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Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG 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. MCIN/AEI/10.13039/501100011033 | Ref. PID2019-108954RB-I00 MCIN/AEI/10.13039/501100011033 | Ref. PID2019-106960GB-I00 MCIN/AEI/10.13039/501100011033 | Ref. BES-2017-08167 MEC/AEI | Ref. CTQ2017-84354-P Xunta de Galicia | Ref. GRC ED431C 2020/09 Xunta de Galicia | Ref. GR 2007/085 |
