Search Results (376)

To prolong the service life of asphalt pavement and reduce its maintenance cost, a fiber Bragg grating (FBG) sensor encapsulated in carboxylated carbon nanotube (CNT-COOH)-modified gel material suitable for strain monitoring of asphalt pavement was developed. Through tensile and bending tests, the effects of carboxylated carbon nanotubes on the mechanical properties of gel materials under different dosages were evaluated and the optimal dosage of carbon nanotubes was determined. Infrared spectrometer and scanning electron microscopy were used to compare and analyze the infrared spectra and microstructure of carbon nanotubes before and after carboxyl functionalization and modified gel materials. The results show that the incorporation of CNTs-COOH increased the tensile strength, elongation at break, and tensile modulus of the gel material by 36.2%, 47%, and 17.2%, respectively, and increased the flexural strength, flexural modulus, and flexural strain by 89.7%, 7.5%, and 63.8%, respectively. Through infrared spectrum analysis, it was determined that carboxyl (COOH) and hydroxyl (OH) were successfully introduced on the surface of carbon nanotubes. By analyzing the microstructure, it can be seen that the carboxyl functionalization of CNTs improved the agglomeration of carbon nanotubes. The tensile section of the modified gel material is rougher than that of the pure epoxy resin, showing obvious plastic deformation, and the toughness is improved. According to the data from the calibration experiment, the strain and temperature sensitivity coefficients of the packaged sensor are 1.9864 pm/μm and 0.0383 nm/°C, respectively, which are 1.63 times and 3.61 times higher than those of the bare fiber grating. The results of an applicability study show that the internal structure strain of asphalt rutting specimen changed linearly with the external static load, and the fitting sensitivity is 0.0286 με/N. Combined with ANSYS finite element analysis, it is verified that the simulation analysis results are close to the measured data, which verifies the effectiveness and monitoring accuracy of the sensor. The dynamic load test results reflect the internal strain change trend of asphalt mixture under external rutting load, confirming that the encapsulated FBG sensor is suitable for the long-term monitoring of asphalt pavement strain. Full article

The inspection of encapsulated MEMS devices typically relies on destructive methods which compromise the structural integrity of samples. In this work, we present the concept and preliminary experimental validation of a laser scanning setup to non-destructively inspect silicon-encapsulated microstructures by measuring small variations of transmitted light intensity in the near-infrared spectrum. This method does not require any particular sample preparation or damage, and it is based on the higher degree of transparency of silicon in the near-infrared and the transmission contrast resulting from the Fresnel reflections observed at the interfaces between the different materials of the MEMS device layers. We characterise the small feature resolving performance of the laser scanning setup using standard targets, and experimentally demonstrate the inspection of a MEMS latching device enclosed within silicon covers, comparing the contrast measurements with theoretical predictions. Full article

This study represents a first step toward improving the repeatability, reproducibility, and accuracy of a process designed to enhance dynamic water structuring. We aim is to investigate the optical reflectivity of a watery magnesium chloride solution treated with electromagnetic waves, we employ a novel methodology derived from human plethysmography (PPG) with three wavelengths spanning the visible and infrared spectra. We measured the reflectance of 17 flasks at 536 nm, 660 nm, and 940 nm before and after treatment, first using the succussion method (control) and second using a 50 Hz signal. The observed variability was acceptable, with repeatability errors below 0.15% and reproducibility errors below 3.5% across all wavelengths before and after treatment. Out of 51 samples dynamically structured using the succussion method, we obtained two false negatives, while one false negative was recorded out of 51 samples dynamically structured using the electromagnetic (EM) method. PPG appears to be a relevant sensor, as it correctly detected dynamically structured water in 99 out of 102 cases, using either the succussion or electromagnetic method. Our results show significant differences in reflectance (supposedly correlated with water’s structured status) at 536 nm between dynamically structured and dynamic non-structured samples (p < 0.001). Future improvements will include a validation protocol against gold-standard spectrophotometry with a larger sample size. Full article

The reliance on deep learning models for sensor-based material classification amplifies the demand for labeled training data. However, acquiring large-scale, annotated spectral data for applications such as near-infrared (NIR) reflectance spectroscopy in plastic sorting remains a significant challenge due to high acquisition costs and environmental variability. This paper investigates the potential of large language models (LLMs) in synthetic spectral data generation. Specifically, it examines whether LLMs have acquired sufficient implicit knowledge to assist in generating spectral data and introduce meaningful variations that enhance model performance when used for data augmentation. Classification accuracy is reported exclusively as a proxy for structural plausibility of the augmented spectra; maximizing augmentation performance itself is not the study’s goal. From as little as one empirical mean spectrum per class, LLM-guided simulation produced data that enabled up to 86% accuracy, evidence that the generated variation preserves class-distinguishing information. While the approach performs best for spectral distinct polymers, overlapping classes remain challenging. Additionally, the transfer of optimized augmentation parameters to unseen classes indicates potential for generalization across material types. While plastic sorting serves as a case study, the methodology may be applicable to other domains such as agriculture or food quality assessment, where spectral data are limited. The study outlines a novel path toward scalable, AI-supported data augmentation in spectroscopy-based classification systems. Full article

