SERS detection of surface-adsorbent toxic substances of microplastics based on gold nanoparticles and surface acoustic waves

Microplastics adsorb toxic substances and act as a transport medium. When microplastics adsorbed with toxic substances accumulate in the body, the microplastics and the adsorbed toxic substances can cause serious diseases, such as cancer. This work aimed to develop a surface-enhanced Raman spectroscopy (SERS) detection method for surface-adsorbent toxic substances by forming gold nanogaps on microplastics using surface acoustic waves (SAWs). Polystyrene microparticles (PSMPs; 1 μm) and polycyclic aromatic hydrocarbons (PAHs), including pyrene, anthracene, and fluorene, were selected as microplastics and toxic substances, respectively. Gold nanoparticles (AuNPs; 50 nm) were used as a SERS agent. The Raman characteristic peaks of the PAHs adsorbed on the surface of PSMPs were detected, and the SERS intensity and logarithm of the concentrations of pyrene, anthracene, and fluorene showed a linear relationship (R2 = 0.98), and the limits of detection were 95, 168, and 195 nM, respectively. Each PAH was detected on the surface of PSMPs, which were adsorbed with toxic substances in a mixture of three PAHs, indicating that the technique can be used to elucidate mixtures of toxic substances. The proposed SERS detection method based on SAWs could sense toxic substances that were surface-adsorbed on microplastics and can be utilized to monitor or track pollutants in aquatic environments.

Multiplex SERS detection of polycyclic aromatic hydrocarbon (PAH) pollutants in water samples using gold nanostars and machine learning analysis

Polycyclic aromatic hydrocarbons (PAHs) have attracted a lot of environmental concern because of their carcinogenic and mutagenic properties, and the fact they can easily contaminate natural resources such as drinking water and river water. This study presents a simple and sensitive point-of-care SERS detection of PAHs combined with machine learning algorithms to predict the PAH content more precisely and accurately in real-life samples such as drinking water and river water. We first synthesized multibranched sharp-spiked surfactant-free gold nanostars (GNSs) that can generate strong surface-enhanced Raman scattering (SERS) signals, which were further coated with cetyltrimethylammonium bromide (CTAB) for long-term stability of the GNSs as well as to trap PAHs. We utilized CTAB-capped GNSs for solution-based 'mix and detect' SERS sensing of various PAHs including pyrene (PY), nitro-pyrene (NP), anthracene (ANT), benzo[a]pyrene (BAP), and triphenylene (TP) spiked in drinking water and river water using a portable Raman module. Very low limits of detection (LOD) were achieved in the nanomolar range for the PAHs investigated. More importantly, the detected SERS signal was reproducible for over 90 days after synthesis. Furthermore, we analyzed the SERS data using artificial intelligence (AI) with machine learning algorithms based on the convolutional neural network (CNN) model in order to discriminate the PAHs in samples more precisely and accurately. Using a CNN classification model, we achieved a high prediction accuracy of 90% in the nanomolar detection range and an f1 score (harmonic mean of precision and recall) of 94%, and using a CNN regression model, achieved an RMSEconc = 1.07 × 10-1 μM. Overall, our SERS platform can be effectively and efficiently used for the accurate detection of PAHs in real-life samples, thus opening up a new, sensitive, selective, and practical approach for point-of-need SERS diagnosis of small molecules in complex practical environments.

Investigating Perampanel Antiepileptic Drug by DFT Calculations and SERS with Custom Spinning Cell

SERS, a clinical practice where medical doctors can monitor the drug concentration in biological fluids, has been proposed as a viable approach to therapeutic drug monitoring (TDM) of the antiepileptic drug Perampanel. The adoption of an acidic environment during the SERS experiments was found to be effective in enhancing the spectroscopic signal. In this work, we combine SERS experiments, conducted with a custom spinning cell in controlled acidic conditions, with DFT calculations aimed at investigating the possible protonated forms of Perampanel. The DFT-simulated Raman spectra of protonated Perampanel accounts for most of the observed SERS signals, thus explaining the effective role of protonation of the analyte. Our results suggest protonation as a viable approach to fostering SERS of alkaline drugs.

SERS Detection of the Anti-Epileptic Drug Perampanel in Human Saliva

Surface-Enhanced Raman Scattering (SERS) can obtain the spectroscopic response of specific analytes. In controlled conditions, it is a powerful quantitative technique. However, often the sample and its SERS spectrum are complex. Pharmaceutical compounds in human biofluids with strong interfering signals from proteins and other biomolecules are a typical example. Among the techniques for drug dosage, SERS was reported to detect low drug concentrations, with analytical capability comparable to that of the assessed High-Performance Liquid Chromatography. Here, for the first time, we report the use of SERS for Therapeutic Drug Monitoring of the Anti-Epileptic Drug Perampanel (PER) in human saliva. We used inert substrates decorated with gold NPs deposited via Pulsed Laser Deposition as SERS sensors. We show that it is possible to detect PER in saliva via SERS after an optimized treatment of the saliva sample. Using a phase separation process, it is possible to extract all the diluted PER in saliva from the saliva phase to a chloroform phase. This allows us to detect PER in the saliva at initial concentrations of the order of 10^-7 M, thus approaching those of clinical interest.

