Applied Spectroscopy最新文献

A compact dual-gas sensor based on the two near-infrared distributed feedback diode lasers and a multipass cell has been established for the simultaneous measurement of methane (CH₄) and acetylene (C₂H₂). The time division multiplexing calibration-free direct absorption spectroscopy is used to eliminate the cross interference in the application of multicomponent gas sensors. A wavelength stabilization technique based on the proportion integration differentiation feedback control is developed to suppress laser wavelength drift and an H-infinity (H_∞) filter algorithm to reduce the system noise. The results show that the detection sensitivity of CH₄ and C₂H₂ reaches 39.9 parts per billion (ppb) and 47.3 ppb in the optimal integration time of 556 s and 312 s, respectively. In addition, the 31 consecutive hours measured results of CH₄ in outdoor ambient air show that the proposed detection technology is very suitable for high-precision in-situ measurement of trace gases.

We present a near real-time measurement method that combines Raman and spark emission spectroscopy to quantitatively analyze the molecular structure of airborne single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs), as well as detect toxic metals within CNTs. A corona-based aerosol microconcentrator was used for airborne CNTs sampling to enhance the measurement accuracy and sensitivity. The intensity of the characteristic Raman bands of CNTs and atomic emission lines of metals exhibited a linear relationship with the analyte mass, yielding high coefficient R² values. By carefully selecting appropriate signal peaks for calibration, we achieved a limit of detection (LOD) in terms of air concentration as low as 0.09 μg/m³ for SWCNT and 0.81 μg/m³ for MWCNT with a sampling time of 10 min. Additionally, our method exhibited excellent performance in measuring metals, with a mass LOD of 0.8-0.9 ng for Co and Ni and a mass LOD of 35.09 ng for Fe. The method performed well for the measurement of CNT and relevant metal composition with advantages of near real-time monitoring, low LOD, and portable use, making it a valuable tool for various applications in nanomaterial analysis.

Under various atmospheric conditions, laser-induced breakdown spectroscopy (LIBS) is a powerful technique for elemental analysis, including in Earth- and Mars-like environments. However, understanding the plasma behavior and its dependence on ambient pressure and laser parameters remains a challenge. In this study, a numerical model based on a three-temperature Eulerian radiation framework under non-local thermodynamic equilibrium conditions is employed to investigate the interaction of a nanosecond laser pulse with a graphite target under helium (He) and carbon dioxide (CO₂ atmospheres. The aim is to provide insights into the effects of focusing conditions and ambient pressure (3 to 9 mbar and 1000 mbar) on plasma parameters relevant to both Earth- and Mars-like settings. Our results show that increased ambient pressure significantly enhances electron and ion densities, while the focusing conditions influence the temperature and fluid velocity of plasma species, as well as the spatial distribution and intensity of the plasma, ultimately affecting its diagnostic potential. These findings are critical for optimizing LIBS applications in planetary exploration and contribute to improving quantitative analyses under varying atmospheric compositions.

Cavity ring-down spectroscopy (CRDS) is rapidly becoming an invaluable tool to measure hydrogen (δ²H) and oxygen (δ¹⁸O) isotopic compositions in water, yet the long-term accuracy and precision of this technique remain relatively underreported. Here, we critically evaluate one-year performance of CRDS δ²H and δ¹⁸O measurements at ETH Zurich, focusing on high throughput (~200 samples per week) while maintaining required precision and accuracy for diverse scientific investigations. We detail a comprehensive methodological and calibration strategy to optimize CRDS reliability for continuous, high-throughput analysis using Picarro's "Express" mode, an area not extensively explored previously. Using this strategy, we demonstrate that CRDS achieves long-term precision better than ±0.5‰ for δ¹⁸O and ±1.0‰ for δ²H (±1σ) on three United States Geological Survey (USGS) reference materials treated as unknowns.¹⁸ Specifically, reported results for each reference material over this one-year period are: (i) USGS W-67444: ${\delta }^2$H = $-399.32\pm 0.96\text{‰}$, ${\delta }^{18}$O = $-51.07\pm 0.45\text{‰}$ ($n=30$), (ii) USGS W-67400: ${\delta }^2$H = $2.55\pm 0.49\text{‰}$, ${\delta }^{18}$O = $-1.85\pm 0.13\text{‰}$ ($n=140$), and (iii) USGS-50: ${\delta }^2$H = $33.68\pm 0.91\text{‰}$, ${\delta }^{18}$O = $5.03\pm 0.04\text{‰}$ ($n=21$). We also address challenges such as aligning our analytical uncertainties with the narrower uncertainties of International Atomic Energy Agency reference materials, and mitigating inherent CRDS issues like memory and matrix effects when analyzing environmental samples. Our review provides a practical framework for CRDS applications in hydrology, paleoclimatology, and biogeochemistry, underscoring the importance of continuous evaluation and methodological refinement to ensure accuracy and precision in δ²H and δ¹⁸O analyses.¹⁸.

The optical and dielectric properties of opals with different water contents were investigated using terahertz time-domain spectroscopy. The refractive indices and absorption coefficients showed different trends due to the different water contents. The effective medium theory was used to extract the intrinsic dielectric permittivity of opal from opal-polytetrafluoroethylene mixtures. The extracted dielectric permittivities were fitted using a double Debye model to analyze the microscopic relaxation mechanism.

