Applied Spectroscopy最新文献

Polarized Raman spectroscopy was used to analyze aqueous solutions of sodium orthophosphate and orthovanadate over a wide concentration range (0.00891-0.702 mol/L) at 23 °C. The isotropic scattering profiles were obtained by measuring polarized Raman scattering spectra. Furthermore, R-normalized spectra were calculated and presented. The tetrahedral ions, VO₄^3-(aq) and PO₄^3-(aq), demand four Raman active bands which have been subsequently characterized and assigned. For the PO₄^3-(aq) ion, the deformation modes ν₂(e) and ν₄(f₂) appear at 415 and 557 cm^-1, and these modes are depolarized. In the P-O stretching region, the strongest Raman band appears at 936.5 cm^-1, which is totally polarized with a depolarization ratio (ρ-value) of 0.002. The broad and depolarized mode at 1010 cm^-1 constitutes the antisymmetric stretching band ν₃(f₂). The Raman spectrum of VO₄^3- shows two depolarized deformation modes ν₂(e) and ν₄(f₂) at 327 and 345.6 cm^-1, which are severely overlapped. These bands are very weak. The strongest band in the Raman spectrum of VO₄^3-(aq) is the symmetric stretching mode ν₁(a₁) at 820.2 cm^-1 which is totally polarized with a ρ-value at 0.004. The depolarized antisymmetric stretching mode ν₃(f₂) appeared at 785 cm^-1 as a broad and weak band. Both anions are strongly hydrated and showed extensive hydrolysis in an aqueous solution. Orthovanadate is a much stronger base than orthophosphate in aqueous solution. Therefore, a large amount of NaOH was used to suppress the hydrolysis of VO₄^3-(aq) sufficiently, so, it was possible to characterize the VO₄^3- modes. Quantitative Raman spectroscopy was applied to follow the hydrolysis of PO₄^3- over a wide concentration range from 0.00891 to 0.592 mol/L. The hydrolysis data allowed the calculation of the pK_a3 value for H₃PO₄ to be 12.330 ± 0.02 (25 °C). The hydrolysis of the VO₄^3- ion is ∼21 times larger than that of the PO₄^3-. The pK_a3 value for H₃VO₄ is estimated to be 13.65 ± 0.1 (25 °C).

In this study, a new system was developed to carry out simultaneous near-infrared (NIR) and small-angle X-ray scattering (SAXS) measurements. Aged polypropylene (PP) was examined with the NIR-SAXS system to demonstrate how it can be utilized to derive pertinent information about the polymer structure. Pairs of SAXS profiles and NIR spectra of PP in its initial state and after aging were measured to derive an in-depth understanding of the aging phenomenon. The SAXS profiles of the PP samples showed a clear shift of the SAXS peak to the lower q direction induced by the thermal aging, indicating an increase in the length of the long-period structure. Two-trace two-dimensional (2T2D) asynchronous correlation spectra derived from NIR spectra clearly revealed that the aging treatment leads to a substantial increase in the spectral intensity of the regularity bands representing the longer helix present in a folded lamellar structure. In other words, it suggests that the long helix structure is more abundantly present than the short helix structure in the aged PP than in the initial PP. By combining the information derived from the SAXS profiles and NIR spectra, the details of the aging-induced variation were clearly determined. Namely, aging causes additional crystallization of the PP by developing more helical structures, which involves an increase in the lamellar thickness as well as a decrease in the amorphous region. The growth of the rigid crystalline phase restricts the elastic deformation in the amorphous structure, which eventually induces the deterioration of PP by making the polymer hard but brittle. Such observation, in turn, implies that retarding or accelerating the crystallized structure of PP substantially works to control the progress of aging.

In this work, we derive a simple method for calibrating Raman bandwidths for the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument onboard NASA's Perseverance rover. Raman bandwidths and shapes reported by an instrument contain contributions from both the intrinsic Raman band (IRB) and instrumental artifacts. To directly correlate bandwidth to sample properties and to compare bandwidths across instruments, the IRB width needs to be separated from instrumental effects. Here, we use the ubiquitous bandwidth calibration method of modeling the observed Raman bands as a convolution of a Lorentzian IRB and a Gaussian instrument slit function. Using calibration target data, we calculate that SHERLOC has a slit function width of 34.1 cm^-1. With a measure of the instrument slit function, we can deconvolve the IRB from the observed band, providing the width of the Raman band unobscured by instrumental artifact. We present the correlation between observed Raman bandwidth and intrinsic Raman bandwidth in table form for the quick estimation of SHERLOC Raman intrinsic bandwidths. We discuss the limitations of using this model to calibrate Raman bandwidth and derive a quantitative method for calculating the errors associated with the calibration. We demonstrate the utility of this method of bandwidth calibration by examining the intrinsic bandwidths of SHERLOC sulfate spectra and by modeling the SHERLOC spectrum of olivine.

