Vibrational Spectroscopy最新文献

The synthesized silver nanoparticles (60–70 nm) coated with a thin (5–6 nm) shell of silicon dioxide were applied for the analysis of popular 4-mercaptobenzoic acid (4-MBA) self-assembled monolayer by the shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS). The detailed synthesis, purification of the nanoparticles, and optimizing washing procedure are provided. The obtained results are proved by HR-TEM, UV-Vis, and SHINERS data. The report opens up a broad possibility of using core-shell nanoparticles in SHINERS experiments, avoiding lengthy purification procedures while maintaining particle stability and Raman signal intensity.

Spectral interval screening is a critical step in multivariate calibration, which can improve the model predictive performance and data interpretation. In this study, a novel method for interval selection is proposed based on a hierarchical variables clustering and group smoothly clipped absolute deviation(group SCAD) in combination with partial least squares(VCG-PLS). The proposed method makes use of hierarchical variables clustering to yield a variables partitioning into groups at each level, and these groups of variables from different clustering levels are then used as input for group SCAD. The method is designed to select informative wavelength intervals for near-infrared(NIR) spectroscopic data analysis. The proposed method mainly consists of three steps. Firstly, an effective hierarchical clustering is employed to cluster wavelengths(variables), which generates a partition of variables into groups at each hierarchy level and obtains all possible wavelength intervals. Then, the series of group variables obtained from various hierarchy levels are given as input to group-SCAD, and group-SCAD can generate potential group variables corresponding to each regularization parameter value. Finally, a collection of PLS models is constructed recursively by employing all wavelength intervals except one, until the optimal wavelength intervals are obtained. The optimal intervals correspond to the lowest root mean square error of prediction. The VCG-PLS integrates the advantages of hierarchical variable clustering and group SCAD, which is an efficient technique to enhance the performance of PLS in interval selection. The performance of VCG-PLS was tested on three real NIR datasets. The results demonstrate that VCG-PLS can improve prediction performance with fewer variables and may be a good wavelength interval selection strategy.

Moisture content (MC) is an important index to measure the quality of maize kernels. This study aims to construct a generic prediction model and optimize the characteristic variables for efficiently predicting the MC of maize kernels across various varieties. Visible/near-infrared hyperspectral imaging (HSI) within the wavelength range of 374.98 - 1038.79 nm is employed. The 270 samples containing 18 varieties at five periods over two years are collected. Spectral and color feature data based on the H, S, and V color channels of maize are extracted. Partial least squares regression (PLSR) and principal component regression (PCR) are used to establish MC prediction models. Backward interval least squares regression (Bi-PLS), uninformative variables elimination (UVE), and successive projections algorithm (SPA) are jointly (Bi-PLS, UVE, Bi-PLS-SPA, UVE-SPA, and SPA) used to select characteristic wavelengths. The MC prediction models are developed using whole wavelengths, characteristic wavelengths, color features, and fused data. The results suggest that the optimal PLSR model is based on fused data of color features and characteristic wavelengths selected by UVE-SPA from S-G smoothing spectral data (HSV+S-G-UVE-SPA-PLSR). The Rc and Rp are 0.9804 and 0.9835, respectively. The RMCEc and RMCEp are 1.6889% and 1.6523%, respectively. The RPD is 5.22. The optimal prediction model can quickly measure MC for different maize kernels, guiding diverse application scenarios to enhance the quality of maize kernels and processing efficiency.

The detection of adenosine triphosphate (ATP) is crucial in several biomedical applications. Here, we propose an electro-optical sensor for ATP that functions without optical labels or recognition unit based on the electrochemistry-assisted formation of ATP-Ag+ complexes and subsequent plasmon-supported generation of the intrinsic surface-enhanced Raman scattering (SERS) spectrum of ATP. Using bimetal-functionalized carbon fiber microelectrodes (CFME), the analysis of ATP with the sensor can be carried out in cellular microenvironments. Bimetallic nanostructures composed of gold nanoparticles and silver nanoparticles were electrochemically deposited on CFME as SERS substrates. Both, fabrication and sensing mainly rely on the execution of redox reactions of silver nanoparticles on the microelectrode, where silver ions released from the oxidation reaction are used as a mediator for the formation of complexes with ATP, which are then anchored to the surface of the microelectrode by the reduction reaction. The SERS spectrum collected from this plasmonic substrate can be used to recognize ATP at low concentrations in the nM range. The miniaturized SERS sensor was applied to cell culture medium and human serum as complex biological microenvironments.

