While extensive research has focused on understanding the degradation mechanisms of Poly(vinyl acetate) (PVAC) paint under different environmental conditions, limited attention has been paid to the long-term stability of PVAC-based white glues, especially when used in artworks. This study investigates the accelerated degradation, under simulated photoaging, and isothermal treatment of a commercial PVAC-based white glue considered representative of this class of materials used in contemporary artworks to predict its durability and assess its behavior in art objects. Through accelerated aging experiments and comparison with natural aging observed in artworks, the study reveals the formation of chromophores and the release of plasticizers as key processes; in particular, the progressive darkening was considered an early indicator of degradation processes, before structural changes could be detected by FTIR or NMR spectroscopies. The plasticizer loss induces an increase in glass transition temperature, from 7 °C to temperatures higher than room temperature, affecting the adhesive\'s cohesive strength and contributing to the detachment of materials in artworks. The findings underscore the importance of preventive conservation measures to mitigate degradation issues in PVAC-based artworks.

Although visible light-based stereolithography (SLA) represents an affordable technology for the rapid prototyping of 3D scaffolds for in vitro support of cells, its potential could be limited by the lack of functional photocurable biomaterials that can be SLA-structured at micrometric resolution. Even if innovative photocomposites showing biomimetic, bioactive, or biosensing properties have been engineered by loading inorganic particles into photopolymer matrices, main examples rely on UV-assisted extrusion-based low-resolution processes. Here, SLA-printable composites were obtained by mixing a polyethylene glycol diacrylate (PEGDA) hydrogel with multibranched gold nanoparticles (NPs). NPs were engineered to copolymerize with the PEGDA matrix by implementing a functionalization protocol involving covalent grafting of allylamine molecules that have C═C pendant moieties. The formulations of gold nanocomposites were tailored to achieve high-resolution fast prototyping of composite scaffolds via visible light-based SLA. Furthermore, it was demonstrated that, after mixing with a polymer and after laser structuring, gold NPs still retained their unique plasmonic properties and could be exploited for optical detection of analytes through surface-enhanced Raman spectroscopy (SERS). As a proof of concept, SERS-sensing performances of 3D printed plasmonic scaffolds were successfully demonstrated with a Raman probe molecule (e.g., 4-mercaptobenzoic acid) from the perspective of future extensions to real-time sensing of cell-specific markers released within cultures. Finally, biocompatibility tests preliminarily demonstrated that embedded NPs also played a key role by inducing physiological cell-cytoskeleton rearrangements, further confirming the potentialities of such hybrid nanocomposites as groundbreaking materials in laser-based bioprinting.

Mycotoxin contamination on food and feed can have deleterious effect on human and animal health. Agricultural crops may contain one or more mycotoxin compounds; therefore, a good multiplex detection method is desirable to ensure food safety. In this study, we developed a rapid method using label-free surface-enhanced Raman spectroscopy (SERS) to simultaneously detect three common types of mycotoxins found on corn, namely aflatoxin B1 (AFB1), zearalenone (ZEN), and ochratoxin A (OTA). The intrinsic chemical fingerprint from each mycotoxin was characterized by their unique Raman spectra, enabling clear discrimination between them. The limit of detection (LOD) of AFB1, ZEN, and OTA on corn were 10 ppb (32 nM), 20 ppb (64 nM), and 100 ppb (248 nM), respectively. Multivariate statistical analysis was used to predict concentrations of AFB1, ZEN, and OTA up to 1.5 ppm (4.8 µM) based on the SERS spectra of known concentrations, resulting in a correlation coefficient of 0.74, 0.89, and 0.72, respectively. The sampling time was less than 30 min per sample. The application of label-free SERS and multivariate analysis is a promising method for rapid and simultaneous detection of mycotoxins in corn and may be extended to other types of mycotoxins and crops.

Corneal cross-linking (CXL) with riboflavin and ultraviolet A light is a therapeutic procedure to restore the mechanical stability of corneal tissue. The treatment method is applied to pathological tissue, such as keratoconus and induces the formation of new cross-links. At present, the molecular mechanisms of induced cross-linking are still not known exactly. In this study, we investigated molecular alterations within porcine cornea tissue after treatment with riboflavin and ultraviolet A light by surface enhanced Raman spectroscopy (SERS). For that purpose, after CXL treatment a thin silver layer was vapor-deposited onto cornea flaps. To explore molecular alterations induced by the photochemical process hierarchical cluster analysis (HCA) was used. The detailed analysis of SERS spectra reveals that there is no general change in collagen secondary structure while modifications on amino acid side chains are the most dominant outcome. The formation of secondary and aromatic amine groups as well as methylene and carbonyl groups were observed. Even though successful cross-linking could not be registered in all treated samples, Raman signals of newly formed chemical groups are already present in riboflavin only treated corneas.