Multifunctional magnetic Fe3O4/Cu2O-Ag nanocomposites with high sensitivity for SERS detection and efficient visible light-driven photocatalytic degradation of polycyclic aromatic hydrocarbons (PAHs)

Polycyclic aromatic hydrocarbons (PAHs) with carcinogenic, teratogenic and mutagenic properties are persistent organic pollutants in the environment. Herein, the novel multifunctional Fe₃O₄/Cu₂O-Ag nanocomposites (NCs) have been established for ultra-sensitive surface-enhanced Raman scattering (SERS) detection and visible light-driven photocatalytic degradation of PAHs. Fe₃O₄/Cu₂O-Ag NCs with different amounts of Ag nanocrystals were synthesized, and the effect of Ag contents on SERS performance was studied by finite-difference time-domain (FDTD) algorithm. The synergistic interplay of electromagnetic and chemical enhancement was responsible for excellent SERS sensitivity of Fe₃O₄/Cu₂O-Ag NCs. The limit of detection (LOD) of optimal SERS substrates (FCA-2 NCs) for Nap, BaP, Pyr and Ant was as low as 10^-9, 10^-9, 10^-9 and 10^-10 M, respectively. The SERS detection of PAHs in actual soil environment was also studied. Moreover, a simple SERS method was used to monitor the photocatalytic process of PAHs. The recovery and reuse of Fe₃O₄/Cu₂O-Ag NCs were achieved through magnetic field, and the outstanding SERS and photocatalytic performance were still maintained even after eight cycles. This magnetic multifunctional NCs provide a unique idea for the integration of ultra-sensitive SERS detection and efficient photocatalytic degradation of PAHs, and thus will have more hopeful prospects in the field of environmental protection.

Halogen ions modified Ag NPs for ultrasensitive SERS detection of Polycyclic aromatic hydrocarbons

Rapid ultrasensitive detection of trace polycyclic aromatic hydrocarbons (PAHs) is essential and significant for pollution control due to their hazard, persistence, and the wide distribution in the environment. Therefore, rapid detection of PAHs is critical for controlling pollution and protecting the ecology. Considering the advantages of Surface-enhanced Raman spectroscopy (SERS), a simple and reliable SERS method was proposed for detecting PAHs in water in this work. Three chemicals, namely NaCl, KBr, and KI, were chosen to modify Ag NPs for phenanthrene (Phe) detection, and Ag NPs modified with KBr (Ag-BrNPs) show the best SERS response. The mixing sequence and the concentration of KBr were optimized. The addition order of mixing KBr and Ag NPs before Phe solution is best, and the optimal concentration of KBr was 20 mM. Under the optimal condition, the limit of quantification for Phe, pyrene (Pyr), and anthracene (Ant) were 10^-6 M, 10^-7 M, and 10^-7 M, respectively. Mixed PAHs (Phe, Pyr, and Ant) in spiked water samples have been identified and quantified successfully. The proposed method has good application prospects in environmental pollution monitoring.

Advances in Optical Sensors for Persistent Organic Pollutant Environmental Monitoring

Optical chemical sensors are widely applied in many fields of modern analytical practice, due to their simplicity in preparation and signal acquisition, low costs, and fast response time. Moreover, the construction of most modern optical sensors requires neither wire connections with the detector nor sophisticated and energy-consuming hardware, enabling wireless sensor development for a fast, in-field and online analysis. In this review, the last five years of progress (from 2017 to 2021) in the field of optical chemical sensors development for persistent organic pollutants (POPs) is provided. The operating mechanisms, the transduction principles and the types of sensing materials employed in single selective optical sensors and in multisensory systems are reviewed. The selected examples of optical sensors applications are reported to demonstrate the benefits and drawbacks of optical chemical sensor use for POPs assessment.

Hybrid Sol-Gel Surface-Enhanced Raman Sensor for Xylene Detection in Solution

This paper reports on the fabrication and characterization of a plasmonic/sol-gel sensor for the detection of aromatic molecules. The sol-gel film was engineered using polysilsesquioxanes groups to capture the analyte, through π-π interaction, and to concentrate it close to the plasmonic surface, where Raman amplification occurs. Xylene was chosen as an analyte to test the sensor. It belongs to the general class of volatile organic compounds and can be found in water or in the atmosphere as pollutants released from a variety of processes; its detection with SERS is typically challenging, due to its low affinity toward metallic surfaces. The identification of xylene was verified in comparison with that of other aromatic molecules, such as benzene and toluene. Investigations were carried out on solutions of xylene in cyclohexane, using concentrations in the range from 0 to 800 mM, to evaluate the limit of detection (LOD) of about 40 mM.