The Taj Mahal and its complex are iconic symbols of India’s cultural heritage, and preserving their appearance and structural integrity is of utmost importance. The discoloration of ancient heritage structures caused by microbial soiling, particularly the action of cyanobacteria, green algae, and lichens, and the role of Extracellular Polymeric Substances (EPS) secreted by these microorganisms, is an important interdisciplinary study. These microorganisms colonize the surfaces of monuments, leading to aesthetic and structural issues. Various analytical techniques like optical microscopy, FTIR (Fourier-transform infrared spectroscopy), XRD (X-ray diffraction), and Scanning Electron Microscopy (SEM) were used to characterize the biofilm and its components. FTIR analysis revealed interactions between EPS and Nano iron oxide in the biofilm, which contributes to surface discoloration. Airborne particulates minerals such as quartz, feldspar, calcite, and hematite are significant source of surface crust on these monuments. This analytical approach has allowed to unravel the complex features of surface discoloration on these historic structures leading to apply appropriate conservation measures.

The ground state pyramidal geometries of NX₃ (X = H, F, Cl, Br) molecules might undergo a process called “pyramidal inversion”, with a planar transition state structure connecting two identical but oppositely oriented pyramids. In view of recent findings regarding infrared intensities of planar molecules as well as IR intensities of transition states structures, which have demonstrated how the atomic dipoles cannot be ignored when describing the molecular dipole moment, we now combine these two approaches in evaluating the IR intensities of the planar transition state structures of the pyramidal inversions of NX₃. We also applied a numerical method to decompose the force constant of the out-of-plane imaginary normal mode. Our findings show that Coulomb forces are the main factor that shapes the inversion barrier of those molecules. Also, the Charge–Charge Transfer–Dipolar Polarization (CCTDP) decomposition of the imaginary reveals that, while the CT term is null due to symmetry constraints, the DP contribution follows the same direction of the inversion and the atomic polarization X in response to the nitrogen movement in the imaginary normal mode depends on the relative volume of N and X. The out-of-plane normal modes of molecules such as PF₅ are slight different from those of NX₃, since their normal modes may not be subject to the same symmetry constrains, indicating a mathematical distinction between planar and non-planar molecules.

This study investigated the use of handheld spatially offset Raman spectroscopy for the identification of drugs concealed within fruit and vegetable food products, which is a common method of drug trafficking in busy environments such as airports. Handheld Raman spectroscopy is advantageous due to its mobility, speed, and chemical specificity for drug analysis. In this study, spatially offset Raman spectra of six substances were collected and included cocaine and its impurities. Raman spectra were collected for drugs on their own and for drugs concealed in transparent bags and in various food products such as green pepper, pomegranate, potato, and zucchini. The collected spectra were analyzed using different algorithms. The results showed successful identification of drugs in three out of the four tested food products, except for pomegranate, which had a thick rind and spongy tissue that hindered detection. An instrumental hit quality index algorithm provided instant identification with matches above 80% in the three identified products. Correlation in wavelength space yielded high correlation coefficient values between substances in food substrates and reference substances, although there were a few false negatives due to noisy spectra. Principal component analysis successfully differentiated between drugs in different food products. In summary, the study demonstrated the potential of handheld spatially offset Raman spectroscopy for identifying drugs concealed within food products. Future work aims to expand the technique to a wider range of substances and food products and develop a quantitative approach to predict substances’ concentrations. Overall, this research contributes to the field of forensic applications and offers insights into the detection of illicit drugs in challenging scenarios.

