Bolaform Surfactant‐Induced Au Nanoparticle Assemblies for Reliable Solution‐Based Surface‐Enhanced Raman Scattering Detection. Issue 10 (3rd April 2022)

• Record Type: Journal Article
• Authors: García‐Lojo, Daniel; Méndez‐Merino, David; Pérez‐Juste, Ignacio; Acuña, Ángel; García‐Río, Luís; Rodríguez‐Patón, Alfonso; Pastoriza‐Santos, Isabel; Pérez‐Juste, Jorge
• Is Part Of: Advanced materials technologies. Volume 7:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-03
• Subjects: SERS -- plasmonic nanoparticles -- Au nanoparticles assembly -- millifluidics -- drug sensing -- deep learning -- ResNet; Materials science -- Periodicals; Technological innovations -- Periodicals; Materials science; Technological innovations; Periodicals; 620.1105
• DOI: 10.1002/admt.202101726
• Languages: English
• ISSNs: 2365-709X

Abstract

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. Abstract : Bolaform surfactant‐induced Au nanoparticles assemblies are investigated for portable surface‐enhanced Raman scattering analysis of aqueous‐based solutions. This approach in combination with deep learning techniques shows good versatility for multiplexing detection. The integration of this reliable methodology into a millifluidic chip allows fabricating a high‐performance sensing platform.