Surface-Enhanced Raman Sensing of Semi-Volatile Organic Compounds by Plasmonic Nanostructures

Facile detection of indoor semi-volatile organic compounds (SVOCs) is a critical issue to raise an increasing concern to current researchers, since their emissions have impacted the health of humans, who spend much of their time indoors after the recent incessant COVID-19 pandemic outbreaks. Plasmonic nanomaterial platforms can utilize an electromagnetic field to induce significant Raman signal enhancements of vibrational spectra of pollutant molecules from localized hotspots. Surface-enhanced Raman scattering (SERS) sensing based on functional plasmonic nanostructures has currently emerged as a powerful analytical technique, which is widely adopted for the ultra-sensitive detection of SVOC molecules, including phthalates and polycyclic aromatic hydrocarbons (PAHs) from household chemicals in indoor environments. This concise topical review gives updated recent developments and trends in optical sensors of surface plasmon resonance (SPR) and SERS for effective sensing of SVOCs by functionalization of noble metal nanostructures. Specific features of plasmonic nanomaterials utilized in sensors are evaluated comparatively, including their various sizes and shapes. Novel aptasensors-assisted SERS technology and its potential application are also introduced for selective sensing. The current challenges and perspectives on SERS-based optical sensors using plasmonic nanomaterial platforms and aptasensors are discussed for applying indoor SVOC detection.

Pinhole-Free Shell-Isolated Nanoparticle Enhanced Raman Spectroscopy for Interference-Free Probing of Electrochemical Reactions

Investigating the behavior of analytes at the electrode surface is crucial in understanding the electrochemical and electrocatalytic reactions. Although Surface Enhanced Raman Scattering (SERS) is sensitive to minor chemical changes in the analyte, it is not widely used to study the reaction mechanisms on nonplasmonic surfaces because of the interference from plasmonic SERS substrates. In this study, we have investigated the redox reaction of Nile Blue A on a glassy carbon surface using pinhole-free silica-coated silver nanoparticles for Raman signal enhancement. The silver nanostructures were synthesized by a chemical reduction method, and the quality of the silica layer was confirmed using microscopic and electrochemical method. The in situ spectroelectrochemical data reveals the catalytic interference from silver which considerably alters the native reaction mechanism. The pinhole-free silica layer prevents the hot electron transfer and yields an interference-free enhancement to the Raman signals.

Surface-enhanced Raman spectroscopy method for classification of doxycycline hydrochloride and tylosin in duck meat using gold nanoparticles

This paper investigated on 478 duck meat samples for the identification of 2 kinds of antibiotics, that is, doxycycline hydrochloride and tylosin, that were classified based on surface-enhanced Raman spectroscopy (SERS) combined with multivariate techniques. The optimal detection parameters, including the effects of the adsorption time, and 2 enhancement substrates (i.e., gold nanoparticles as well as gold nanoparticles and NaCl) on Raman intensities, were analyzed using single factor analysis method. The results showed that the optimal adsorption time between gold nanoparticles and analytes was 2 min, and the colloidal gold nanoparticles without NaCl as the active substrate were more conducive to enhance the Raman spectra signal. The SERS data were pretreated by using the method of adaptive iterative penalty least square method (air-PLS) and second derivative, and from which the feature vectors were extracted with the help of principal component analysis. The first four principal components scores were selected as the input values of support vector machines model. The overall classification accuracy of the test set was 100%. The experimental results showed that the combination of SERS and multivariate analysis could identify the residues of doxycycline hydrochloride and tylosin in duck meat quickly and sensitively.

Trace detection of polycyclic aromatic hydrocarbons in environmental waters by SERS

Polycyclic aromatic hydrocarbons (PAHs) are among the most hazardous pollutants and have attracted significant attention in the last decades. Up to now, rapid and on-site trace detection of PAHs remains a challenging issue. Here, taking advantage of the high sensitivity and reliable qualification of Surface-enhanced Raman Spectroscopy (SERS), we firstly carried out trace analyses of 16 typical PAHs in water at concentrations as low as 100-0.1 μg/L, depending on the number of aromatic rings of the molecule. Furthermore, owing to the simplicity of the liquid-liquid extraction (LLE) step, the sensitivity was further improved 2-3 orders of magnitude, and the lowest detectable concentrations were 100, 50, and 5 ng/L for anthracene, pyrene, and benzo[a]pyrene (the three PAHs typically found in heavily polluted waters), respectively. The LLE-SERS approach was successfully applied to the qualitative and quantitative analyses of different (ocean and coast) water samples being spiked by these three PAHs, which showed great promise as a trace detection tool of PAHs under water environments having different contaminant matrices.