The current study presents the results of a methodical investigation into the ionization of rare gas atoms, specifically focusing on argon. In this study, two configurations are examined: ionization via a near-infrared (NIR) laser field alone, and ionization caused by extreme ultraviolet (XUV) radiation in the presence of a strong, synchronized NIR pulse. The theoretical investigation is conducted using an ab initio method to solve the time-dependent Schrödinger equation within the single active electron (SAE) approximation. The simulation results show a sequence of above-threshold ionization (ATI) peaks that shift to lower energies with increasing laser intensity. This behavior reflects the onset of the Stark effect, which modifies atomic energy levels and increases the number of photons required for ionization. An examination of the two-color photoionization spectrum, which includes sideband structures and harmonic peaks, shows how the ionization probability is redistributed between the direct path (single XUV photon absorption) and sideband pathways (XUV ± n × IR) as the intensity of the infrared field increases. Quantum interference between continuum states is further revealed by the photoelectron angular distribution, clearly indicating the control of ionization dynamics by the IR field. Full article

This study presents a methodology for developing a cyclodextrin-based delivery system for ceftobiprole, a poorly water-soluble and amphoteric drug, chemically stable in acidic conditions. Ceftobiprole is a broad-spectrum cephalosporin antibiotic administered clinically as its water-soluble prodrug, ceftobiprole medocaril, due to limited aqueous solubility of the parent compound. Solubility enhancement was achieved through complexation with anionic sulfobutylether-β-cyclodextrin (SBE-β-CD). At a pH below 3, ceftobiprole is protonated and cationic, which facilitates electrostatic interactions with the anionic cyclodextrin. An optimised high-performance liquid chromatography (HPLC) method was used to assess solubility, the impurity profile, and long-term chemical stability. X-ray powder diffraction (XRPD) confirmed the amorphous nature of the system and the absence of recrystallization. Nuclear magnetic resonance (NMR) and attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy supported the formation of a host–guest complex. The freeze-dried system prepared from 0.1 M formic acid solution contained negligible residual acid due to nearly complete sublimation. The most promising formulation was a ternary system of ceftobiprole, maleic acid, and SBE-β-CD (1:25:4 molar ratio), showing ~300-fold solubility improvement, low levels of degradation products, and stability after eight months at −20 °C. After pH adjustment to a parenterally acceptable level, the formulation demonstrated solubility and a pH comparable to the marketed drug product. Full article

Plastic waste and pollution is growing rapidly worldwide and most plastics end up in landfill or are incinerated because high-quality recycling is not possible. Plastic-type identification with a low-cost, handheld spectral approach could help in parts of the world where high-end spectral imaging systems on conveyor belts cannot be implemented. Here, we investigate how two fundamentally different handheld infrared spectral devices can identify plastic types by benchmarking the same analysis against a high-resolution bench-top spectral approach. We used the handheld Plastic Scanner, which measures a discrete infrared spectrum using LED illumination at different wavelengths, and the SpectraPod, which has an integrated photonics chip which has varying responsivity in different channels in the near-infrared. We employ machine learning using SVM, XGBoost, Random Forest and Gaussian Naïve Bayes models on a full dataset of plastic samples of PET, HDPE, PVC, LDPE, PP and PS, with samples of varying shape, color and opacity, as measured with three different experimental approaches. The high-resolution spectral approach can obtain an accuracy (mean ± standard deviation) of (0.97 ± 0.01), whereas we obtain (0.93 ± 0.01) for the SpectraPod and (0.70 ± 0.03) for the Plastic Scanner. Differences of reflectance at subsequent wavelengths prove to be the most important features in the plastic-type classification model when using high-resolution spectroscopy, which is not possible with the other two devices. Lower accuracy for the handheld devices is caused by their limitations, as the spectral range of both devices is limited—up to 1600 nm for the SpectraPod, while the Plastic Scanner has limited sensitivity to reflectance at wavelengths of 1100 and 1350 nm, where certain plastic types show characteristic absorbance bands. We suggest that combining selective sensitivity channels (as in the SpectraPod) and illuminating the sample with varying LEDs (as with the Plastic Scanner) could increase the accuracy in plastic-type identification with a handheld device. Full article

Recent research in agricultural remote sensing mainly focuses on how soil background affects canopy reflectance and the inversion of LAI, while often overlooking the influence of the weed layer. The coexistence of crop and weed layers forms two-layered vegetation canopies in tall crops such as sugarcane and maize. Although radiative transfer models can simulate the weed layer’s influence on canopy reflectance and LAI inversion, few experimental investigations use in situ measurement data to verify these effects. Here, we propose a practical background modification scheme in which black material with near-zero reflectance covers the weed layer and alters the background spectrum of crop canopies. We conduct an experimental investigation in a sugarcane field with different background properties (i.e., bare soil and a weed layer). Tower-based and UAV-based hyperspectral measurements examine the spectral differences in sugarcane canopies with and without the black covering. We then use LAI measurements to evaluate the weed layer’s impact on LAI inversion from UAV-based hyperspectral data through a hybrid inversion method. We find that the weed layer significantly affects the canopy reflectance spectrum, changing it by 13.58% and 42.53% in the near-infrared region for tower-based and UAV-based measurements, respectively. Furthermore, the weed layer substantially interferes with LAI inversion of sugarcane canopies, causing significant overestimation. Estimated LAIs of sugarcane canopies with a soil background generally align well with measured values (root mean square error (RMSE) = 0.69 m²/m²), whereas those with a weed background are considerably overestimated (RMSE = 2.07 m²/m²). We suggest that this practical background modification scheme quantifies the weed layer’s influence on crop canopy reflectance from a measurement perspective and that the weed layer should be considered during the inversion of crop LAI. Full article