Surface-enhanced coherent anti-Stokes Raman scattering (SE-CARS) takes advantage of surface plasmon resonances supported on metallic nanostructures to amplify the coherent Raman response of target molecules. While these metallic antennas have found significant success in SE-CARS studies, photoinduced morphological changes to the nanoantenna under ultrafast excitation introduce significant hurdles in terms of stability and reproducilibty. These hurdles need to be overcome in order to establish SE-CARS as a reliable tool for rapid biomolecular sensing. Here, we address this challenge by performing molecular CARS measurements enhanced by nanoantennas made from high-index dielectric particles with more favorable thermal properties. We present the first experimental demonstration of enhanced molecular CARS signals observed at Si nanoantennas, which offer much improved thermal stability compared to their metallic counterparts.

Widespread testing and isolation of infected patients is a cornerstone of viral outbreak management, as underscored during the ongoing COVID-19 pandemic. Here, we report a large-area and label-free testing platform that combines surface-enhanced Raman spectroscopy and machine learning for the rapid and accurate detection of SARS-CoV-2. Spectroscopic signatures acquired from virus samples on metal-insulator-metal nanostructures, fabricated using nanoimprint lithography and transfer printing, can provide test results within 25 min. Not only can our technique accurately distinguish between different respiratory and nonrespiratory viruses, but it can also detect virus signatures in physiologically relevant matrices such as human saliva without any additional sample preparation. Furthermore, our large area nanopatterning approach allows sensors to be fabricated on flexible surfaces allowing them to be mounted on any surface or used as wearables. We envision that our versatile and portable label-free spectroscopic platform will offer an important tool for virus detection and future outbreak preparedness.

Plant hormones are the molecules that control the vigorous development of plants and help to cope with the stress conditions efficiently due to vital and mechanized physiochemical regulations. Biologists and analytical chemists, both endorsed the extreme problems to quantify plant hormones due to their low level existence in plants and the technological support is devastatingly required to established reliable and efficient detection methods of plant hormones. Surface Enhanced Raman Spectroscopy (SERS) technology is becoming vigorously favored and can be used to accurately and specifically identify biological and chemical molecules. Subsistence molecular properties with varying excitation wavelength require the pertinent substrate to detect SERS signals from plant hormones. Three typical mechanisms of Raman signal enhancement have been discovered, electromagnetic, chemical and Tip-enhanced Raman spectroscopy (TERS). Though, complex detection samples hinder in consistent and reproducible results of SERS-based technology. However, different algorithmic models applied on preprocessed data enhanced the prediction performances of Raman spectra by many folds and decreased the fluorescence value. By incorporating SERS measurements into the microfluidic platform, further highly repeatable SERS results can be obtained. This review paper tends to study the fundamental working principles, methods, applications of SERS systems and their execution in experiments of rapid determination of plant hormones as well as several ways of integrated SERS substrates. The challenges to develop an SERS-microfluidic framework with reproducible and accurate results for plant hormone detection are discussed comprehensively and highlighted the key areas for future investigation briefly.

Due to the harmful effects of chemical fertilizers and pesticides, the need for an eco-friendly solution to improve soil fertility has become a necessity, thus microbial biofertilizer research is on the rise. Plant endophytic bacteria inhabiting internal tissues represent a novel niche for research into new biofertilizer strains. However, the number of species and strains that need to be differentiated and identified to facilitate faster screening in future plant-bacteria interaction studies, is enormous. Surface enhanced Raman spectroscopy (SERS) may provide a platform for bacterial discrimination and identification, which, compared with the traditional methods, is relatively rapid, uncomplicated and ensures high specificity. In this study, we attempted to differentiate 18 bacterial isolates from two oaks via morphological, physiological, biochemical tests and SERS spectra analysis. Previous 16S rRNA gene fragment sequencing showed that three isolates belong to Paenibacillus, 3-to Pantoea and 12-to Pseudomonas genera. Additional tests were not able to further sort these bacteria into strain-specific groups. However, the obtained label-free SERS bacterial spectra along with the high-accuracy principal component (PCA) and discriminant function analyses (DFA) demonstrated the possibility to differentiate these bacteria into variant strains. Furthermore, we collected information about the biochemical characteristics of selected isolates. The results of this study suggest a promising application of SERS in combination with PCA/DFA as a rapid, non-expensive and sensitive method for the detection and identification of plant-associated bacteria.

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

In this study, we developed highly sensitive substrates for Surface-Enhanced-Raman-Scattering (SERS) spectroscopy, consisting of silicon nanowires (SiNWs) decorated by silver nanostructures using single-step Metal Assisted Chemical Etching (MACE). One-step MACE was performed on p-type Si substrates by immersion in AgNO3/HF aqueous solutions resulting in the formation of SiNWs decorated by either silver aggregates or dendrites. Specifically, dendrites were formed during SiNWs\' growth in the etchant solution, whereas aggregates were grown after the removal of the dendrites from the SiNWs in HNO3 aqueous solution and subsequent re-immersion of the specimens in a AgNO3/HF aqueous solution by adjusting the growth time to achieve the desired density of silver nanostructures. The dendrites had much larger height than the aggregates. R6G was used as analyte to test the SERS activity of the substrates prepared by the two fabrication processes. The silver aggregates showed a considerably lower limit of detection (LOD) for SERS down to a R6G concentration of 10-13 M, and much better uniformity in terms of detection in comparison with the silver dendritic structures. Enhancement factors in the range 105-1010 were calculated, demonstrating very high SERS sensitivities for analytic applications.