Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset
<div><p>Nowadays, most experiments to synthesize and test photocatalytic antimicrobial materials are based on trial and error. More often than not, the mechanism of action of the antimicrobial activity is unknown for a large spectrum of microorganisms. Here, we propose a scheme to speed...
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2021
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| _version_ | 1864513516716687360 |
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| author | Heesoo Park (1604989) |
| author2 | El Tayeb Bentria (9904024) Sami Rtimi (5043176) Abdelilah Arredouani (10914455) Halima Bensmail (10400) Fedwa El-Mellouhi (2011099) |
| author2_role | author author author author author |
| author_facet | Heesoo Park (1604989) El Tayeb Bentria (9904024) Sami Rtimi (5043176) Abdelilah Arredouani (10914455) Halima Bensmail (10400) Fedwa El-Mellouhi (2011099) |
| author_role | author |
| dc.creator.none.fl_str_mv | Heesoo Park (1604989) El Tayeb Bentria (9904024) Sami Rtimi (5043176) Abdelilah Arredouani (10914455) Halima Bensmail (10400) Fedwa El-Mellouhi (2011099) |
| dc.date.none.fl_str_mv | 2021-08-20T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/catal11081001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Accelerating_the_Design_of_Photocatalytic_Surfaces_for_Antimicrobial_Application_Machine_Learning_Based_on_a_Sparse_Dataset/25764867 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Materials engineering photocatalytic systems reactive oxygen species illumination predictive activity machine learning |
| dc.title.none.fl_str_mv | Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Nowadays, most experiments to synthesize and test photocatalytic antimicrobial materials are based on trial and error. More often than not, the mechanism of action of the antimicrobial activity is unknown for a large spectrum of microorganisms. Here, we propose a scheme to speed up the design and optimization of photocatalytic antimicrobial surfaces tailored to give a balanced production of reactive oxygen species (ROS) upon illumination. Using an experiment-to-machine-learning scheme applied to a limited experimental dataset, we built a model that can predict the photocatalytic activity of materials for antimicrobial applications over a wide range of material compositions. This machine-learning-assisted strategy offers the opportunity to reduce the cost, labor, time, and precursors consumed during experiments that are based on trial and error. Our strategy may significantly accelerate the large-scale deployment of photocatalysts as a promising route to mitigate fomite transmission of pathogens (bacteria, viruses, fungi) in hospital settings and public places.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Catalysts<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/catal11081001" target="_blank">https://dx.doi.org/10.3390/catal11081001</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_893185deb6afe3dbed5ad7b29d4149dc |
| identifier_str_mv | 10.3390/catal11081001 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25764867 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse DatasetHeesoo Park (1604989)El Tayeb Bentria (9904024)Sami Rtimi (5043176)Abdelilah Arredouani (10914455)Halima Bensmail (10400)Fedwa El-Mellouhi (2011099)EngineeringMaterials engineeringphotocatalytic systemsreactive oxygen speciesilluminationpredictive activitymachine learning<div><p>Nowadays, most experiments to synthesize and test photocatalytic antimicrobial materials are based on trial and error. More often than not, the mechanism of action of the antimicrobial activity is unknown for a large spectrum of microorganisms. Here, we propose a scheme to speed up the design and optimization of photocatalytic antimicrobial surfaces tailored to give a balanced production of reactive oxygen species (ROS) upon illumination. Using an experiment-to-machine-learning scheme applied to a limited experimental dataset, we built a model that can predict the photocatalytic activity of materials for antimicrobial applications over a wide range of material compositions. This machine-learning-assisted strategy offers the opportunity to reduce the cost, labor, time, and precursors consumed during experiments that are based on trial and error. Our strategy may significantly accelerate the large-scale deployment of photocatalysts as a promising route to mitigate fomite transmission of pathogens (bacteria, viruses, fungi) in hospital settings and public places.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Catalysts<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/catal11081001" target="_blank">https://dx.doi.org/10.3390/catal11081001</a></p>2021-08-20T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/catal11081001https://figshare.com/articles/journal_contribution/Accelerating_the_Design_of_Photocatalytic_Surfaces_for_Antimicrobial_Application_Machine_Learning_Based_on_a_Sparse_Dataset/25764867CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/257648672021-08-20T03:00:00Z |
| spellingShingle | Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset Heesoo Park (1604989) Engineering Materials engineering photocatalytic systems reactive oxygen species illumination predictive activity machine learning |
| status_str | publishedVersion |
| title | Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset |
| title_full | Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset |
| title_fullStr | Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset |
| title_full_unstemmed | Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset |
| title_short | Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset |
| title_sort | Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset |
| topic | Engineering Materials engineering photocatalytic systems reactive oxygen species illumination predictive activity machine learning |