Applied Spectroscopy最新文献

This second of the two-part series of a comprehensive survey review provides the diverse applications of two-dimensional correlation spectroscopy (2D-COS) covering different probes, perturbations, and systems in the last two years. Infrared spectroscopy has maintained its top popularity in 2D-COS over the past two years. Fluorescence spectroscopy is the second most frequently used analytical method, which has been heavily applied to the analysis of heavy metal binding, environmental, and solution systems. Various other analytical methods including laser-induced breakdown spectroscopy, dynamic mechanical analysis, differential scanning calorimetry, capillary electrophoresis, seismologic, and so on, have also been reported. In the last two years, concentration, composition, and pH are the main effects of perturbation used in the 2D-COS fields, as well as temperature. Environmental science is especially heavily studied using 2D-COS. This comprehensive survey review shows that 2D-COS undergoes continuous evolution and growth, marked by novel developments and successful applications across diverse scientific fields.

In this study, the application of low-level fusion (LLF) and high-level fusion (HLF) strategies using a combination of Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy in the identification of antemortem and postmortem fracture at different postmortem intervals (PMIs) was investigated. On a technical level, the same hard tissue sample can be detected using a mix of FT-IR and Raman techniques. At the method level, two cutting-edge chemometrics approaches (LLF and HLF) combining FT-IR and Raman spectroscopic data are explored. The models were ranked in accordance with their parametric quality as follows: HLF and LLF + HLF models > LLF single model > Raman single model > FT-IR single model. The LLF model performed marginally better than the Raman model, however, when compared to other models, the HLF model performed considerably better. The HLF model achieved the best performance, with both cross-validation accuracy and test data set accuracy of 0.88. The importance of the feature wavelengths in the model construction process was subsequently evaluated by intersection fusion, and it was found that the absorbance bands of amide I, PO₄^3- ν₁ ν_3, and CH₂ in FT-IR and phenylalanine, CO₃^2- ν₁- PO₄^3- ν₃, and amide III in Raman have outstanding contributions to the construction of antemortem and postmortem fractures identification models. Overall, the combination of FT-IR and Raman with the HLF strategy is a novel and promising approach for developing antemortem and postmortem fracture identification models at different PMIs.

The precise regulation of nanoenzyme activity is of great significance for application to biosensing analysis. Herein, the peroxidase-like activity of carbon dots was effectively modulated by doping phosphorus, which was successfully employed for sensitive, selective detection of acid phosphatase (ACP). Phosphorus-doped carbon dots (P-CDs) with excellent peroxidase-like activity were synthesized by a one-pot hydrothermal method, and the catalytic activity could be easily modulated by controlling the additional amount of precursor phytic acid. P-CDs could effectively catalyze the oxidation of colorless 3,3',5,5'-tetramethylbenzidine (TMB) to blue TMB oxidation products in the presence of hydrogen peroxide. While ACP was able to catalyze the hydrolysis of L-ascorbyl-2-phosphate trisodium salt (AAP) to produce ascorbic acid (AA), which inhibited the peroxidase-like activity of P-CDs, by combining P-CDs nanoenzymes and ACP-catalyzed hydrolysis the colorimetric method was established for ACP detection. The absorbance variation showed a good linear relationship with ACP concentration in the range of 0.4-4.0 mU/mL with a limit of detection at 0.12 mU/mL. In addition, the method was successfully applied to detect ACP in human serum samples with recoveries in the range of 98.7-101.6%. The work provides an effective strategy for regulating nanoenzymes activity and a low-cost detection technique for ACP.

