Surface-enhanced Raman spectroscopy for environmental monitoring using gold clusters anchored on reduced graphene oxide

Rapid and simple sensing of acetylcholinesterase and inhibition activity by utilizing a portable Raman spectrometer

The establishment of a fast, simple-yet-practical, cost-effective and reliable sensing method for the detection of acetylcholinesterase (AChE) activity and inhibition is always desired in clinical Alzheimer's disease (AD) diagnosis and drug screening. Herein, a CoOOH nanosheet-isolated SERS nanoprobe (Ag-Au NPs@4-MBA@CoOOH, AAMC) with core-shell-molecule-shell structure was developed for sensitive and selective quantification of AChE. Experimental results indicated that the CoOOH shell can effectively impede the penetration of the external illuminated laser and block the internal SERS signal of Raman molecules. When AChE and its substrate were present, the specific AChE-catalyzed reaction would be rapidly triggered, resulting in the decomposition of CoOOH in AAMC probe and the generation of greatly enhanced SERS signal. By taking advantage of a portable Raman spectrometer, the AAMC nanoprobe is capable for rapid, sensitive and specific detection of AChE in the range 1 × 10-5 - 10 U/mL (LOD of 7.9 × 10-6 U/mL). Moreover, the measurement of AChE activity in complex human serum samples (with recoveries ranging from 98.0 to 103.3 %) and effective detection of its inhibition activity with the developed strategy were also successfully realized, showing great promise for on-site and point-of-care testing.

Radial SERS acquisition on coffee ring for Serum-based breast cancer diagnosis through Multilayer Perceptron

The coffee-ring effect, involving spontaneous solute separation, has demonstrated promising potential in the context of patient serum analysis. In this study, an approach leveraging the coffee-ring-based analyte redistribution was developed for spectral analysis of surface-enhanced Raman scattering (SERS). By performing radical SERS scanning through the coffee-ring area and sampling across the coffee ring, complicated chemical information was spatially gathered for further spectra analysis. The corresponding application in classification of serum samples from breast cancer patients was also proposed. A simulated serum environment was constructed by mixing phenylalanine, hypoxanthine, and bovine serum albumin (BSA), yielding the coffee-ring patterns along with gold nanoparticles. Distinct divergence in the distributions between hypoxanthine and phenylalanine within the rings were characterized, which is attributed to the inherent electrostatic properties of the noble metal colloid and the interactions among different solvents. Subsequently, this method was applied to serum samples from patients diagnosed with the four breast cancer subtypes. By preparing serum with SERS substrates and forming the coffee-ring patterns, radial SERS scanning was conducted across the rings. The acquired spectra were spatially segmented and processed by employing a multilayer perceptron for learning and prediction. The classification results demonstrated a predictive accuracy of 85.7% in distinguishing among the four breast cancer subtypes, highlighting the feasibility and effectiveness of the coffee-ring assisted radial SERS analysis.

Wet-spun Ag/PEDOT: PSS composite fibers for high-sensitive SERS sensing and high electrical conducting

Nanometal-based composite fibers have been widely explored in flexible sensors due to their outstanding optical and electrical properties. However, the weak binding force between metallic nanomaterial and fiber greatly limits the real application. In this work, nano silver (Ag) are strongly bonded with poly(3,4-ethylenedioxythiophene)-poly (styrene sulfonate) (PEDOT: PSS) fiber by the wet-spun process. Ag-S chemical bonds are formed by the interaction of Ag and PEDOT. The Ag/PEDOT: PSS composite fiber shows excellent surface-enhanced Raman scattering (SERS) sensitivity on Rhodamine 6G (R6G) molecules. The detection limit can reach 10-11 M and Raman enhancement factor (EF) is of 1.3 × 107. The high-sensitive SERS activity of Ag/PEDOT: PSS composite fiber mainly results from PEDOT: PSS, and the enhancement factor is 3 orders of magnitude better than that of other PEDOT: PSS based SERS substrates. Moreover, the composite fiber has metal-level conductivity of 1019 S/cm. This is 5 times higher than the conductivity of PEDOT: PSS fiber and a two-fold improvement over the reported values for nanometal/PEDOT: PSS based fabrics. The composite fiber has electric stability under bending test with bending speeds of 2 Hz indicating the composite fiber has good structural stability. In addition, the temperature of the composite fiber with 7 cm length can reach 76.5 °C at a voltage of 18 V. Additionally, the composite fiber shows anti-bacterial property and melting drop resistance, which pave the way for the integration of fiber-based optical and electrical sensors in the future multifunctional flexible devices.

Modulating the d-Band Center of Palladium via Ethylene Glycol Modification: Accelerating Had Desorption for Enhanced Formate Electrooxidation

This study addresses the critical challenge in alkaline direct formate fuel cells (DFFCs) of slow formate oxidation reaction (FOR) kinetics as a result of strong hydrogen intermediate (Had) adsorption on Pd catalysts. We developed WO3-supported Pd nanoparticles (EG-Pd/WO3) via an organic reduction method using ethylene glycol (EG), aiming to modulate the d-band center of Pd and alter Had adsorption dynamics. Cyclic voltammetry demonstrated significantly improved Had desorption kinetics in EG-Pd/WO3 catalysts. Density functional theory (DFT) calculations revealed that the presence of EG reduces the d-band center of Pd, leading to weaker Pd-H bonds and enhanced Had desorption during the FOR. This research provides a new approach to optimize catalyst efficiency in DFFCs, highlighting the potential for more effective and sustainable energy solutions through advanced material engineering.