Acute leukemia, a highly perilous cancer, is diagnosed using invasive procedures like bone marrow aspirate and biopsy (BMA/BMB). This study investigated the use of artificial intelligence (AI)-enhanced Fourier transform infrared (FT-IR) spectroscopy as a non-invasive, reagent-free diagnostic alternative with high sensitivity and specificity. The spectral peak patterns of peripheral blood smears (PBS) from clinically healthy individuals (n = 50) BMA/BMB-confirmed acute leukemia patients (n = 50) were examined in the 1800-850 cm^-1 range. Six trained models were used to assess the diagnostic performance, focusing on accuracy, positive predictive value (PPV), negative predictive value (NPV), F₁ score, and area under the receiver operating characteristic (ROC) curve (AUC). The study shows significantly lower absorbance peaks in leukemia cases compared to healthy controls across various spectral regions: 1637.82, 1528.63, 1448.29, and 1388.54 cm^-1, 1302.02, and 1240.21 cm^-1, and 1163.99 cm^-1. These differences indicate decreased concentrations or distinct molecular configurations of proteins, lipids, nucleic acids, and carbohydrates in cases. Conversely, they exhibited elevated absorbance peaks at 1032.14 and 894.11 cm^-1 regions, suggesting potential disparities in amino acid, DNA, fatty acid, and saccharide residues compared to healthy controls. Of the six trained models, the SVM model demonstrated remarkable diagnostic performance, achieving an accuracy of 83%, a PPV of 80%, an NPV of 86%, an F₁ score of 82.47%, and an AUC of 90.76%. This study demonstrates the potential of AI-enhanced FT-IR spectroscopy as a valuable adjunct diagnostic tool for acute leukemia. By offering a less invasive and faster alternative to BMA/BMB, this approach can potentially enhance leukemia diagnosis and improve patient outcomes, particularly in pediatric and geriatric cases.

Significant dehydration can increase thermoregulatory and cardiovascular strain and impair physical and cognitive performance. Despite these negative effects, there are currently no objective, non-invasive tools to monitor systemic hydration. Raman spectroscopy is an optical modality with the potential to fill this gap because it is sensitive to water, provides results quickly, and can be applied non-invasively. In this work, high wavenumber Raman spectroscopy has been developed toward detection of systemic hydration via validation with tissue-mimicking phantoms, followed by three in vivo feasibility studies to investigate the relationship between spectral features and systemic hydration. The area under the curve (AUC) of the water bands and the ratio of water bands to CH bands are Raman-derived metrics that can be used to describe systemic hydration. Here, we determined a trend in decreasing water bands AUC after exercise, although the magnitude of the change was highly variable. In investigating the sources of variability, we identified significant inter-subject variability and a failure of current clinical standards to benchmark our developed technique against. Despite the high variability, we found that multiple anatomical locations were suitable for collecting the spectral measurements. While the high degree of variability may confound the use of Raman spectroscopy for non-invasive hydration monitoring, when implementing additional study standardization, significant differences (p <.05) in spectral metrics can be identified before and after exercise. Raman spectroscopy can allow for rapid, non-invasive detection of systemic hydration, which would improve routine hydration monitoring and reduce the incidence of negative side effects associated with dehydration.

The low thickness of plastic films poses a challenge when using near-infrared (NIR) spectroscopy as it affects the spectral quality and classification. This research focuses on offering a solution to the challenge of classifying multilayer plastic film materials with a focus on polyolefin multilayer plastics. It presents the importance of spectral quality on accurate classification. The aim is to demonstrate the suitability of the handheld NIR spectrometer in classifying multilayer polyolefin films and assess the impact of various measuring backgrounds (white tile, Teflon, aluminum, copper, silver, and gold) on classification accuracy in the wavelength range of 1596-2396 nm. Metallic backgrounds have been found to enhance spectral quality and classification accuracy. The classification accuracy was consistently high, ranging from 96.55% to 100%, with aluminum and gold backgrounds yielding the best results in theoretical accuracy. In experimental classification, the accuracy reached 100% when any metallic backgrounds were used. Conversely, Teflon showed a theoretically high accuracy of 96.21% but only achieved an experimental accuracy of 72.2%. These findings suggest that using metallic backgrounds can improve the spectral quality and classification of plastics with low thickness (films) and complex material composition (multilayers).

In situ optical analytical spectroscopies offer great geochemical insights due to their capability to resolve the chemical composition of regolith surfaces of rocky celestial bodies. The use of suitable calibration targets improves the precision of mineral determination, which is of critical importance for short-living, low-mobility landers, and enables, in special cases, determination of elemental composition. We investigate the capabilities of three space-relevant optical analytical techniques used for in situ mineralogical analysis, i.e., mid-infrared reflection, Raman light scattering, and laser-induced plasma spectroscopies, to predict the chemical composition of olivine under a limited calibration input, namely using two bulk samples of natural olivine, chemically close to the end-members of the mineral group. We determine the accuracy of the forsterite numbers obtained with each technique and discuss the choice of calibration methods applicable to limited in situ calibration input, which are summarized in recommendations for space instrumentation.

This work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO${}_2$) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity. Fusing principal component scores from the two sensors yielded an order of magnitude improvement in precision and sensitivity of predictions made with an artificial neural network, compared to predictions made by models trained on independent sensor data. Lastly, a boosted ensemble trained on the fused spectral features yielded an ideal predictor with root-mean-squared error on the order of 10^-6 and calculated limit of detection order 10^-5 wt$\%$.