Advances in Label-Free Glucose Detection Using Self-Assembled Nanoparticles and Surface-Enhanced Raman Spectroscopy.

In the landscape of biomolecular detection, surface-enhanced Raman spectroscopy (SERS) confronts notable obstacles, particularly in the label-free detection of biomolecules, with glucose and other sugars presenting a quintessential challenge. This study heralds the development of a pioneering SERS substrate, ingeniously engineered through the self-assembly of nanoparticles of diverse sizes (Ag1@Ag2NPs). This configuration strategically induces 'hot spots' within the interstices of nanoparticles, markedly amplifying the detection signal. Rigorous experimental investigations affirm the platform's rapidity, precision, and reproducibility, and the detection limit of this detection method is calculated to be 6.62 pM. Crucially, this methodology facilitates nondestructive glucose detection in simulated samples, including phosphate-buffered saline and urine. Integrating machine learning algorithms with simulated serum samples, the approach adeptly discriminates between hypoglycemic, normoglycemic, and hyperglycemic states. Moreover, the platform's versatility extends to the detection and differentiation of monosaccharides, disaccharides, and methylated glycosides, underscoring its universality and specificity. Comparative Raman spectroscopic analysis of various carbohydrate structures elucidates the unique SERS characteristics pertinent to these molecules. This research signifies a major advance in nonchemical, label-free glucose determination with enhanced sensitivity via SERS, laying a new foundation for its application in precision medicine and advancing structural analysis in the sugar domain.

Surface-Enhanced Raman Scattering Imaging Assisted by Machine Learning Analysis: Unveiling Pesticide Molecule Permeation in Crop Tissues.

Surface-enhanced Raman scattering (SERS) imaging technology faces significant technical bottlenecks in ensuring balanced spatial resolution, preventing image bias induced by substrate heterogeneity, accurate quantitative analysis, and substrate preparation that enhances Raman signal strength on a global scale. To systematically solve these problems, artificial intelligence techniques are applied to analyze the signals of pesticides based on 3D and dynamic SERS imaging. Utilizing perovskite/silver nanoparticles composites (CaTiO3/Ag@BONPs) as enhanced substrates, enabling it not only to cleanse pesticide residues from the surface to pulp of fruits and vegetables, but also to investigate the penetration dynamics of an array of pesticides (chlorpyrifos, thiabendazole, thiram, and acetamiprid). The findings challenge existing paradigms, unveiling a previously unnoticed weakening process during pesticide invasion and revealing the surprising permeability of non-systemic pesticides. Of particular note is easy to overlook that the combined application of pesticides can inadvertently intensify their invasive capacity due to pesticide interactions. The innovative study delves into the realm of pesticide penetration, propelling a paradigm shift in the understanding of food safety. Meanwhile, this strategy provides strong support for the cutting-edge application of SERS imaging technology and also brings valuable reference and enlightenment for researchers in related fields.