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Dataset . 2022
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Dataset . 2022
License: CC BY
Data sources: Sygma
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2022
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Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection

Authors: Daniel García-Lojo; David Méndez-Merino; Ignacio Pérez-Juste; Ángel Acuña; Luís García-Río; Alfonso Rodríguez-Patón; Isabel Pastoriza-Santos; +1 Authors

Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection

Abstract

Related publication: García-Lojo, D; Méndez-Merino, D; Pérez-Juste, I; Acuña, A; García-Río, L; Rodríguez-Patón, A; Pastoriza-Santos, I; Pérez-Juste, J. Bolaform surfactant-induced Au nanoparticle assemblies for reliable solution-based SERS detection. Adv.Mater. Technol. 2022, 2101726. https://doi.org/10.1002/admt.202101726 Abstract: Solution-based surface-enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate-stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting-out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B20, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B20, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B20 by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on-site detection and quantification of beta-blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state-of-the-art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.

Keywords

drug sensing, SERS, millifluidics, deep learning, Plasmonic nanoparticles, Au nanoparticles assembly, ResNet

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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