Maize (Zea mays) is one of the most cultivated plants in the world. Due to the large area, the scale of its production, and the demand to increase the yield, there is a need for new environmentally friendly fertilizers. One group of such candidates is bacteria-produced nodulation (or nod) factors. Limited research has explored the impact of nodulation, factors on maize within field conditions, with most studies restricted to greenhouse settings and early developmental stages. Additionally, there is a scarcity of investigations that elucidate the metabolic alterations in the maize stem due to nod-factor exposure. It was therefore the aim of this study. Maize stem's metabolites and fibers were analyzed with various imaging analytical techniques: matrix assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI), Raman spectroscopy, attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR), and diffuse reflectance infrared Fourier transform spectroscopy. Moreover, the biochemical analyses were used to evaluate the proteins and soluble carbohydrates concentration and total phenolic content. These techniques were used to evaluate the influence of nod factor-based biofertilizer on the growth of a non-symbiotic plant, maize. The biofertilizer increased the grain yield and the stem mass. Moreover, the spectroscopic and biochemical investigation proved the appreciable biochemical changes in the stems of the maize in biofertilizer-treated plants. Noticeable changes were found in the spatial distribution and the increase in the concentration of flavonoids such as maysin, quercetin, and rutin. Moreover, the concentration of cell wall components (fibers) increased. Furthermore, it was shown that the use of untargeted analyses (such as Raman and ATR FT-IR, spectroscopic imaging, and MALDI-MSI) is useful for the investigation of the biochemical changes in plants.

Pesticides play an important role in conventional agriculture. Yet, their harmful effects on the environment are becoming increasingly apparent. The occurrence of pesticides is hence being monitored worldwide. For fast, easy, yet sensitive identification, surface-enhanced Raman spectroscopy (SERS) is a powerful tool. In this study, a method is introduced that may be amended to in-field detection of pesticides. Gold and silver nanoparticles were synthesized, size-tailored, and characterized. The herbicide paraquat and the fungicide thiram served as model compounds. The preparation yielded reproducible SERS spectra. Using quantum chemical computation, Raman and SERS spectra were calculated and analyzed. The interpretation of vibrational modes in combination with SERS enhancement and attenuation allowed us to identify compound-specific bands. The assignment was interpreted in terms of the orientation of paraquat and thiram on the gold and silver nanoparticle surfaces. Paraquat preferred a co-planar arrangement parallel to the gold nanoparticle surface and a head-on orientation on the silver nanoparticle. For thiram, breaking of the disulfide bond was recognized, such that interaction with the surface occurred via the sulfur atoms. Successful detection of the pesticides after recollection from vegetable leaves demonstrated the method's applicability for pesticide identification.

Whispering gallery mode resonator sensors are nondisruptive optical sensors that can detect and monitor perturbations in a gaseous environment. Through its resonant properties of peak wavelength, amplitude, and quality factor (Q factor), changes in concentration can be quantified within seconds and monitored over days with great stability. In addition, the small footprint, low cost, and high sensitivity are ideal properties for a disposable sensor that can be utilized in extreme environments. The large Q factor of the resonant cavity enables long interaction lengths and amplifies the effect of small changes in the background refractive index, which is detectable in picometer shifts of the resonance wavelength. However, this measurement is susceptible to changes in other environmental factors such as temperature, pressure, and humidity, which manifest on the picometer wavelength scale, reinforcing the need to decouple the variables. In this work, we compare the spectral response of different diameter resonators to carbon dioxide, nitrogen, and its mixtures, observing the spectral shifting and broadening of the cavity resonance near 1550 nm. In addition, the effect of environmental temperature on spectral shifting due to the thermo-optic effect is characterized and quantified. Lastly, the gas concentrations are changed in real time to showcase the tracking and recovery capabilities of the resonator sensor.

Thyroid nodules are common clinical entities, with a significant proportion being malignant. Early, accurate, and non-invasive tools to differentiate benign and malignant nodules can optimize patient management and reduce unnecessary surgery. This study aimed to evaluate the efficacy and accuracy of near-infrared spectroscopy (NIRS) in distinguishing benign from malignant thyroid nodules. A diffuse reflectance spectrum for a total of 20 thyroid nodule samples (10 samples as colloid goiter and 10 samples as thyroid cancer), were acquired in the wavelength range from 1000 to 2500 nm. Spectral data from NIRS were analyzed by means of principal component analysis (PCA), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) to classify and differentiate thyroid nodule samples. The present study found that NIRS effectively distinguished colloid goiter and thyroid cancer using the first two principal components (PCs), explaining 90% and 10% of the variance, respectively. QDA discrimination plot displayed a clear separation between colloid goiter and thyroid cancer with minimal overlap, aligning with reported 95% accuracy. Additionally, applying LDA to seven PCs from PCA achieved a 100% accuracy rate in classifying colloid goiter and thyroid cancer from near-infrared spectral data. In conclusion, NIRS offers a promising, non-invasive complementing diagnostic tool for differentiating benign from malignant thyroid nodules with high accuracy. Future work should integrate these results into predictive model development, emphasizing external validation, alternative performance metrics, and protecting against potential overfitting translation of a machine learning model to a clinical setting.

