SERS Monitored Kinetic Process of Gaseous Thiophenol Compound in Plasmonic MOF Nanoparticles

Quantitative detection of microcystin-LR in Bellamya aeruginosa by thin-layer chromatography coupled with surface-enhanced Raman spectroscopy based on in-situ ZIF-67/Ag NPs/Au NWs composite substrate

As a hypertoxic natural toxin, the risk of Microcystin-leucine-arginine (MC-LR) residues in Bellamya aeruginosa deserves more attention. Herein, employing the conventional thin-layer chromatography (TLC) technology and a novel surface-enhanced Raman scattering (SERS) substrate, a TLC-SERS chip was fabricated for the purification and quantitative detection of MC-LR in complex samples. The substrate exhibited excellent SERS performance with an enhancement factor of 6.6 × 107, a low detection limit of 2.27 × 10-9 mM for MC-LR, excellent uniformity and reproducibility, as well as a wide linear range. With the application of TLC, the MC-LR was efficiently purified and the concentration was increased to >3 times. Ultimately, recovery rates fluctuated between 93.28% and 101.66% were obtained from the TLC-SERS chip. On balance, the TLC-SERS chip has a robust capacity for achieving rapid and stable quantitative detection of MC-LR, which promises to improve the efficiency of food safety monitoring.

Hierarchical PVDF/ZnO/Ag/ZIF-8 nanofiber membrane used in trace-level Raman detection of H2S

surface enhanced Raman scattering (SERS) detection of gases has always been difficult due to the low affinity and poor Raman cross section of the moving molecules. To mitigate the impact of these problems on detection of gases, a structure of zinc oxide/silver nanowires coated with zeolitic imidazolate framework-8 (ZnO NWs/Ag/ZIF-8) was constructed on polyvinylidene fluoride (PVDF) nanofiber membrane (PVDF/ZnO NWs/Ag/ZIF-8) and in detail researched in this work. Benefitting from the quadruple synergistic effect of efficient Knudsen diffusion of gas molecules inside ZIF-8, enrichment of ZIF-8 microsponges for gaseous molecules, regulation of ZIF-8 dielectric layer for light and reverse light scattering of ZnO NW/Ag tip, the structure was proven to have precise co-confinement on both hot spots and gaseous molecules. As a result, this PVDF/ZnO NWs/Ag/ZIF-8 achieved excellent detection for hydrogen sulfide (H2S), with a limit of detection of 1 × 10-10 v/v and the minimum relative standard deviation value of ca. 7.13 %. Furthermore, as a proof of concept, in practical application, we designed and assembled our substrate (3.5 cm × 3.5 cm) into a SERS face mask and realized efficient monitoring of H2S in human's exhaled breath.

SERS-based AI diagnosis of lung and gastric cancer via exhaled breath

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

Wearable intelligent sweat platform for SERS-AI diagnosis of gout

For the past few years, sweat analysis for health monitoring has attracted increasing attention benefiting from wearable technology. In related research, the sensitive detection of uric acid (UA) in sweat with complex composition based on surface-enhanced Raman spectroscopy (SERS) for the diagnosis of gout is still a significant challenge. Herein, we report a visualized and intelligent wearable sweat platform for SERS detection of UA in sweat. In this wearable platform, the spiral channel consisted of colorimetric paper with Ag nanowires (AgNWs) that could capture sweat for SERS measurement. With the help of photos from a smartphone, the pH value and volume of sweat could be quantified intelligently based on the image recognition technique. To diagnose gout, SERS spectra of human sweat with UA are collected in this wearable intelligent platform and analyzed by artificial intelligence (AI) algorithms. The results indicate that the artificial neural network (ANN) algorithm exhibits good identification of gout with high accuracy at 97%. Our work demonstrates that SERS-AI in a wearable intelligent sweat platform could be a feasible strategy for diagnosis of gout, which expands research on sweat analysis for comfortable and noninvasive health monitoring.

A core-shell structured AuNPs@ZnCo-MOF SERS substrate for sensitive and selective detection of thiram

Surface-enhanced Raman scattering (SERS) enables pesticide residue monitoring to become facile and efficient. In this study, a core-shell structured gold nanoparticles@ZnCo metal-organic framework (AuNPs@ZnCo-MOF) SERS substrate was designed and successfully synthesized for efficient and selective detection of thiram. The bimetallic ZnCo-MOF shell can not only enrich the targeted molecules in the electromagnetic field because of its excellent absorptive capacity, but also act as a stabilized matrix for protecting the AuNPs from aggregation. The AuNPs@ZnCo-MOFs exhibited a high enhancement factor (EF) of 3.51 × 106 and a low detection limit of 1 × 10-7 mol L-1. Besides, the substrate material showed exceptional stability for up to 28 days at room temperature. The AuNPs@ZnCo-MOFs were used to detect thiram which displayed wide linearity (1 × 10-7 to 1 × 10-4 mol L-1) and high recoveries (83.45-99.61%). Moreover, the AuNPs@ZnCo-MOF SERS substrate exhibited excellent anti-interference ability and size selectivity for the target molecules. These indicate that the AuNPs@ZnCo-MOF substrate has great potential for the detection of thiram residues in practical applications.

