Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation
Gold nanorods (GNRs) coated with silica shells are excellent photothermal agents with high surface functionality and biocompatibility. Understanding the correlation of the coating process with both structure and property of silica-coated GNRs is crucial to their optimizing preparation and performanc...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/13/6/1024 |
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| author | Jintao Zhang Jinchang Yin Ruiran Lai Yue Wang Baorui Mao Haonan Wu Li Tian Yuanzhi Shao |
| author_facet | Jintao Zhang Jinchang Yin Ruiran Lai Yue Wang Baorui Mao Haonan Wu Li Tian Yuanzhi Shao |
| author_sort | Jintao Zhang |
| collection | DOAJ |
| description | Gold nanorods (GNRs) coated with silica shells are excellent photothermal agents with high surface functionality and biocompatibility. Understanding the correlation of the coating process with both structure and property of silica-coated GNRs is crucial to their optimizing preparation and performance, as well as tailoring potential applications. Herein, we report a machine learning (ML) prediction of coating silica on GNR with various preparation parameters. A total of 306 sets of silica-coated GNRs altogether were prepared via a sol–gel method, and their structures were characterized to extract a dataset available for eight ML algorithms. Among these algorithms, the eXtreme gradient boosting (XGboost) classification model affords the highest prediction accuracy of over 91%. The derived feature importance scores and relevant decision trees are employed to address the optimal process to prepare well-structured silica-coated GNRs. The high-throughput predictions have been adopted to identify optimal process parameters for the successful preparation of dumbbell-structured silica-coated GNRs, which possess a superior performance to a conventional cylindrical core–shell counterpart. The dumbbell silica-coated GNRs demonstrate an efficient enhanced photothermal performance in vivo and in vitro, validated by both experiments and time domain finite difference calculations. This study epitomizes the potential of ML algorithms combined with experiments in predicting, optimizing, and accelerating the preparation of core–shell inorganic materials and can be extended to other nanomaterial research. |
| first_indexed | 2024-03-11T06:05:01Z |
| format | Article |
| id | doaj.art-481e42ee167d4c41ab792ef92e802691 |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-11T06:05:01Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-481e42ee167d4c41ab792ef92e8026912023-11-17T13:00:27ZengMDPI AGNanomaterials2079-49912023-03-01136102410.3390/nano13061024Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor AblationJintao Zhang0Jinchang Yin1Ruiran Lai2Yue Wang3Baorui Mao4Haonan Wu5Li Tian6Yuanzhi Shao7State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaState Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaState Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaState Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaState Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaState Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, ChinaState Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaGold nanorods (GNRs) coated with silica shells are excellent photothermal agents with high surface functionality and biocompatibility. Understanding the correlation of the coating process with both structure and property of silica-coated GNRs is crucial to their optimizing preparation and performance, as well as tailoring potential applications. Herein, we report a machine learning (ML) prediction of coating silica on GNR with various preparation parameters. A total of 306 sets of silica-coated GNRs altogether were prepared via a sol–gel method, and their structures were characterized to extract a dataset available for eight ML algorithms. Among these algorithms, the eXtreme gradient boosting (XGboost) classification model affords the highest prediction accuracy of over 91%. The derived feature importance scores and relevant decision trees are employed to address the optimal process to prepare well-structured silica-coated GNRs. The high-throughput predictions have been adopted to identify optimal process parameters for the successful preparation of dumbbell-structured silica-coated GNRs, which possess a superior performance to a conventional cylindrical core–shell counterpart. The dumbbell silica-coated GNRs demonstrate an efficient enhanced photothermal performance in vivo and in vitro, validated by both experiments and time domain finite difference calculations. This study epitomizes the potential of ML algorithms combined with experiments in predicting, optimizing, and accelerating the preparation of core–shell inorganic materials and can be extended to other nanomaterial research.https://www.mdpi.com/2079-4991/13/6/1024silica-coated gold nanorodsmachine learningprocess optimizationhigh-throughput predictionphotothermal tumor ablation |
| spellingShingle | Jintao Zhang Jinchang Yin Ruiran Lai Yue Wang Baorui Mao Haonan Wu Li Tian Yuanzhi Shao Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation Nanomaterials silica-coated gold nanorods machine learning process optimization high-throughput prediction photothermal tumor ablation |
| title | Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation |
| title_full | Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation |
| title_fullStr | Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation |
| title_full_unstemmed | Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation |
| title_short | Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation |
| title_sort | machine learning predicting optimal preparation of silica coated gold nanorods for photothermal tumor ablation |
| topic | silica-coated gold nanorods machine learning process optimization high-throughput prediction photothermal tumor ablation |
| url | https://www.mdpi.com/2079-4991/13/6/1024 |
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