The natural gas production from Brazilian pre-salt fields imposed new challenges for petrochemical industry. Actual treatment facilities are not adequate for this new scenario and studies have been conducted to apply new adsorbents materials and membranes for water and CO₂ removal from natural gas at high pressures. To better develop such investigations, sensors for on-line monitoring of natural gas properties like CO₂ and water content are important, since their presence affects the quality of the final product. Near infrared (NIR) spectroscopy associated to chemometric models (partial least squares) were employed for on-line monitoring of representative natural gas systems (methane/CO₂, methane/water and methane/CO₂/water) and a real natural gas at temperature and pressure ranges from 20 to 60 °C and 10 to 200 bar, respectively, water content up to gas saturation and CO₂ content up to 50 wt%. Water solubility values used as reference for NIR Spectrometer calibration in model systems were taken from the literature and for real natural gas calculated with Cubic Plus Association (CPA) Equation of State (EoS), while CO₂ content was experimentally controlled aiming to calibrate the chemometric models in the full range of pressure and temperature. Several strategies were adopted for the chemometric model’s development to obtain the best correlation between NIR spectra and experimental data. Results indicate good correlation in both calibration and validation steps attaining linear correlation coefficients (R²) higher than 0.96 for all systems investigated. The proposed methodology is a potential tool for on-line monitoring of natural gas composition, including CO₂ and water content, at high-pressures and can be applied at petrochemical industries or in laboratories, dispensing sampling or any sample preparation.

“Drug-facilitated sexual assault" (DFSA) is a sexual assault perpetrated against a person rendered unconscious by a substance that changes her/his physical and/or mental condition, such as ethanol or drugs. Several active pharmaceutical ingredients, whether used alone or with alcoholic beverages, can produce anterograde amnesia and loss of inhibition. The most common pharmaceuticals found in DFSAs are GHB (-hydroxybutyric acid), benzodiazepines (Valium, Xanax, or Roipnol), antidepressants (Venlafaxine), muscle relaxants (cyclobenzaprine), antihistamines, sleeping pills (diphenhydramines), hallucinogens, and opioids. Biological samples are typically examined in cases of suspected DFSA; however, occasionally, samples are sent to labs a long period after being collected, jeopardizing the accuracy of the analysis. As a result, in recent years, the focus has shifted to directly detecting the presence of drugs in alcoholic beverages. In light of this, the purpose of the current study is to build a FT-IR-based approach for the determination of alprazolam in a common long drink (gin and tonic). To achieve this goal, pure (Class Pure) and spiked gin tonics (Class Spiked) were analyzed by Fourier-transform infrared spectroscopy (FT-IR). Afterward, two classifiers were used: Sequential preprocessing through ORThogonalization Linear Discriminant Analysis (SPORT-LDA) and Soft Independent Modeling of Class Analogies (SIMCA). Both approaches provided good results: SPORT-LDA achieved a 95% and a 98% accuracy rate (on the external test set of samples) for spiked and pure cocktails, respectively. This corresponds to the misclassification of 5 spiked and 1 pure drinks. The SIMCA model of class pure achieved 98.2% and 91.7% of specificity and sensitivity, respectively, coinciding with 55 pure samples (over 60) correctly accepted and 2 (over 110) erroneously rejected by the model. In conclusion, the SIMCA model of class pure seems preferable, because it minimizes the type II error. Eventually, the study was circumscribed to the spiked cocktails and a novel SPORT model was used to quantify alprazolam in spiked cocktails. This provided noteworthy results, in fact, it led to a Root Mean Square Error in Prediction (RMSEP) of 0.95, and a R²_pred of 0.98.

The low solubility of solute molecules in a nonpolar solvent and the weak interactions between solute and solvent molecules can be utilized to study isolated solute molecules. The in-situ inclusion process of H₂O and D₂O in CCl₄ was observed by infrared absorption spectroscopy. These in-situ spectra showed deviations from the monomer spectrum over time attributed to the formation of water dimers. The water dimer spectrum was extracted by subtraction between the time-series spectra. DFT calculations allowed the assignment of the four vibrational modes in the dimer spectrum. The most notable changes from the monomer to the dimer spectra were in the donor molecule, which showed a peak redshift and a large increase in the absorption strengths, especially for the donor OH (OD) bond which participates in the hydrogen bonding to the acceptor water molecule. Using free energy calculations, the equilibrium constants of H₂O and D₂O dimers dissolved in CCl₄ were determined to be 0.013 and 0.015, respectively, and the concentrations were determined to be ∼1 $\mu$M. The intensity ratio between the $v_3$ mode of the water monomer and the $v_{\mathrm{BD}}$ mode of the dimer calculated through DFT was found to be in close agreement with the ratios from the monomer and dimer spectra. In contrast, when the solvent was chloroform, the increase of the $v_{\mathrm{BD}}$ mode of the dimer from the experiment did not agree with that from the calculations, indicating that the specific interaction of the water molecules with chloroform should be considered.