Deep Learning Enabled SERS Identification of Gaseous Molecules on Flexible Plasmonic MOF Nanowire Films

Through the capture of a target molecule at the metal surface with a highly confined electromagnetic field induced by surface plasmon, surface enhanced Raman spectroscopy (SERS) emerges as a spectral analysis technology with high sensitivity. However, accurate SERS identification of a gaseous molecule with low density and high velocity is still a challenge due to its difficulty in capture. In this work, a flexible paper-based plasmonic metal-organic framework (MOF) film consisting of Ag nanowires@ZIF-8 (AgNWs@ZIF-8) is fabricated for SERS detection of gaseous molecules. Benefiting from its micronanopores generated by the nanowire network and ZIF-8 shell, the effective capture of the gaseous molecule is achieved, and its SERS spectrum is obtained in this paper-based flexible plasmonic MOF nanowire film. With optimal structure parameters, spectra of gaseous 4-aminothiophenol, 4-mercaptophenol, and dithiohydroquinone demonstrate that this film has good SERS performance, which could maintain obvious Raman signals within 30 days during reproducible detection. To realize SERS identification of gaseous molecules, deep learning is performed based on the SERS spectra of the mixed gaseous analyte obtained in this flexible porous film. The results point out that an artificial neural network algorithm could identify gaseous aldehydes (gaseous biomarker of colorectal cancer) in simulated exhaled breath with high accuracy at 93.7%. The integration of the flexible paper-based film sensors with deep learning offers a promising new approach for noninvasive colorectal cancer screening. Our work explores SERS applications in gaseous analyte detection and has broad potential in clinical medicine, food safety, environmental monitoring, etc.

SERS detection of volatile gas in spoiled pork with the Ag/MoS2 nano-flower cavity/PVDF micron-bowl cavity (FIB) substrate

Putrescine and cadaverine are significant volatile indicators used to assess the degree of food spoilage. Herein, we propose a micro-nano multi cavity structure for surface-enhanced Raman spectroscopy (SERS) to analyze the volatile gas putrescine and cadaverine in decomposing food. The MoS2 nano-flowers are inserted into a PVDF micro-cavity through in-situ growth, followed by vacuum evaporation technology of Ag nanoparticles to form an Ag/MoS2 nano-flower cavity/PVDF micron-bowl cavity (FIB) substrate. The micro-nano multi cavity structure can improve the capture capacity of both light and gas, thereby exhibiting high sensitivity (EF = 7.71 × 107) and excellent capability for gas detection of 2-naphthalenethiol. The SERS detections of the putrescine and cadaverine are achieved in the spoiled pork samples with the FIB substrate. Therefore, this substrate can provide an efficient, accurate, and feasible method for the specific and quantitative detection in the food safety field.

Self-Assembled Bifunctional Copper Hydroxide/Gold-Ordered Nanoarray Composites for Fast, Sensitive, and Recyclable SERS Detection of Hazardous Benzene Vapors

Volatile organic compounds (VOCs), particularly monoaromatic hydrocarbon compounds (MACHs), pose a potential risk to the atmospheric environment and human health. Therefore, the progressive development of efficient detection methodologies is a pertinent need, which is still a challenge at present. In this study, we present a rapid and sensitive method to detect trace amounts of MACHs using a bifunctional SERS composite substrate. We prepared an Au/SiO₂ enhanced layer and a porous Cu(OH)₂ adsorption layer via microfluidic-assisted gas-liquid interface self-assembly. The composite substrate effectively monitored changes in benzaldehyde using time-varying SERS spectra, and track-specifically identified various VOCs such as benzene, xylene, styrene, and nitrobenzene. In general, the substrate exhibited a rapid response time of 20 s to gaseous benzaldehyde, with a minimum detection concentration of less than 500 ppt. Further experimental assessments revealed an optimum Cu(OH)₂ thickness of the surrounding adsorption layer of 150 nm, which can achieve an efficient SERS response to MACHs. Furthermore, the recoverable and reusable property of the composite substrate highlights its practicality. This study presents a straightforward and efficient approach for detecting trace gaseous VOCs using SERS, with significant implications in the designing of SERS substrates for detecting other VOCs.

Ultrasensitive SERS substrate for label-free therapeutic drug monitoring of chlorpromazine hydrochloride and aminophylline in human serum

Surface-enhanced Raman spectroscopy (SERS) has been widely used in the field of therapeutic drug monitoring (TDM) because of its powerful fingerprinting capability. In this paper, we used an in situ synthesis method to anchor Ag nanoparticles (AgNPs) on the surface of MIL-101(Cr) to obtain MIL-101(Cr)@Ag. Owing to the large specific surface area and ultra-high porosity of MIL-101(Cr)@Ag, we developed a method for the determination of chlorpromazine hydrochloride (CPZ) and aminophylline (AMP) in human serum by using it as a solid-phase extraction sorbent and SERS substrate. The label-free TDM-SERS method was able to evaluate the levels of CPZ and AMP in serum samples with detection limits as low as 8.91 × 10^-2 µg/mL and 3.4 × 10^-2 µg/mL, respectively. In addition, influencing factors including sample solution pH, AgNO₃ concentration, drug adsorption time, and the amount of sample solution were optimized. This protocol provides a new method with good selectivity, stability, reproducibility, homogeneity, and sensitivity for the determination of small-molecule drug content in serum samples. This label-free TDM-SERS method will help to achieve rapid individualized dosing regimens in clinical practice and has potential applications in the field of TDM.