Vibrational Spectroscopy最新文献

The intention of this review is to reflect on the development of ultrafast 2D-IR spectroscopy to date and to attempt to envisage how the technique might develop in the period between now and 2050. As ultrafast 2D-IR spectroscopy measurements were first-reported in 1998, the timing of this article represents a ‘halfway’ stage, allowing us to look back on 26 years of development to provide a perspective on what the next 26 years might bring. We begin by briefly introducing the method and summarising the development of 2D-IR experiments thus far, but then focus on the most recent advances in technology, sample handling and data analysis methods to inform a discussion on the direction of travel for the field in terms of measurement capabilities. Finally, we examine the most recent applications of 2D-IR, with a particular focus on emerging research areas to show how the field continues to explore new challenges and provide novel insights.

During emergency inspections, drug control institutions often encounter samples with unknown components. It is essential to develop a method for quickly identifying these unknown components. Transforming the component analysis problem into a multi-label classification problem, this study addresses this challenge by employing non-destructive spectroscopic technology combined with machine learning. Spectral data from 368 compounds were initially collected for modeling. The ResUCA model was developed based on the residual neural network and compared with other models. Using the same data enhancement method, ResUCA outperformed the other models in terms of accuracy, recall, precision and F1_score. Subsequently, optimization was performed, considering factors such as data augmentation, spectrum selection, and sample processing, all of which impact the model's construction. Finally, the model was expanded in two steps, maintaining a consistently high recall rate, albeit with an increase in false positives. This suggests that fine-tuning the model parameters can help mitigate this challenge in various scenarios, highlighting its potential for ongoing optimization in future research efforts. Additionally, its applicability extends across diverse fields, including food, cosmetics, and coating analysis.

The field of vibrational biospectroscopy has undergone continuous evolution, advancing from its earliest pioneers to the current innovators. Emerging frontier technologies have enabled vibrational biospectroscopy to reach new heights, expanding its applications in biomedical and clinical settings. Key advancements include the incorporation of multimodal spectroscopy, improvements in spatial resolution and the miniaturization of spectrometers coupled with machine learning. Multimodal spectroscopy is a growing subfield within vibrational biospectroscopy, offering different perspectives of the same sample to better understand the origins of vibrational modes. Meanwhile, the miniaturization of spectrometers has opened the door for field studies and personalized diagnostics, made possible by the integration of machine learning. The combination of miniaturized spectrometers and machine learning has paved the way for novel disease detection approaches. This review will discuss the historical progression of vibrational biospectroscopy and its potential for future applications, with a particular focus on the use of machine learning, multimodal spectroscopy, and miniaturized spectrometers in biomedicine. The primary goal of this review is to provide insight into the prospects of vibrational biospectroscopy, identify gaps in the current literature for future applications, and assess the potential impact of this field in the biomedical domain.

We report the simulations of coherent supercontinuum generation from 2.63 to 8.04 μm in a silicon germanium photonic waveguide. The influence of input quantum noise pulses on coherence of the generated spectra was investigated. A high value of first order degree of coherence (i.e. 0.98) on supercontinuum spectra was predicted numerically. Our mid-infrared simulated coherent chirped supercontinuum source was then used as the input light source in absorption spectroscopy of carbon dioxide and methane gases. The simulated absorbance spectra for these greenhouse gases have high molecular contrast, thanks to the intense, chirped supercontinuum used.

Liquid crystalline properties of the synthesized liquid crystal (LC) N-(o-hydroxybenzylidene)-N'-(4-n-alkoxybenzylidene) azines (HBDBA) are probed thoroughly using the comprehensive array of techniques e.g. differential scanning calorimetry (DSC), differential thermal analysis (DTA), polarizing optical microscopy (POM), temperature-dependent Raman spectroscopy and density functional theory (DFT) method. In this study, intricate molecular interactions crucial for mesophase formation of liquid crystalline system HBDBA and molecular rearrangement that occurs during LC transitions are unravelled comprehensively. Remarkably, at the Cr → SmA phase transition, the peak position, linewidth, and intensity of signature Raman bands are prominently changed. A thorough analysis of Raman marker bands and DFT calculation confirm the disruption of intramolecular hydrogen bonds in HBDBA at the Cr → SmA transition. The conclusion of the present study enriches the understanding of the underlying mechanisms of mesophase formation and intricate molecular interactions and arrangement at the molecular level of the thermotropic LC.

Accurate quantification of bacteria is critical for ensuring food safety, advancing biomedical research, and a range of other pressing concerns. Raman spectroscopy is a popular technique for quantitative analysis due to its benefits of being fast, non-destructive, and highly sensitive. However, the accuracy of the transfer model is often limited by factors such as differences in equipment and environmental noise, which limits the popularization of Raman spectroscopy. In this paper, we propose an approach that overcomes this challenge by introducing a dual branch network based on Continuous Wavelet Transform (CWT) for model transfer. Our model comprises dual branches that perform distinct tasks. The spectral learning branch is responsible for extracting features from the spectral domain. The time-frequency map learning branch employs CNNs for extracting the multi-scale information-rich features. The proposed method is used for the quantitative analysis of Escherichia coli. The proposed approach significantly outperforms traditional methods in improving prediction accuracy. It offers a much-needed solution to the long-standing challenge of Raman spectroscopy in the field of bacterial quantitative analysis. With our approach, we can expect to see Raman spectroscopy more widely adopted in the future.