Ongoing technological advancements in the field of mid-infrared (MIR) spectroscopy continuously yield novel sensing modalities, offering capabilities beyond traditional techniques like Fourier transform infrared spectroscopy (FT-IR). One such advancement is MIR dispersion spectroscopy, utilizing a tunable quantum cascade laser and Mach-Zehnder interferometer for liquid-phase analysis. Our study assesses the performance of a custom MIR dispersion spectrometer at its current development stage, benchmarks its performance against FT-IR, and validates its potential for time-resolved chemical reaction monitoring. Unlike conventional methods of IR spectroscopy measuring molecular absorptions using intensity attenuation, our method detects refractive index changes (phase shifts) down to a level of 6.1 × 10^-7 refractive index units (RIU). This results in 1.5 times better sensitivity with a sevenfold increase in analytical path length, yielding heightened robustness for the analysis of liquids compared to FT-IR. As a case study, we monitor the catalytic activity of invertase with sucrose, observing the formation of resultant monosaccharides and their progression toward thermodynamic equilibrium. Anomalous refractive index spectra of reaction mixtures, with substrate concentrations ranging from 2.5 to 25 g/L, are recorded, and analyzed at various temperatures, yielding Michaelis-Menten kinetics findings comparable to the literature. Additionally, the first-time application of two-dimensional correlation spectroscopy on the recorded dynamic dispersion spectra correctly identifies the mutarotation of reaction products (glucose and fructose). The results demonstrate high precision and sensitivity in investigating complex time-dependent chemical reactions via broadband refractive index changes.

Growing demand for pesticides has created an environment prone to deceptive activities, where counterfeit or adulterated pesticide products infiltrate the market, often escaping rapid detection. This situation presents a significant challenge for sensor technology, crucial in identifying authentic pesticides and ensuring agricultural safety practices. Raman spectroscopy emerges as a powerful technique for detecting adulterants. Coupling the electrochemical techniques allows a more specific and selective detection and compound identification. In this study, we evaluate the efficacy of spectroelectrochemical measurements by coupling a potentiostat and Raman spectrograph to identify paraquat, a nonselective herbicide banned in several countries. Our findings demonstrate that applying -0.70 V during measurements yields highly selective Raman spectra, highlighting the primary vibrational bands of paraquat. Moreover, the selective Raman signal of paraquat was discernible in complex samples, including tap water, apple, and green cabbage, even in the presence of other pesticides such as diquat, acephate, and glyphosate. These results underscore the potential of this technique for reliable pesticide detection in diverse and complex matrices.

How to identify bloodstains and obtain some potential evidence is of great significance for solving criminal cases. First, the spectral data of different species of bloodstain samples (human blood and animal blood) were acquired by using a hyperspectral imager. Then, an extreme learning machine (ELM) algorithm was used to build the training models of different species of bloodstain samples. Meanwhile, two traditional support vector machine and random forest classification algorithms were also compared with the ELM algorithm. The prediction results showed that the precision, sensitivity, specificity, and F1 score of the ELM algorithm were the highest. This indicates that hyperspectral technology, together with an ELM algorithm, could identify bloodstain species rapidly, non-destructively, and accurately. It has provided a new technical reference for bloodstain detection and identification.

Spectral multivariate calibration aims to derive models characterizing mathematical relationships between sample analyte amounts and corresponding spectral responses. These models are effective at predicting target domain sample analyte amounts when target samples are within the analyte and spectral calibration source domain. Models fail when target samples shift (analyte amounts and/or spectra) from the original calibration domain model. A total recalibration solution requires acquisition of new sample reference values and spectra. However, obtaining enough reference values to distinguish the target domain may be challenging or expensive. A simpler approach adapts the original model to the target domain using target sample spectra without analyte reference values (unlabeled). Analytical chemists have developed several machine learning algorithms using unlabeled regression domain adaptation processes. Unfortunately, prediction accuracy declines for these methods depending on how much the target domain analyte distribution has shifted from the calibration distribution, and regression transfer learning methods are instead needed. Regression domain adaptation and transfer learning are often referred to as model updating in analytical chemistry, but regression domain adaptation only applies to spectral shifts. The regression transfer learning method presented in this paper named null augmentation regression constant analyte (NARCA) leverages unlabeled repeat spectra of a single target sample to update an original calibration model to the shifted target domain sample. With sample repeat spectra, the analyte amount can be assumed constant or nearly constant for NARCA and because models are formed for one sample, NARCA operates as a local modeling method. The performance of NARCA as a regression transfer learning method is evaluated using five near-infrared data sets.

An infrared squaraine dye was utilized to detect Cu²⁺ in solvents based on H-aggregates of squaraine dye. H-aggregates are a type of aggregation with enhanced photophysical properties compared to monomers. In the presence of a Ca²⁺ solution, F-Cl offers exceptional H-aggregators that can be transformed into monomers by adding Cu²⁺. Furthermore, this mode successfully demonstrated fluorescence changes in HeLa cells cultured in vitro after the addition of Ca²⁺ or Cu²⁺. A highly specific detection of Cu²⁺ was achieved using this transformation mode.