Journal of Raman Spectroscopy最新文献

Carbonaceous materials (CMs) in black-shale rock specimens from the Kular Range, Sakha Republic, Yakutia, Russia, were investigated by micro-Raman spectroscopy. Studies of CM in Permian rock samples from the Ulakhan-Sis anticline collected from various boreholes in the Kular gold-bearing area, and CM in nodular monazite (NM) samples from alluvial sediments of different creeks of the Yana River basin revealed similar spectral features. Micro-Raman spectra excited at a wavelength of 532 nm exhibited characteristic CM bands together with bands of quartz, muscovite, anatase, rutile, carbonate, and pyrite. In the first-order Raman spectra of CM, G-bands were observed in the wavenumber range of ~1584–1597 cm⁻¹, and D1 bands at ~1347–1351 cm⁻¹ for black shale specimens, whereas for NM, G bands appeared between ~ 1572 cm⁻¹ and 1594 cm⁻¹, and D1 bands between ~1340 cm⁻¹ and 1351 cm⁻¹. NM grains consist of monazite containing numerous mineral inclusions of various sizes (from several to 10–20 μm). The Raman spectral characteristics of these associated minerals are discussed. Temperature ranges for metamorphic transformations derived from the Raman data are estimated to be 300–360 °C for CM bedrock and 346–430 °C for CM NM.

Renal failure (RF) is a progressive disorder characterized by incurability, elevated morbidity, and increased mortality, frequently affecting the adult population, particularly individuals diagnosed with diabetes and hypertension. In this paper, a rapid, easy, and non-invasive diagnostic technique, surface-enhanced Raman spectroscopy (SERS), is discussed for the screening of RF using blood serum samples with elevated creatinine concentrations. Silver nanoparticles (AgNPs) were employed as SERS substrates to enhance the Raman signals of biomolecules present in the serum samples. For this purpose, serum samples from healthy/control individuals and RF patients with varying creatinine concentrations were used for SERS spectral measurements. The diagnostic capability of SERS was further improved by applying multivariate analysis techniques such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) to evaluate the potential of SERS for differentiating and classifying healthy and RF blood serum samples. Moreover, partial least-squares regression (PLSR) was used to predict unknown creatinine concentrations in RF serum samples, yielding coefficients of determination of 0.9395 mg/dL for calibration and 0.9561 mg/dL for prediction/validation. The associated errors included a root mean squared error of calibration (RMSEC) of 0.6977 mg/dL and a root mean squared error of prediction (RMSEP) of 0.8595 mg/dL. This study demonstrates that SERS, combined with multivariate analysis, is a valuable tool for the effective diagnosis of RF.

A stainless steel mesh loaded with silver nanoparticles (SSM/Ag) was developed as a low-cost and high-performance flexible surface-enhanced Raman spectroscopy (SERS) substrate for residual detection of methylene blue (MB) on curved samples. The sensor's performance was systematically investigated using rhodamine 6G (R6G) as a target molecule. The SSM/Ag substrate showed a limit of detection (LOD) of 10⁻⁸ M and an enhancement factor (EF) of 1.4 × 10⁶. The experiment also utilized the SSM/Ag substrate to detect MB residues by wiping Alternanthera philoxeroides leaves, immersing river water samples, and performing in situ detection of the delivery label on the cup body. The results showed that the limits of detection (LODs) for these three cases were 10⁻⁶, 10⁻⁷, and 10⁻⁷ M, respectively, exhibiting a reproducibility relative standard deviation (RSD) below 12%. This provides strong evidence for the validity and feasibility of the SSM/Ag substrate for in situ detection of MB residues on curved samples.

Organic memristors (OMEMRs) based on biopolymeric matrices functionalized with silver nanoparticles (AgNPs) are emerging as promising candidates for next-generation non-volatile memory technologies, owing to their multilevel resistive switching behavior, scalability, and compatibility with solution-processable fabrication methods. In this study, AgNPs were coordinated with chondroitin sulfate (ChS) to produce Ag@ChS-based OMEMRs, which were fabricated via spin-coating and subjected to a comprehensive thermal stability assessment using temperature-resolved Raman spectroscopy over the 25°C–275°C range. The Raman spectra revealed a prominent vibrational mode at 76 cm⁻¹, attributed to AgNP lattice dynamics, along with a distinct Ag–O stretching feature near 240 cm⁻¹, indicative of coordination with carboxylate groups within the ChS matrix. A marked attenuation of sulfate-associated Raman bands was observed at approximately 175°C, signifying a critical degradation threshold associated with desulfation, interfacial breakdown, and irreversible loss of memristive functionality. These findings demonstrate that Raman spectroscopy provides a sensitive, non-destructive platform for probing thermally induced molecular transformations and diagnosing early-stage failure in biopolymer-based memristive systems. The insights gained herein offer valuable guidance for the rational design of thermally robust organic memory devices.

The paint coatings of two scrap metal sculptures by Gonçalo Mabunda were analyzed using XRF, μRaman, and FTIR spectroscopies. These complementary techniques were employed to investigate the materials and degradation processes of recycled paint-coated metals in an art context, an emerging field for material and preservation studies. XRF and μRaman spectroscopies offered detailed insights into the composition and degradation mechanisms of modified industrial materials. These analyses enabled the identification and characterization of several pigments, including phthalocyanine blue and green, chrome yellow and yellow-orange, titanium white, red iron oxide, monoazo red, and yellow goethite, typically used in industrial paint coatings. Alkyd and barium sulfate were identified as the binder and filler in most paint coatings using XRF and FTIR spectroscopies. The stratigraphic study of the paint samples revealed a unique multilayer coating—a silica-lined retroreflective sheet—previously uncharacterized in conservation studies. The analysis of this material offers important insights that may contribute to the conservation of artworks utilizing retroreflective sheets. The combination of colorimetry, ATR-FTIR, and μRaman provided valuable information on the degradation of paint coatings when exposed to heat during the sculptures' assembly process. Results indicate that while a coating's binder may begin to degrade at certain temperatures, the pigment may remain stable. These findings lay the groundwork for a collaborative database on the composition of paint coatings and the definition of effective conservation procedures for painted scrap metals in art and industrial heritage contexts. Overall, this study demonstrates the utility of Raman spectroscopy in conservation research for industrial materials that have been repurposed as art, and its importance in advancing understanding in this novel and rapidly growing area.

Raman spectroscopy is a non-destructive optical detection technique providing fingerprints of molecular vibrations. For on-site detection, the handheld spectrometers require high excitation laser power to overcome the Johnson noises in the detector, leading to deterioration of analyte molecules. In this work, a general deep learning algorithm of neural network integrating residual networks and U-Net (i.e., ResUNet) is developed for reconstruction of Raman spectra from the low-power handheld spectrometer. The deep learning model is trained by a predesigned Raman spectral dataset from only 30 substances covering the full detective wavenumbers of the spectrometer. Two criteria, termed as mean squared error (MSE) and structure similarity index measure (SSIM), are employed to evaluate the training performance. The denoising performance via the ResUNet model is superior to the traditional Savitzky–Golay filter and wavelet transform algorithms, with the MSE down to 0.003038 and the SSIM up to 0.7124, respectively. The F1-score is further employed to evaluate the reconstructed Raman peaks with true positives, false positives, and false negatives, by which the optimized ResUnet model achieves 77.23%. The trained model is able to reduce the excitation laser power by 28-fold in spectral acquisition and well-reconstruct major Raman characteristic peaks, avoiding the potential damage of analyte molecules using handheld spectrometers. The present work paves a new way to upgrade the low-cost handheld spectrometers achieving high sensitivity for on-site detection.