Thermal lens spectroscopy (TLS) is a high-sensitivity method to determine the concentration of light-absorbing species in samples. Here, we implemented a transient configuration of the technique, with a focused pump and a collimated probe beam coaxially propagating. A Fabry-Perot optical resonator is incorporated allowing multi-passing of the probe beam through the sample to enhance sensitivity. We show how the low detection limit of the method can be reduced approximately by half by making differential measurements of the signal at a far field in the center point of the probe beam spot and that obtained by spatial filtering of the same beam, the so-called eclipsed signal. Measurements were performed in test samples of Deyman's organic dye, Strawberry 2143 v.7, dissolved in ethanol. The thermal lens signal measured as a function of the dye concentration in water at the center of the beam was compared with the differential signal resulting from this and the eclipsed beam.

Given the growing urge for plastic management and regulation in the world, recent studies have investigated the problem of plastic material identification for correct classification and disposal. Recent works have shown the potential of machine learning techniques for successful microplastics classification using Raman signals. Classification techniques from the machine learning area allow the identification of the type of microplastic from optical signals based on Raman spectroscopy. In this paper, we investigate the impact of high-frequency noise on the performance of related classification tasks. It is well-known that classification based on Raman is highly dependent on peak visibility, but it is also known that signal smoothing is a common step in the pre-processing of the measured signals. This raises a potential trade-off between high-frequency noise and peak preservation that depends on user-defined parameters. The results obtained in this work suggest that a linear discriminant analysis model cannot generalize properly in the presence of noisy signals, whereas an error-correcting output codes model is better suited to account for inherent noise. Moreover, principal components analysis (PCA) can become a must-do step for robust classification models, given its simplicity and natural smoothing capabilities. Our study on the high-frequency noise, the possible trade-off between pre-processing the high-frequency noise and the peak visibility, and the use of PCA as a noise reduction technique in addition to its dimensionality reduction functionality are the fundamental aspects of this work.

This study introduces two novel sandwich-type tungsten-oxygen cluster compounds synthesized by hydrothermal methods, H₄(C₆H₁₂N₂H₂)₃{Na(H₂O)₂[Mn₂(H₂O)(GeW₉O₃₄)]}₂ (Compound 1) and H₂(C₆H₁₂N₂H₂)_3.5{Na₃(H₂O)₄[Co₂(H₂O)(GeW₉O₃₄)]₂}·17H₂O (Compound 2). The two compounds comprise cluster anions [GeW₉O₃₄]^10- coordinated with transition metal atoms, either Mn or Co, and are stabilized by organic ligands. These compounds are crystallized in the hexagonal crystal system and P6₃/m space group. The two compounds were characterized through various techniques. Fourier transform infrared (IR) spectroscopy showed absorption peaks of anionic backbone vibrations of the Keggin cluster at 500-1000 cm^-1, IR spectral peaks of δ(N-H) and ν_as(C-N) of the ligand triethylenediamine at 1000-2000 cm^-1, and IR spectral peaks of the ligand ν_as(N-H) and ν_as(O-H) of water at 3000-3500 cm^-1. Despite similar one-dimensional (1D) IR spectra due to the same cluster anions and similar molecular structures, the two compounds exhibited distinct responses in two-dimensional correlation spectroscopy with IR under magnetic and thermal perturbations. Under magnetic perturbation, Compound 1 showed a strong response peak for ν_as(W-O_b-W), while Compound 2 exhibited a strong response peak for ν_as(W=O_d), possibly linked to differing magnetic particles. Similarly, Compound 1 displayed a strong response peak under thermal perturbation for ν_as(W-O_c-W). In contrast, Compound 2 showed a strong response peak for ν_as(W=O_d); these results may be attributed to the different hydrogen bonding connections between the two compounds, which affect the groups in distinct ways through vibration and transmit these vibrations to the W-O bonds. The research presented in this paper expands the theoretical and experimental data of 2D correlation IR spectroscopy.