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Optimising Nanoparticle Synthesis with Machine Learning and Genetic Algorithms: A Study on Crystallite Size and Luminescence
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| 1.69 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
"Lanthanide-doped nanoparticles (NPs) have shown great potential in biomedical applications, particu-
larly in imaging and targeted therapies, due to their unique photoluminescence (PL) properties (2,3).
Recent advancements in machine learning (ML) offer new opportunities for optimising NP synthesis.
ML enables algorithms to learn from data, potentially accelerating discoveries, but significant challenges
remain (4–6). Additionally, genetic algorithms (GAs), inspired by natural selection, can efficiently search
through complex parameter spaces, making them suitable for optimising synthesis conditions (7–9).
This study explores the potential of artificial intelligence (AI), specifically combining ML and GAs, to
model and optimise NaGdF4:Eu NP synthesis conditions, aiming to maximise crystallite sizes while
maintaining a small particle size. Increasing crystallite size has been shown to enhance PL intensity in
various doped NPs. This approach can accelerate biomedical advancements efficiently in terms of both
time and resources. Our approach included feature selection, standardisation, and one-hot encoding to
build robust models.(...)"
Descrição
Palavras-chave
Nanoparticles, Genetic Algorithms Crystallisation Machine Learning Optimisation
Contexto Educativo
Citação
Editora
Instituto de Tecnologia Química e Biológica António Xavier. Universidade NOVA de Lisboa