We demonstrate the reliable creation of multiple layers of Au nanoparticles in random close-packed arrays with sub-nm gaps as a sensitive surface-enhanced Raman scattering substrate. Using oxygen plasma etching, all the original molecules creating the nanogaps can be removed and replaced with scaffolding ligands that deliver extremely consistent gap sizes below 1 nm. This allows precision tailoring of the chemical environment of the nanogaps which is crucial for practical Raman sensing applications. Because the resulting aggregate layers are easily accessible from opposite sides by fluids and by light, high-performance fluidic sensing cells are enabled. The ability to cyclically clean off analytes and reuse these films is shown, exemplified by sensing of toluene, volatile organic compounds, and paracetamol, among others.
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http://dx.doi.org/10.1021/acssensors.3c00967 | DOI Listing |
Nanophotonics
January 2025
Institute of Physical Chemistry, Polish Academy of Sciences, 01-224 Warsaw, Kasprzaka 44/52, Poland.
A combination of femtosecond stimulated Raman scattering and surface-enhanced Raman scattering, termed surface-enhanced stimulated Raman scattering (SE-FSRS), was proposed to leverage both temporal precision and sensitivity for advanced molecular dynamics analysis. During the initial successful implementations of this approach, unexpected spectral distortions were observed, and several potential explanations were proposed. Further progress in this novel technique and its broader implementation requires a profound understanding of the factors influencing the shape of the registered spectra and the underlying mechanisms.
View Article and Find Full Text PDFNanophotonics
January 2025
Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland.
Sum-frequency generation (SFG) enables the coherent upconversion of electromagnetic signals and plays a significant role in mid-infrared vibrational spectroscopy for molecular analysis. Recent research indicates that plasmonic nanocavities, which confine light to extremely small volumes, can facilitate the detection of vibrational SFG signals from individual molecules by leveraging surface-enhanced Raman scattering combined with mid-infrared laser excitation. In this article, we compute the degree of second order coherence ( (0)) of the upconverted mid-infrared field under realistic parameters and accounting for the anharmonic potential that characterizes vibrational modes of individual molecules.
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January 2025
Faculty of Physics, M.V. Lomonosov Moscow State University, 1-2 Leninskie Gory, Moscow, 119991, Russia.
The issue of variability introduced into blood plasma and serum analysis by preanalytical procedures is the major obstacle to obtaining accurate and reproducible results. While the question of how to overcome this issue has been discussed in biochemical detection of analytes and omics technologies, its relevance to the field of optical spectroscopy remains mostly unexplored. In this work, we evaluated the freeze-thaw cycle (FTC)-induced alternations in blood serum optical properties by means of autofluorescence and Raman spectroscopy, including surface-enhanced Raman spectroscopy (SERS).
View Article and Find Full Text PDFFood Chem
January 2025
School of Chemistry and Chemical Engineering, University of Jinan, Jinan 250022, China.
Carbaryl is a broad-spectrum carbamate fungicide that may pose a threat to ecosystems and human health. To prevent and control the harm caused by excessive application of carbaryl, a full-dimensional divergence effect SERS sensor has been constructed. Biodegradable paper chips were used as sensor substrates.
View Article and Find Full Text PDFAnalyst
January 2025
Department of Chemistry, University of Victoria, Victoria, British Columbia, V8W 3V6, Canada.
Infrared absorption spectroscopy and surface-enhanced Raman spectroscopy were integrated into three data fusion strategies-hybrid (concatenated spectra), mid-level (extracted features from both datasets) and high-level (fusion of predictions from both models)-to enhance the predictive accuracy for xylazine detection in illicit opioid samples. Three chemometric approaches-random forest, support vector machine, and -nearest neighbor algorithms-were employed and optimized using a 5-fold cross-validation grid search for all fusion strategies. Validation results identified the random forest classifier as the optimal model for all fusion strategies, achieving high sensitivity (88% for hybrid, 92% for mid-level, and 96% for high-level) and specificity (88% for hybrid, mid-level, and high-level).
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