Controllable preparation of mesoporous spike gold nanocrystals for surface-enhanced Raman spectroscopy detection of micro/nanoplastics in water

In situ surface-enhanced Raman spectroscopy for the detection of nanoplastics: A novel approach inspired by the aging of nanoplastics

Nanoplastics (NPs) present a hidden risk to organisms and the environment via migration and enrichment. Detecting NPs remains challenging because of their small size, low ambient concentrations, and environmental variability. There is an urgency to exploit detection approaches that are more compatible with real-world environments. Herein, this study provides a surface-enhanced Raman spectroscopy (SERS) technique for the in situ reductive generation of silver nanoparticles (Ag NPs), which is based on photoaging-induced modifications in NPs. The feasibility of generating Ag NPs on the surface of NPs was derived by exploring the photoaging mechanism, which was then utilized to SERS detection. The approach was applied successfully for the detection of polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) NPs with excellent sensitivity (e.g., as low as 1 × 10-6 mg/mL for PVC NPs, and an enhancement factor (EF) of up to 2.42 × 105 for small size PS NPs) and quantitative analytical capability (R2 > 0.95579). The method was successful in detecting NPs (PS NPs) in lake water. In addition, satisfactory recoveries (93.54-105.70 %, RSD < 12.5 %) were obtained by spiking tap water as well as lake water, indicating the applicability of the method to the actual environment. Therefore, the proposed approach offers more perspectives for testing real environmental NPs.

High sensitivity in quantitative analysis of mixed-size polystyrene micro/nanoplastics in one step

As emerging environmental pollutants, microplastics (MPs) and nanoplastics (NPs) pose a serious threat to human health. Owing to the lack of feasible and reliable analytical methods, the separation and identification of MPs and NPs of different sizes remains a challenge. In this study, a hyphenated method involving filtration and surface-enhanced Raman spectroscopy (SERS) for the separation and identification of MPs and NPs is reported. This method not only avoids the loss of MPs and NPs during the transfer process but also provides an excellent SERS substrate. The SERS substrate was fabricated by electrochemically depositing silver particles onto the reduced graphene oxide layer coated on stainless steel mesh. Results show that polystyrene (PS) MPs and NPs are efficiently separated on the SERS substrate via vacuum filtration, resulting in high retention rates (74.26 % ± 1.58 % for 100 nm, 81.06 % ± 1.49 % for 500 nm, and 97.73 % ±0.11 % for 5 μm) and low limit of detection (LOD). The LOD values of 100 nm, 500 nm, and 5 μm PS are 8.89 × 10-5, 3.39 × 10-5, and 1.57 × 10-4 μg/mL, respectively. More importantly, a linear relationship for uniform quantification of 100 nm, 500 nm, 3 μm and 5 μm PS was established, and the relationship is Y = 225.61 lgX + 1076.36 with R2 = 0.980. The method was validated for the quantitative analysis of a mixture of 100 nm, 500 nm PS NPs, 3 μm and 5 μm PS MPs in a ratio of 1:1:1:1, which successfully approaches the evaluation of evaluated PS NPs in the range of 10-4-10 μg/mL with an LOD value of approximately 7.82 × 10-5 μg/mL. Moreover, this method successfully detected (3.87 ± 0.06) × 10-5 μg MPs and NPs per gram of oyster tissue.

Urchin-like covalent organic frameworks templated Au@Ag composites for SERS detection of emerging contaminants

Au@Ag core-shell composites were successfully fabricated on urchin-like covalent organic frameworks (COFs), providing a platform with numerous hot spots for the detection of two categories of emerging contaminants: sulfonamide antibiotics and nanoplastics, using surface-enhanced Raman spectroscopy (SERS). Au seeds (∼10 nm) were generated on the COFs, leveraging the reducing properties of the vinyl and imino groups within the framework. This ensured the growth of dense and uniformly distributed Ag nanoparticles. The COFs exceptionally large surface area (2324 m2 g-1) and high adsorption capacity, significantly contributed to the enrichment and detection of trace pollutants. As a result, using a portable Raman spectrometer, limits of detection of 0.008 μmol L-1 for sulfamethoxazole and 0.029 mg L-1 for polystyrene nanoplastics were achieved.

