were iteratively designed using directed evolution principles. The integration of a machine learning algorithm enabled the exploration of otherwise inaccessible electrode designs. However, optimisation of the screening platform during the screening process indicated the need to repeat the workflow with a more consistent methodology across libraries. This study presented the development of a streamlined process for developing biophotoelectrodes, integrating rational design, rapid fabrication, directed evolution, high-throughput screening and machine learning optimisation. These advancements provide a strong foundation for future progress. The knowledge gained from this research is anticipated to inform future developments in the evolution of electrode design.","abstract_html":"Photosynthetic microorganisms provide an abundant source of scalable and sustainable biocatalysts. Interfacing these microorganisms with a biophotoelectrode has emerged as a promising platform to harness solar energy for power generation or chemical production. Nonetheless, the development of biophotoelectrodes has been a significant challenge, limiting the performance of such platforms under theoretical estimates. This challenge stems from the inherent difficulty of establishing structure-activity relationships within their three-dimensional (3D) hierarchical architectures, as well as the lack of efficient methods for prototyping and identifying the best-performing biophotoelectrode. This research aimed to develop high-performing biophotoelectrodes through streamlined design, fabrication and screening processes. To achieve these objectives, biophotoelectrodes have been developed through both rational design (i.e., relying on detailed knowledge of structure-activity relationship to fabricate targeted designs) and semi-rational design approaches (i.e., adding empirical insights to generate random designs that could capture unforeseen benefits). Firstly, in the initial stage of employing a rational design approach, the pore sizes of 3D porous electrodes were systematically adjusted using a Monte-Carlo optical simulation. Electrodes optimised for light irradiation showed the highest current output when normalised by biocatalyst loading, but their overall output was limited by lower biocatalyst loading. Conversely, electrodes with the highest overall output performed poorly when normalised. This suggested the need to balance both properties for optimal performance. Despite the successful validation of such designs through simulations, the fabrication process remained time-consuming and labour-intensive, restricting the exploration of electrode structures. Moreover, optimising one property risks overlooking other critical effects. Subsequently, to enable the transition to a theoretically more flexible and efficient semi-rational approach, the 3D printing method, aerosol jet printing, was employed to rapidly prototype diverse electrode structures, such as pillar, cone, arborescent and honeycomb shapes. This method significantly accelerated the fabrication process and expanded the range of design parameters. By allowing rapid prototyping of diverse electrode libraries, it established the groundwork for subsequent stages of screening and optimisation. Finally, a high-throughput screening platform for biophotoelectrochemical measurements was developed in two stages, allowing the simultaneous evaluation of 4 electrodes and, later, 24 electrodes. Concurrently, 4 electrode libraries, each consisting of 24 electrodes, were iteratively designed using directed evolution principles. The integration of a machine learning algorithm enabled the exploration of otherwise inaccessible electrode designs. However, optimisation of the screening platform during the screening process indicated the need to repeat the workflow with a more consistent methodology across libraries. This study presented the development of a streamlined process for developing biophotoelectrodes, integrating rational design, rapid fabrication, directed evolution, high-throughput screening and machine learning optimisation. These advancements provide a strong foundation for future progress. The knowledge gained from this research is anticipated to inform future developments in the evolution of electrode design.","abstract_has_math":false,"creators":["Shang, Linying"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Zhang, Jenny"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-10-04","date_published":"2024-10-04","updated_at":"2026-07-22T22:24:07Z","subjects":["biophotoelectrochemical systems","biophotoelectrodes","directed evolution","high-throughput screening","photosynthetic microorganisms","structure-activity relationship","three-dimensional electrodes","three-dimensional printing"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/32e91b8c-f17f-4924-91f8-27062931a55c/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.121702","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zhang, Jenny"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["China Scholarship Council Cambridge Commonwealth, European & International Trust Henry Royce Institute"]},{"key":"dc:creator","label":"Author","values":["Shang, Linying"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024-10-04"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/389999"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["biophotoelectrochemical systems","biophotoelectrodes","directed evolution","high-throughput screening","photosynthetic microorganisms","structure-activity relationship","three-dimensional electrodes","three-dimensional printing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/32e91b8c-f17f-4924-91f8-27062931a55c/download","http://purl.org/NET/rdflicense/allrightsreserved"]},{"key":"dc:rights.embargodate","label":"Dc Rights Embargodate","values":["2026-09-29"]},{"key":"dc:rights.embargotype","label":"Dc Rights Embargotype","values":["embargo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.121702"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/aeaa2820-c088-4c47-b792-df3ef1fc92e7/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Photosynthetic microorganisms provide an abundant source of scalable and sustainable biocatalysts. 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To achieve these objectives, biophotoelectrodes have been developed through both rational design (i.e., relying on detailed knowledge of structure-activity relationship to fabricate targeted designs) and semi-rational design approaches (i.e., adding empirical insights to generate random designs that could capture unforeseen benefits). Firstly, in the initial stage of employing a rational design approach, the pore sizes of 3D porous electrodes were systematically adjusted using a Monte-Carlo optical simulation. Electrodes optimised for light irradiation showed the highest current output when normalised by biocatalyst loading, but their overall output was limited by lower biocatalyst loading. Conversely, electrodes with the highest overall output performed poorly when normalised. This suggested the need to balance both properties for optimal performance. Despite the successful validation of such designs through simulations, the fabrication process remained time-consuming and labour-intensive, restricting the exploration of electrode structures. Moreover, optimising one property risks overlooking other critical effects. Subsequently, to enable the transition to a theoretically more flexible and efficient semi-rational approach, the 3D printing method, aerosol jet printing, was employed to rapidly prototype diverse electrode structures, such as pillar, cone, arborescent and honeycomb shapes. This method significantly accelerated the fabrication process and expanded the range of design parameters. By allowing rapid prototyping of diverse electrode libraries, it established the groundwork for subsequent stages of screening and optimisation. Finally, a high-throughput screening platform for biophotoelectrochemical measurements was developed in two stages, allowing the simultaneous evaluation of 4 electrodes and, later, 24 electrodes. 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Firstly, in the initial stage of employing a rational design approach, the pore sizes of 3D porous electrodes were systematically adjusted using a Monte-Carlo optical simulation. Electrodes optimised for light irradiation showed the highest current output when normalised by biocatalyst loading, but their overall output was limited by lower biocatalyst loading. Conversely, electrodes with the highest overall output performed poorly when normalised. This suggested the need to balance both properties for optimal performance. Despite the successful validation of such designs through simulations, the fabrication process remained time-consuming and labour-intensive, restricting the exploration of electrode structures. Moreover, optimising one property risks overlooking other critical effects. Subsequently, to enable the transition to a theoretically more flexible and efficient semi-rational approach, the 3D printing method, aerosol jet printing, was employed to rapidly prototype diverse electrode structures, such as pillar, cone, arborescent and honeycomb shapes. This method significantly accelerated the fabrication process and expanded the range of design parameters. By allowing rapid prototyping of diverse electrode libraries, it established the groundwork for subsequent stages of screening and optimisation. Finally, a high-throughput screening platform for biophotoelectrochemical measurements was developed in two stages, allowing the simultaneous evaluation of 4 electrodes and, later, 24 electrodes. Concurrently, 4 electrode libraries, each consisting of 24 electrodes, were iteratively designed using directed evolution principles. The integration of a machine learning algorithm enabled the exploration of otherwise inaccessible electrode designs. 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