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.

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 %.

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

Microplastics and nanoplastics are emerging classes of environmental contaminants that pose significant threats to human health. In particular, small nanoplastics (<1 µm) have drawn considerable attention owing to their adverse effects on human health; for example, nanoplastics have been found in the placenta and blood. However, reliable detection techniques are lacking. In this study, we developed a fast detection method that combines membrane filtration technology and surface-enhanced Raman spectroscopy (SERS), which can simultaneously enrich and detect nanoplastics with sizes as small as 20 nm. First, we synthesized spiked gold nanocrystals (Au NCs), achieving a controlled preparation of thorns ranging from 25 nm to 200 nm and regulating the number of thorns. Subsequently, mesoporous spiked Au NCs were homogeneously deposited on a glass fiber filter membrane to form an Au film as a SERS sensor. The Au-film SERS sensor achieved in-situ enrichment and sensitive SERS detection of micro/nanoplastics in water. Additionally, it eliminated sample transfer and prevented the loss of small nanoplastics. Using the Au-film SERS sensor, we detected 20 nm to 10 µm standard polystyrene (PS) microspheres with a detection limit of 0.1 mg/L. We also realized the detection of 100 nm PS nanoplastics at the 0.1 mg/L level in tap water and rainwater. This sensor provides a potential tool for rapid and susceptible on-site detection of micro/nanoplastics, especially small-sized nanoplastics.

Surface-enhanced Raman spectroscopy for detection of fentanyl and its analogs by using Ag-Au nanoparticles.

The problem of opioid abuse has become a global problem. Thus, creating an urgent need for highly sensitive detection of opioid substances. In this work, we developed a method for the controllable preparation of Ag@Au nanocrystals (Ag@Au NCs) for highly sensitive SERS detection of fentanyl and its analogs. By regulating the concentration of ligands on the surface of silver seed, we successfully prepared Ag@Au NCs with three different morphologies, including core-satellite, yolk shell and hollow structure. Firstly, we explored the SERS-enhancing effect of Ag@Au NCs with different morphology using rhodamine 6G as the molecule to be tested. The results show that the core-satellite Ag@Au NCs has the best SERS effect, and the lowest detection concentration for R6G reached to 10-10 M. Furthermore, we used the prepared core-satellite Ag@Au NCs to detect fentanyl and its five analogs, including carfentanyl, furanylfentanyl, thiofentanyl, 4-fluorobutyrfentanyl and N-4-piperidylacetanilide. Trace detection was achieved for the above six substances. For the environmental water samples spiked with fentanyl, the calculated recovery was 89.2% with an RSD value of 7.3%. Moreover, in order to realize the qualitative analysis of the characteristic peaks of different fentanyl analogs, we performed DFT calculations on the Raman spectra of the above-mentioned 6 substances. By analyzing the DFT calculation results, conventional Raman spectroscopy and SERS spectroscopy, we realized the distinction of six fentanyl analogs with similar structures.

Facile Synthesis of Porous Ag Crystals as SERS Sensor for Detection of Five Methamphetamine Analogs.

Porous noble metal nanomaterials have attracted extensive attention due to their high specific surface area and surface plasmon resonance effect. However, it is difficult to form porous structures due to the high mobility and low reduction potential of noble metal precursors. In this article, we developed a facile method for preparing porous Ag with a controllable structure at room temperature. Two kinds of Ag crystals with different porous structures were successfully prepared by using AgCl cubes as sacrificial templates. Through the galvanic replacement reaction of Zn and AgCl, Ag crystals with a sponge-like porous structure were successfully prepared. Additionally, using NaBH4 as the reducing agent, we prepared granular porous Ag cubes by optimizing the amount of reducing agent. Both the sponge-like and granular porous Ag cubes have clean and accessible surfaces. In addition, we used the prepared two porous Ag cubes as substrate materials for SERS detection of five kinds of methamphetamine analogs. The experimental results show that the enhancement effect of granular porous Ag is better than that of sponge-like porous Ag. Furthermore, we probed the hot spot distribution of granular porous Ag by Raman mapping. By using granular porous Ag as the substrate material, we have achieved trace detection of 5 kinds of methamphetamine analogs including Ephedrine, Amphetamine, N-Methyl-1-(benzofuran-5-yl)propan-2-amine (5-MAPB), N-Methyl-1-(4-methoxyphenyl)propan-2-amine (PMMA) and N-Methyl-1-(4-fluorophenyl)propan-2-amine (4-FMA). Furthermore, to achieve qualitative differentiation of analogs with similar structures we performed density functional theoretical (DFT) calculations on the Raman spectra of the above analogs. The DFT calculations provided the vibrational frequencies, Raman activities, and normal mode assignment for each analog, enabling the qualitative differentiation of the above analogs.

Predicting apparent singlet oxygen quantum yields of dissolved black carbon and humic substances using spectroscopic indices.

Dissolved black carbon (DBC) is ubiquitous in aquatic systems, being an important subgroup of the dissolved organic matter (DOM) pool. Nevertheless, its aquatic photoactivity remains largely unknown. In this study, a range of spectroscopic indices of DBC and humic substance (HS) samples were determined using UV-Vis spectroscopy, fluorescence spectroscopy, and proton nuclear magnetic resonance. DBC can be readily differentiated from HS using spectroscopic indices. It has lower average molecular weight, but higher aromaticity and lignin content. The apparent singlet oxygen quantum yield (Φsinglet oxygen) of DBC under simulated sunlight varies from 3.46% to 6.13%, significantly higher than HS, 1.26%-3.57%, suggesting that DBC is the more photoactive component in the DOM pool. Despite drastically different formation processes and structural properties, the Φsinglet oxygen of DBC and HS can be well predicted by the same simple linear regression models using optical indices including spectral slope coefficient (S275-295) and absorbance ratio (E2/E3) which are proxies for the abundance of singlet oxygen sensitizers and for the significance of intramolecular charge transfer interactions. The regression models can be potentially used to assess the photoactivity of DOM at large scales with in situ water spectrophotometry or satellite remote sensing.