Dyes are important concerns regarding aquatic environmental contamination given their extensive industrial use and the occurrence of highly toxic and carcinogenic effects on the biota. In this work, photodegradation processes of the organic dye crystal violet (CV) by a hybrid plasmonic photocatalyst involving titanium dioxide (TiO₂) and silver nanoparticles (AgNP), through irradiation with low-power visible light were studied, and the experiments were tracked by ultraviolet-visible absorption (UV–VIS) and surface-enhanced resonance Raman scattering (SERRS) spectroscopies. Enhanced photocatalytic activity was observed, reaching about 71, 80 and 87 % of CV removal with only 100 minutes of irradiation, depending on the Ag loading used. The high photocatalytic efficiency is further highlighted by the low energy consumption in the process, requiring only ca. 1.46 kW h L^-1 in the best reaction condition. Quantum-mechanical calculations were used to the assignment of electronic spectra, as well as to the prediction of frontier molecular orbitals and atomic charges, aiming to propose mechanisms for radical attacks. Such results allow suggesting degradation processes involved mainly N-demethylation and bond breaking of central carbon. The presence of CV protonated species was also supported through Density Functional Theory (DFT) investigation. The integration of theoretical and experimental results allows proposing the formation of pararosaniline, phenol and benzophenone derivatives, which may have highest ecotoxicity than the original contaminant, outstanding the remarkable relevance of SERRS spectroscopy in monitoring such recalcitrant substances.

Radiation therapy, particularly X-ray-based treatment, is widely used against cancer due to its ability to induce cell death, hence local tumor control Recently, increasing attention has been devoted to the role of lipid metabolism in the radiation-induced response of tumor cells. This study utilized Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy to examine the role of lipids in the response of hepatocarcinoma (HepG2) cells to X-ray radiation. Infrared spectra were acquired from lipids extracted from HepG2 cells exposed to different X-ray doses (0, 2, and 6 Gy). Results showed that X-ray exposure causes shifts in the peak positions in infrared spectra indicating biochemical changes in lipid components. The phosphate group asymmetric stretching band shifted to higher wave numbers in the 2 and 6 Gy exposed samples, likely due to alterations in membrane fluidity. The 2-Gy exposure led a reduction of sphingolipid, phospholipid, and fatty acid contributions that can be probably ascribed to apoptosis processes. The 6-Gy exposure triggered also changes in sphingolipid content potentially linked to increased lipid peroxidation supported by higher carbonyl contribution. This peroxidation results in smaller lipid fragments and various degradation products. The changes in sphingolipids are also confirmed by the analysis of different ratios between the areas of selected bands and the results of a mass spectroscopy investigation carried out on the same samples.

The determination of the o-nitrotoluene (o-MNT) content in separation process of mononitrotoluene (MNT) is of interest, since it affects the purity of m-nitrotoluene (m-MNT) and p-nitrotoluene (p-MNT). In real-world applications, the calibration model inevitably requires dealing with complex extrapolation problems. Therefore, this study extracted the spectral features of the o-nitrotoluene based on the interval selection algorithm. The linear calibration method (partial least squares (PLS)) and nonlinear calibration methods (support vector machine (SVM), back propagation (BP), random forest (RF), extreme learning machine (ELM)) were used to build the calibration models based on o-nitrotoluene samples in different concentration ranges, and the prediction accuracy and robustness of the calibration model were compared. The results indicate that the effectiveness of different calibration methods is different when going from prediction accuracy to robustness. The prediction accuracy and robustness of RF models are not satisfactory. BP models, which are capable of producing very accurate results in terms of prediction accuracy, are not able to solve extrapolation problems. PLS model has more advantages in model prediction accuracy. ELM has shown the best behavior in terms of robustness of model, but is inferior to PLS in terms of prediction accuracy.

In recent years, many antibacterial agents have been produced with the aim of eradicating infectious diseases, but many of these agents are ineffective against the resistance presented by bacteria. It is currently estimated that more than 60 % of current antibiotics are ineffective, so the discovery of new drugs is vital. Among the compounds studied in recent years are polyoxotungstates, inorganic compounds targeted for their pharmacological properties. The aim of this study was therefore to chemically characterize two tungstates: calcium and sodium, and to evaluate their microbiological properties, both in combination with antibiotics and due to their ability to reverse the resistance process represented by the expression of the enzyme betalactamase. The microbiological tests were carried out using the microdilution technique, with colorimetric disclosure, using resazurin, and the chemical characterization and vibrational modes of the compounds were evaluated using Fourier transform infrared spectroscopy with attenuated total reflectance. Calcium tungstate showed four spectroscopic bands, located between 84 and 1915 cm⁻¹, while sodium tungstate showed two bands at 335 and 935 cm⁻¹. Calcium tungstate intensified the effect of gentamicin against the bacterium Escherichia coli 06, as well as reversing the mechanism of enzymatic resistance presented by the bacteria Staphylococcus aureus K-4100 and K-4414. Given the current scenario of resistance, these results represent new alternatives for the treatment of bacterial infections, allowing a better understanding of the properties of polyoxometalates. These results are unprecedented as far as the literature is concerned.