Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles
| Autor: | Vasile-Dan Hodoroaba, Valter Maurino, Raluca Isopescu, Letizia Pellutiè, Francesco Pellegrino, Erik Ortel, Fabrizio Sordello, Andrea Mario Rossi, Gianmario Martra |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Reverse engineering
Work (thermodynamics) Aspect ratio Computer science Nanoparticle Inverse lcsh:Medicine 02 engineering and technology morphology controlled nanoparticles 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Outcome (game theory) Article Machine Learning Machine Learning TiO2 nanoparticles morphology controlled nanoparticles lcsh:Science Multidisciplinary Nanoscale materials Series (mathematics) business.industry Synthesis and processing lcsh:R Process (computing) 021001 nanoscience & nanotechnology 0104 chemical sciences lcsh:Q Artificial intelligence 0210 nano-technology business computer TiO2 nanoparticles |
| Zdroj: | Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | In the present work a series of design rules are developed in order to tune the morphology of TiO2 nanoparticles through hydrothermal process. Through a careful experimental design, the influence of relevant process parameters on the synthesis outcome are studied, reaching to the develop predictive models by using Machine Learning methods. The models, after the validation and training, are able to predict with high accuracy the synthesis outcome in terms of nanoparticle size, polydispersity and aspect ratio. Furthermore, they are implemented by reverse engineering approach to do the inverse process, i.e. obtain the optimal synthesis parameters given a specific product characteristic. For the first time, it is presented a synthesis method that allows continuous and precise control of NPs morphology with the possibility to tune the aspect ratio over a large range from 1.4 (perfect truncated bipyramids) to 6 (elongated nanoparticles) and the length from 20 to 140 nm. |
| Databáze: | OpenAIRE |
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