Publicação
L-Dopa functionalized gold nanourchins as the next-generation nanosensors for the detection of food contaminants
| Resumo: | In recent years, innovative materials have advanced in food safety, facilitating the rapid and accurate detection of additives and contaminants in food products. Gold nanoparticles (AuNPs) are key in this field, notably for their use as nanosensors, e.g., utilizing surface-enhanced Raman spectroscopy (SERS). Although several AuNP morphologies have been investigated for the development of these sensors, anisotropic nanostructures such as gold nanostars (AuNSs) and nanourchins (AuNUs) are among the most promising. These morphologies outperform isotropic NPs due to their branched structure and "hot points" with strong electromagnetic fields. However, it is crucial to develop eco-friendly methods for precise control over size and morphology in obtaining these Au anisotropic nanostructures while demonstrating that they are safe and effective in detecting various contaminants. In this study, we synthesized for the first time Au nanostructures, viz. nanourchins, using L-Dopa (levodopa) extract from Mucuna pruriens, and assessed their capability to detect food contaminants. We investigated the effect of several reaction parameters, namely concentrations of polyethylene glycol, gold seeds, gold salt, and L-Dopa extract, to control AuNPs morphology and size. It was found that polyethylene glycol and gold salt did not affect the morphology and size, while seed concentration significantly impacted the size of the AuNPs and the resulting morphology. Therefore, a response surface methodology (RSM) was applied using the parameters that had the most significant impact on the size and shape of the AuNPs. Our study revealed that the concentration of the gold seed emerged as a pivotal factor in determining the size of the AuNPs. In contrast, although the L-Dopa extract concentration played a relatively smaller role in size control, it demonstrated a more pronounced influence on the ultimate morphology of the AuNPs. More specifically, a low concentration of gold seeds and a high concentration of L-Dopa resulted in larger AuNUs ( ͌ 100 nm).In comparison, a high concentration of gold seeds and a low concentration of L-Dopa extract led to small-sized spherical shapes ( ͌ 32 nm). Finally, we evaluated three colloidal suspensions of AuNPs, each varying in size and the presence of protrusions (one small with a few protrusions, another small with protrusions, and a large one with protrusions), for their capability to detect specific food contaminants. All samples successfully detected methylene blue, a toxic food additive, with a detection limit of 10ˉ⁵ M. Generally, larger nanoparticles with more protrusions exhibited stronger SERS signals. These results underscore the significant potential of these AuNPs in creating efficient and selective biobased nanosensors for monitoring food safety. |
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| Autores principais: | Justi, Marina Setubal |
| Assunto: | Mucuna pruriens Plant extracts Levodopa Gold nanoparticles Gold nanourchins Nanosensors Food contaminants |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| Resumo: | In recent years, innovative materials have advanced in food safety, facilitating the rapid and accurate detection of additives and contaminants in food products. Gold nanoparticles (AuNPs) are key in this field, notably for their use as nanosensors, e.g., utilizing surface-enhanced Raman spectroscopy (SERS). Although several AuNP morphologies have been investigated for the development of these sensors, anisotropic nanostructures such as gold nanostars (AuNSs) and nanourchins (AuNUs) are among the most promising. These morphologies outperform isotropic NPs due to their branched structure and "hot points" with strong electromagnetic fields. However, it is crucial to develop eco-friendly methods for precise control over size and morphology in obtaining these Au anisotropic nanostructures while demonstrating that they are safe and effective in detecting various contaminants. In this study, we synthesized for the first time Au nanostructures, viz. nanourchins, using L-Dopa (levodopa) extract from Mucuna pruriens, and assessed their capability to detect food contaminants. We investigated the effect of several reaction parameters, namely concentrations of polyethylene glycol, gold seeds, gold salt, and L-Dopa extract, to control AuNPs morphology and size. It was found that polyethylene glycol and gold salt did not affect the morphology and size, while seed concentration significantly impacted the size of the AuNPs and the resulting morphology. Therefore, a response surface methodology (RSM) was applied using the parameters that had the most significant impact on the size and shape of the AuNPs. Our study revealed that the concentration of the gold seed emerged as a pivotal factor in determining the size of the AuNPs. In contrast, although the L-Dopa extract concentration played a relatively smaller role in size control, it demonstrated a more pronounced influence on the ultimate morphology of the AuNPs. More specifically, a low concentration of gold seeds and a high concentration of L-Dopa resulted in larger AuNUs ( ͌ 100 nm).In comparison, a high concentration of gold seeds and a low concentration of L-Dopa extract led to small-sized spherical shapes ( ͌ 32 nm). Finally, we evaluated three colloidal suspensions of AuNPs, each varying in size and the presence of protrusions (one small with a few protrusions, another small with protrusions, and a large one with protrusions), for their capability to detect specific food contaminants. All samples successfully detected methylene blue, a toxic food additive, with a detection limit of 10ˉ⁵ M. Generally, larger nanoparticles with more protrusions exhibited stronger SERS signals. These results underscore the significant potential of these AuNPs in creating efficient and selective biobased nanosensors for monitoring food safety. |
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