Raman spectra of human skin obtained by laser excitation have been used to non-invasively detect blood glucose. In previous reports, however, Raman spectra thus obtained were mainly derived from the epidermis and interstitial fluid as a result of the shallow penetration depth of lasers in skin. The physiological process by which glucose in microvessels penetrates into the interstitial fluid introduces a time delay, which inevitably introduces errors in transcutaneous measurements of blood glucose. We focused the laser directly on the microvessels in the superficial layer of the human nailfold, and acquired Raman spectra with multiple characteristic peaks of blood, which indicated that the spectra obtained predominantly originated from blood. Incorporating a multivariate approach combining principal component analysis (PCA) and back propagation artificial neural network (BP-ANN), we performed noninvasive blood glucose measurements on 12 randomly selected volunteers, respectively. The mean prediction performance of the 12 volunteers was obtained as an RMSEP of 0.45 mmol/L and R² of 0.95. It was no time lag between the predicted blood glucose and the actual blood glucose in the oral glucose tolerance test (OGTT). We also applied the procedure to data from all 12 volunteers regarded as one set, and the total predicted performance was obtained with an RMSEP of 0.27 mmol/L and an R² of 0.98, which is better than that of the individual model for each volunteer. This suggested that anatomical differences between volunteer fingernails do not reduce the prediction accuracy and 100% of the predicted glucose concentrations fall within Region A and B of the Clarke error grid, allowing acceptable predictions in a clinically relevant range. The Raman spectroscopy detection of blood glucose from microvessels is of great significance of non-invasive blood glucose detection of Raman spectroscopy. This innovative method may also facilitate non-invasive detection of other blood components.

The development of two-dimensional (2D) transition metal dichalcogenides has been in a rapid growth phase for the utilization in surface-enhanced Raman scattering (SERS) analysis. Here, we report a promising 2D transition metal tellurides (TMTs) material, hafnium ditelluride ( HfTe 2 ), as an ultrasensitive platform for Raman identification of trace molecules, which demonstrates extraordinary SERS activity in sensitivity, uniformity, and reproducibility. The highest Raman enhancement factor of 2.32 × 10 6 is attained for a rhodamine 6G molecule through the highly efficient charge transfer process at the interface between the HfTe 2 layered structure and the adsorbed molecules. At the same time, we provide an effective route for large-scale preparation of SERS substrates in practical applications via a facile stripping strategy. Further application of the nanosheets for reliable, rapid, and label-free SERS fingerprint analysis of uric acid molecules, one of the biomarkers associated with gout disease, is performed, which indicates arresting SERS signals with the limits of detection as low as 0.1 mmol/L. The study based on this type of 2D SERS substrate not only reveals the feasibility of applying TMTs to SERS analysis, but also paves the way for nanodiagnostics, especially early marker detection.

Growth mechanism, controlled synthesis, and excellent properties of ultralong CNTs.