Silver nanostars arrayed on GO/MWCNT composite membranes for enrichment and SERS detection of polystyrene nanoplastics in water

Nanoplastic water contamination has become a critical environmental issue, highlighting the need for rapid and sensitive detection of nanoplastics. In this study, we aimed to prepare a graphene oxide (GO)/multiwalled carbon nanotube (MWCNT)-silver nanostar (AgNS) multifunctional membrane using a simple vacuum filtration method for the enrichment and surface-enhanced Raman spectroscopy (SERS) detection of polystyrene (PS) nanoplastics in water. AgNSs, selected for the size and shape of nanoplastics, have numerous exposed Raman hotspots on their surface, which exert a strong electromagnetic enhancement effect. AgNSs were filter-arrayed on GO/MWCNT composite membranes with excellent enrichment ability and chemical enhancement effects, resulting in the high sensitivity of GO/MWCNT-AgNS membranes. When the water samples flowed through the portable filtration device with GO/MWCNT-AgNS membranes, PS nanoplastics could be effectively enriched, and the retention rate for 50 nm PS nanoplastics was 97.1 %. Utilizing the strong SERS effect of the GO/MWCNT-AgNS membrane, we successfully detected PS nanoparticles with particle size in the range of 50-1000 nm and a minimum detection concentration of 5 × 10-5 mg/mL. In addition, we detected 50, 100, and 200 nm PS nanoplastics at concentrations as low as 5 × 10-5 mg/mL in real water samples using spiking experiments. These results indicate that the GO/MWCNT-AgNS membranes paired with a portable filtration device and Raman spectrometer can effectively enrich and rapidly detect PS nanoplastics in water, which has great potential for on-site sensitive water quality safety evaluation.

Deep learning analysis for rapid detection and classification of household plastics based on Raman spectroscopy

The overuse of plastics releases large amounts of microplastics. These tiny and complex pollutants may cause immeasurable damage to human social life. Raman spectroscopy detection technology is widely used in the detection, identification and analysis of microplastics due to its advantages of fast speed, high sensitivity and non-destructive. In this work, we first recorded the Raman spectra of eight common plastics in daily life. By adjusting parameters such as laser wavelength, laser power, and acquisition time, the Raman data under different acquisition conditions were diversified, and the corresponding Raman spectra were obtained, and a database of eight household plastics was established. Combined with deep learning algorithms, an accurate, fast and simple classification and identification method for 8 types of plastics is established. Firstly, the acquired spectral data were preprocessed for baseline correction and noise reduction, Then, four machine learning algorithms, linear discriminant analysis (LDA), decision tree, support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN), are used to classify and identify the preprocessed data. The results showed that the classification accuracy of the three machine learning models for the Raman spectra of standard plastic samples were 84%, 93% and 93% respectively. The 1D-CNN model has an accuracy rate of up to 97% for Raman spectroscopy. Our study shows that the combination of Raman spectroscopy detection techniques and deep learning algorithms is a very valuable approach for microplastic classification and identification.

Submicron- and nanoplastic detection at low micro- to nanogram concentrations using gold nanostar-based surface-enhanced Raman scattering (SERS) substrates

The presence of submicron- (1 μm-100 nm) and nanoplastic (<100 nm) particles within various sample matrices, ranging from marine environments to foods and beverages, has become a topic of increasing interest in recent years. Despite this interest, very few analytical techniques are known that allow for the detection of these small plastic particles in the low concentration ranges that they are anticipated to be present at. Research focused on optimizing surface-enhanced Raman scattering (SERS) to enhance signal obtained in Raman spectroscopy has been shown to have great potential for the detection of plastic particles below conventional resolution limits. In this study, we produce SERS substrates composed of gold nanostars and assess their potential for submicron- and nanoplastic detection. The results show 33 nm polystyrene could be detected down to 1.25 μg mL-1 while 36 nm poly(ethylene terephthalate) was detected down to 5 μg mL-1. These results confirm the promising potential of the gold nanostar-based SERS substrates for nanoplastic detection. Furthermore, combined with findings for 121 nm polypropylene and 126 nm polyethylene particles, they highlight potential differences in analytical performance that depend on the properties of the plastics being studied.

SERS and machine learning based effective feature extraction for detection and identification of amphetamine analogs

Surface-enhanced Raman spectroscopy (SERS) is extensively researched in diverse disciplines due to its sensitivity and non-destructive nature. It is particularly considered a potential and promising technology for rapid on-site screening in drug detection. In this investigation, a technique was developed for fabricating nanocrystals of Ag@Au SNCs. Ag@Au SNCs, as the basic material of SERS, can detect amphetamine at concentrations as low as 1 μg/mL. The Ag@Au SNCs exhibits a strong surface plasmon resonance effect, which amplifies molecular signals. The SERS spectra of ten substances, including amphetamine and its analogs, showed a strong peak signal. To establish a qualitative distinction, we examined the Raman spectra and conducted density functional theory (DFT) calculations on the ten aforementioned species. The DFT calculation enabled us to determine the vibrational frequency and assign normal modes, thereby facilitating the qualitative differentiation of amphetamines and its analogs. Furthermore, the SERS spectrum of the ten mentioned substances was analysed using the support vector machine learning algorithm, which yielded a discrimination accuracy of 98.0 %.