Gold-Deposited Graphene Nanosheets for Self-Cleaning Graphene Surface-Enhanced Raman Spectroscopy with Superior Charge-Transfer Contribution

The interaction of graphene with metals initiates charge-transfer interaction-induced chemical enhancements, which critically depend on the doping effect from deposited metallic configurations. In this paper, we have explored the gold nanoparticle-decorated monolayer graphene nanosheets for the large graphene-induced Raman enhancement of adsorbed analytes, indicating the surface-enhanced Raman spectroscopy (SERS) capabilities of metal-doped graphene (G-SERS). Here, the systematically sputtered Au thickness optimization procedure revealed noticeable modifications in the graphene Raman spectra and photoluminescence (PL) background quenching, which indicated favorable charge transfer through n-type doping of chemical vapor deposition-grown graphene nanosheets. The highly consistent, individually distributed morphology of the gold nanoislands over graphene nanosheets depicted a reproducibly uniform G-SERS signal with excellent relative standard deviation values (<5%), resulting in the strongest Raman intensity enhancement factors of ∼108 (MB) (methylene blue) and 107 (DPA) (2,6-pyridinedicarboxylic acid) composed of the weakest PL background. The combined charge-transfer-induced chemical enhancement and electromagnetic enhancement from individual Au nanoislands result in a lowering of detectability down to 10-16 M (MB) and 10-11 M (DPA) concentrations with efficient time-dependent signal stability. Additionally, the GAu demonstrated its effective (∼94.4%) photocatalytic degradation capabilities by decomposing MB dye molecules from a concentration of 1 μM to 2.52 fM within 60 min. Therefore, the prominent charge-transfer contribution through controlled Au decoration over graphene nanosheets provides a potential strategy for fabricating superior SERS sensors and photocatalysts exhibiting adequate signal consistency, stability, and photodegradation efficiency through overcoming the limitations of the traditional sensing platforms.

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

Developments in food neonicotinoids detection: novel recognition strategies, advanced chemical sensing techniques, and recent applications

Neonicotinoid insecticides (NEOs) are a new class of neurotoxic pesticides primarily used for pest control on fruits and vegetables, cereals, and other crops after organophosphorus pesticides (OPPs), carbamate pesticides (CBPs), and pyrethroid pesticides. However, chronic abuse and illegal use have led to the contamination of food and water sources as well as damage to ecological and environmental systems. Long-term exposure to NEOs may pose potential risks to animals (especially bees) and even human health. Consequently, it is necessary to develop effective, robust, and rapid methods for NEOs detection. Specific recognition-based chemical sensing has been regarded as one of the most promising detection tools for NEOs due to their excellent selectivity, sensitivity, and robust interference resistance. In this review, we introduce the novel recognition strategies-enabled chemical sensing in food neonicotinoids detection in the past years (2017-2023). The properties and advantages of molecular imprinting recognition (MIR), host-guest recognition (HGR), electron-catalyzed recognition (ECR), immune recognition (IR), aptamer recognition (AR), and enzyme inhibition recognition (EIR) in the development of NEOs sensing platforms are discussed in detail. Recent applications of chemical sensing platforms in various food products, including fruits and vegetables, cereals, teas, honey, aquatic products, and others are highlighted. In addition, the future trends of applying chemical sensing with specific recognition strategies for NEOs analysis are discussed.

Online detection of concentrate grade in the antimony flotation process based on in situ Raman spectroscopy combined with a CNN-GRU hybrid model

Froth flotation is the most critical process for separating stibnite from raw ore. Concentrate grade is a vital production indicator in the antimony flotation process. It is a direct reflection of the product quality of the flotation process and an essential basis for the dynamic adjustment of its operating parameters. Existing methods of measuring concentrate grades suffer from expensive measurement equipment, difficult maintenance of complex sampling systems, and extended testing times. This paper presents a nondestructive and fast methodology to quantify the concentrate grade in the antimony flotation process based on in situ Raman spectroscopy. A particular Raman spectroscopic measuring system is designed for on-line measurement of the Raman spectra of the mixed minerals from the froth layer during the antimony flotation process. To obtain representative Raman spectra that better characterize the concentrate grades, a traditional Raman spectroscopic system has been redesigned to account for the different interferences during actual flotation field acquisition. A one-dimensional convolutional neural network (1D-CNN) is combined with a gated recurrent unit (GRU) and applied to construct a model for online prediction of concentrate grades based on continuously collected Raman spectra of mixed minerals in the froth layer. With an average prediction error of 4.37% and a maximum prediction deviation of 10.56%, the quantitative analysis of concentrate grade by the model demonstrates that our method is distinguished by high accuracy, low deviation, and in situ analysis, and it essentially satisfies the requirements for online quantitative determination of concentrate grade in the antimony flotation site.