Applied Spectroscopy最新文献

Extraction of components from individual refinery streams (e.g., reformates and alkylates) in finished gasoline was undertaken using Raman spectroscopy to characterize the chemical content of the finished product. Modified alternating least squares (MALS) was used for separating Raman spectroscopic data sets of the finished product into its pure individual components. The advantages of MALS over alternating least squares (ALS) for multicomponent resolution are highlighted in this study using three Raman spectroscopic data sets which provide a suitable benchmark for comparing the performance of these two methods. MALS is superior to ALS in terms of accuracy and can better resolve components than ALS, and it is also more robust toward collinear data. Finally, components near the noise level usually cannot be extracted by ALS because of instability when inverting the covariance structure which inflates the noise present in the data. However, these same components can be extracted by MALS due to the stabilization of the least squares regression with respect to the matrix inversion using modified techniques from ridge regression.

Analyzing the composition of animal hair fibers in textiles is crucial for ensuring the quality of yarns and fabrics made from animal hair. Among others, Fourier transform infrared (FT-IR) spectroscopy is a technique that identifies vibrations associated with chemical bonds, including those found in amino acid groups. Cashmere, mohair, yak, camel, alpaca, vicuña, llama, and sheep hair fibers were analyzed via attenuated total reflection FT-IR (ATR FT-IR) spectroscopy and scanning electron microscopy techniques aiming at the discrimination among them to identify possible commercial frauds. ATR FT-IR, being a novel approach, was coupled with chemometric tools (partial least squares discriminant analysis, PLS-DA), building classification/prediction models, which were cross-validated. PLS-DA models provided an excellent differentiation among animal hair of both camelids and eight animal species. In addition, the combination of ATR FT-IR and PLS-DA was used to discriminate the cashmere hair from different origins (Afghanistan, Australia, China, Iran, and Mongolia). The model showed very good discrimination ability (accuracy 87%), with variance expression of 94.88% and mean squared error of cross-validation of 0.1525.

The presence of lead has been identified as a critical health risk in drinking water systems serviced by Pb-bearing plumbing. Among several corrosion control strategies, the use of sodium silicates has attracted interest due to the advantages it offers compared to other approaches, such as phosphate dosage. However, the interaction of silicate ions with lead corrosion scales and other ubiquitous dissolved species such as Al ions in drinking water is not well understood. In this work, surface and bulk spectroscopic analysis of the solid scale is combined with quantitative analysis of the aqueous phase. A detailed spectroscopic probing of the transformations taking place on the solid phase enables us to develop a mechanistic framework for reports published in the last four years in the open literature, suggesting that silicates may not be an adequate corrosion control option in drinking water systems rich in solid lead carbonates. The spectroscopic data obtained demonstrate that in the presence of chlorine residual, silicates inhibit Pb(II) carbonates from oxidizing into less soluble Pb(IV) oxides thus, negatively impacting water quality. Furthermore, aluminum ions interact with silicates resulting in the formation of solid allophane phase over the lead scale surface, extending into the bulk. However, the formation of this new solid allophane phase does not protect against lead dissolution.

Archaeological human remains provide key insight into lifestyles, health, and diseases affecting past societies. However, only limited analyses can be conducted without causing damage due to the destructive nature of current technologies. The same problem exists with current clinical analyses of the skeleton, and the preferred advanced imaging techniques only provide macroscopic information. Raman spectroscopy could provide chemical information without detriment to archaeological bone samples and perhaps the need for invasive diagnostic procedures in the future. This study measured archaeological human vertebrae to investigate if chemical differences with aging were detectable with Raman spectroscopy and if differences in mineral chemistry could contribute to information on bone mineral diseases. The three lowest bones of the spine (lumbar vertebrae L3-L5), which are subject to the heaviest loading in life, of nine adults from three age groups (18-25, 25-45, and 45+ years) were provided by the Thornton Abbey Project. Three biomechanically important anatomical locations were selected for analysis; likely sites chosen to measure any chemical changes associated with aging, the vertebral body center and the zygapophyseal joints. Results detected chemical changes associated with aging. These changes relate to the minerals phosphate (∼960 cm^-1) and carbonate (∼1070 cm^-1), which are fundamental to bone function. Overall mineralization was found to increase with aging, but while carbonate increased with age, phosphate increased up to ∼45 years and then declined. These fluctuations were found in all three vertebrae, but were more distinct in L5, particularly in the vertebral body, indicating this is an optimal area for detecting bone mineral chemistry changes with aging. This is the first Raman analysis of bone samples from the historically significant site of Thornton Abbey. Results detected age-related changes, illustrating that ancient remains can be used to enhance understanding of modern diseases and provide information on the health and lifestyle of historic individuals.

Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for in vivo biomedical applications, such as arthroscopic evaluation of joint injuries and degeneration. Considering that these techniques provide complementary chemical information, in this study, we hypothesized that combining the MIR, NIR, and Raman data from human osteochondral samples can improve the detection of cartilage degradation. This study evaluated 272 osteochondral samples from 18 human knee joins, comprising both healthy and damaged tissue according to the reference Osteoarthritis Research Society International grading system. We established the one-block and multi-block classification models using partial least squares discriminant analysis (PLSDA), random forest, and support vector machine (SVM) algorithms. Feature modeling by principal component analysis was tested for the SVM (PCA-SVM) models. The best one-block models were built using MIR and Raman data, discriminating healthy cartilage from damaged with an accuracy of 77.5% for MIR and 77.8% for Raman using the PCA-SVM algorithm, whereas the NIR data did not perform as well achieving only 68.5% accuracy for the best model using PCA-SVM. The multi-block approach allowed an improvement with an accuracy of 81.4% for the best model by PCA-SVM. Fusing three blocks using MIR, NIR, and Raman by multi-block PLSDA significantly improved the performance of the single-block models to 79.1% correct classification. The significance was proven by statistical testing using analysis of variance. Thus, the study suggests the potential and the complementary value of the fusion of different spectroscopic techniques and provides valuable data analysis tools for the diagnostics of cartilage health.

