Surface Filtration in Mesoporous Au Films Decorated by Ag Nanoparticles for Solving SERS Sensing Small Molecules in Living Cells

Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams

Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.

Ag-Au-Cu Trimetallic Alloy Microflower: A Highly Sensitive SERS Substrate for Detection of Low Raman Scattering Cross-Section Thiols

Trimetallic Ag-Au-Cu alloy microflowers (MFs) with various surface compositions were synthesized on a glass coverslip and used as efficient surface-enhanced Raman spectroscopy (SERS) substrates for highly sensitive label-free detection of smaller Raman scattering cross-section molecules, namely, L-cysteine and toxic thiophenols. MFs of different compositions were synthesized via appropriate mixing of metal-alkyl ammonium halide precursors followed by a single-step thermolysis at 350 °C. While the Ag percentage was kept constant at 90% for all the substrates, the composition of Au and Cu was varied between 1 and 9% sequentially. The synthesized MFs were thoroughly characterized by using field emission scanning electron microscopy (FE-SEM), wide-angle X-ray scattering, X-ray photoelectron spectroscopy (XPS), and X-ray fluorescence techniques. FE-SEM studies revealed that the MFs were present throughout the substrate, and the average size varied from 20 to 40 μm. XPS studies showed that the top surface of the alloy substrates was rich in either Au or Cu atoms, while Ag remained underneath. The performance of the trimetallic MFs as SERS substrates was evaluated using Rhodamine 6G as a probe molecule, which showed that the MFs with Ag-Au-Cu compositions 90-7-3 and 90-3-7 were found to be the best and of equal SERS efficiency. The SERS enhancement factor (EF) of both these MFs was found to be the same, approximately 9 × 107, when calculated using 1,2,3-benzatriazole as the probe molecule. Between the two, the trimetallic substrate with a higher Au percentage (Ag-Au-Cu as 90-7-3) was used for the sensitive SERS-based detection of thiols to exploit the strong Au-S binding interaction. By virtue of the high EF of the substrate, the inherently low Raman scattering cross-sections of the probe molecules were greatly enhanced in SERS mode. The 'limit of quantification (LOQ)' values were found to be 1 nM for aliphatic L-Cysteine and 1-0.1 pM for aromatic thiols using the trimetallic SERS sensor.

Application of SERS in In-Vitro Biomedical Detection

Surface-enhanced Raman scattering (SERS), as a rapid and nondestructive biological detection method, holds great promise for clinical on spot and early diagnosis. In order to address the challenging demands of on spot detection of biomedical samples, a variety of strategies has been developed. These strategies include substrate structural and component engineering, data processing techniques, as well as combination with other analytical methods. This report summarizes the recent SERS developments for biomedical detection, and their promising applications in cancer detection, virus or bacterial infection detection, miscarriage spotting, neurological disease screening et al. The first part discusses the frequently used SERS substrate component and structures, the second part reports on the detection strategies for nucleic acids, proteins, bacteria, and virus, the third part summarizes their promising applications in clinical detection in a variety of illnesses, and the forth part reports on recent development of SERS in combination with other analytical techniques. The special merits, challenges, and perspectives are discussed in both introduction and conclusion sections.