Raman spectroscopy is used to monitor the development of live neurons exposed to cytosine arabinoside (ara-C). Ara-C is widely used to culture neurons and exclude non-neuronal cells. In this study, Raman spectra obtained from neurons exposed to ara-C were plotted using an analytical model of neuronal development to evaluate the impact of ara-C on neuronal development. After two days of culturing, neurons were exposed to ara-C for 24 h at final concentrations of 0 (control), 5, and 25 μM. Principal component analysis (PCA) was performed to build an analytical model for evaluating neurodevelopmental disorders caused by ara-C treatment. We projected the Raman spectra obtained from ara-C-treated cells onto the control group dataset. The distribution of PC1 scores for neurons exposed to ara-C at a final concentration of 5 μM was not significantly different from that of the control group. In contrast, under a final concentration of 25 μM, the data population at 10 and 15 days of culturing overlapped significantly with that of neurons at 4 days of normal culturing. These results suggest that Raman spectroscopy can detect very small physiological alterations in the neurons even after a short-term exposure (24 h) of ara-C. Our analytical method has high potential to evaluate the developmental stages for living neurons under exposure to chemicals.

We demonstrate single-shot standoff hyperspectral Raman imaging of liquid diisopropyl methylphosphonate at a standoff distance of 1 m using two different techniques: multi-bandpass filter imaging (MBFI) and fiber-bundle imaging spectroscopy (FBIS). We find that MBFI has good spatial resolution, but poor spectral resolution, due to the limitations of commercially available bandpass filters. On the other hand, we find FBIS to have excellent spectral resolution, but limited spatial resolution due to the relatively small number of fibers in a bundle. For FBIS, we also determine, for a 1 m standoff distance, a minimum pump fluence of 10 mJ/cm² to obtain good single-shot spectra.

The development of non-contact in situ techniques for monitoring cure kinetics has the potential to greatly improve both resin formulation and processing. We have recently shown that low-frequency Raman spectroscopy is a viable method for assessing resin structural cure kinetics and complements the traditional chemical conversion determined from the fingerprint region of the spectrum. In this work, we further evaluate the relationship between structural and chemical conversion by investigating two chemically identical yet rheologically different interpenetrating polymer network resin formulations. Rheological analysis demonstrates a relationship between structural conversion and storage modulus, which is not observed in the chemical conversion data. We show that one can produce master cure kinetics curves with comparable kinetic constants using both the chemical and structural conversion methodologies. Parametric analysis of the structural conversion, chemical conversion, and photorheological conversion was combined with a semi-empirical model for the storage shear modulus as a function of the extent of cure.

We have demonstrated high-speed, super-resolution infrared (IR) spectroscopy and chemical imaging of autofluorescent biomaterials and organisms using camera-based widefield photothermal detection that takes advantage of temperature-dependent modulations of autofluorescent emission. A variety of biological materials and photosynthetic organisms exhibit strong autofluorescence emission under ultraviolet excitation and the autofluorescent emission has a very strong temperature dependence, of order 1%/K. Illuminating a sample with pulses of IR light from a wavelength-tunable laser source causes periodic localized sample temperature increases that result in a corresponding transient decrease in autofluorescent emission. A low-cost light-emitting diode-based fluorescence excitation source was used in combination with a conventional fluorescence microscopy camera to detect localized variations in autofluorescent emission over a wide area as an indicator of localized IR absorption. IR absorption image stacks were acquired over a range of IR wavelengths, including the fingerprint spectral range, enabling extraction of localized IR absorption spectra. We have applied widefield fluorescence detected photothermal IR (FL-PTIR) to an analysis of autofluorescent biological materials including collagen, leaf tissue, and photosynthetic organisms including diatoms and green microalgae cells. We have also demonstrated the FL-PTIR on live microalgae in water, demonstrating the potential for label-free dynamic chemical imaging of autofluorescent cells.

This study investigates the combined effects of nanoscale surface roughness and electron-phonon interaction on the vibrational modes of cadmium telluride (CdTe) using resonant Raman spectroscopy. Raman spectra simulations aided in identifying the active phonon modes and their dependence on roughness. Our results reveal that increasing surface roughness leads to an asymmetric line shape in the first-order longitudinal optical (1LO) phonon mode, attributed to an increase in the electron-phonon interaction. This asymmetry broadens the entire Raman spectrum. Conversely, the overtone (second-order longitudinal optical, or 2LO. mode exhibits a symmetrical line shape that intensifies with roughness. Additionally, we identify and discuss the contributions of surface optical phonon mode and multiphonon modes to the Raman spectra, highlighting their dependence on roughness. This work offers a deeper understanding of how surface roughness and electron-phonon scattering influence the line shape of CdTe resonant Raman spectra, providing valuable insights into its vibrational properties.