Background/Objectives: Serum diagnostic tests for alopecia areata may be used to monitor response to treatment, aiding in the objective assessment of disease activity and helping to change treatment at an earlier point. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy offers a nondestructive and user-friendly approach for analyzing a wide range of samples. In this study, we evaluated whether ATR-FTIR spectroscopy combined with machine learning can detect alopecia areata and quantify disease activity. We also established whether patient-specific spectral differences correlate with response to therapy, offering molecular insight into treatment response. Methods: Serum samples from 42 patients with alopecia areata and 41 healthy donors were compared. Logistic regression models were developed to separate alopecia areata patients from controls and to monitor treatment response based on clinical scoring. Results: Significant spectral variations were found in the 3000–2800 cm⁻¹ and 1800–1000 cm⁻¹ regions corresponding to the principal biochemical constituents such as proteins, lipids, carbohydrates, and nucleic acids. The AUC of the logistic regression model for distinguishing alopecia areata patients from healthy controls was 0.85 (95% CI: 0.75–0.94) with a sensitivity of 0.89 and a specificity of 0.71. In terms of prediction of treatment response, the model showed discriminative potential (AUC = 0.86, 95% CI: 0.71–0.98), with distinct alterations in the spectrum, particularly in the Amide I band, associated with improvement in the patient’s condition. Conclusions: ATR-FTIR spectroscopy assisted by machine learning offers a serum-based solution for treatment monitoring in alopecia areata patients with clinical applicability. This technique has highly promising potential for the development of rapid, non-invasive, and objective biomarkers in autoimmune dermatology. Additional multi-center trials are required to validate and incorporate these spectral biomarkers into individual treatment regimens. Full article

The multi-band atmospheric correction algorithms, now referred to as remote sensing reflectance (R_rs) algorithms, have been implemented on a NASA computing facility for global remote sensing of ocean color and atmospheric aerosol parameters from data acquired with several satellite instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi spacecraft platform. These algorithms are based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. The bands centered near 0.75 and 0.865 μm are used for atmospheric corrections. In order to obtain high-quality R_rs values over Case 1 waters (deep clear ocean waters), strict masking criteria are implemented inside these algorithms to mask out thin clouds and very turbid water pixels. As a result, R_rs values are often not retrieved over bright Case 2 waters. Through our analysis of VIIRS data, we have found that spatial features of bright Case 2 waters are observed in VIIRS visible band images contaminated by thin cirrus clouds. In this article, we describe methods of combining cirrus and aerosol corrections to improve spatial coverage in R_rs retrievals over Case 2 waters. One method is to remove cirrus cloud effects using our previously developed operational VIIRS cirrus reflectance algorithm and then to perform atmospheric corrections with our updated version of the spectrum-matching algorithm, which uses shortwave IR (SWIR) bands above 1 μm for retrieving atmospheric aerosol parameters and extrapolates the aerosol parameters to the visible region to retrieve water-leaving reflectances of VIIRS visible bands. Another method is to remove the cirrus effect first and then make empirical atmospheric and sun glint corrections for water-leaving reflectance retrievals. The two methods produce comparable retrieved results, but the second method is about 20 times faster than the spectrum-matching method. We compare our retrieved results with those obtained from the NASA VIIRS R_rs algorithm. We will show that the assumption of zero water-leaving reflectance for the VIIRS band centered at 0.75 μm (M6) over Case 2 waters with the NASA R_rs algorithm can sometimes result in slight underestimates of water-leaving reflectances of visible bands over Case 2 waters, where the M6 band water-leaving reflectances are actually not equal to zero. We will also show conclusively that the assumption of thin cirrus clouds as ‘white’ aerosols during atmospheric correction processes results in overestimates of aerosol optical thicknesses and underestimates of aerosol Ångström coefficients. Full article

Long-wave infrared (LWIR) broadband absorption is of great significance in science and technology. The electromagnetic field energy is absorbed by the metamaterials material, leading to the enhanced light absorption, from which the Metal–Dielectric–Metal (MDM) structure is designed. FDTD simulation calculation indicate that the bandwidth within which the absorber absorption ratio greater than 90% is 11.04 μm, and the average absorption rate (9.10~20.14 μm) is 93.6%, which can be accounted for by the impedance matching theory. Upon the matching of the impedance of the metamaterial absorber with the impedance of the incident light, the light reflection is reduced to a minimum, and increase the absorption ratio. Meanwhile, the good incidence angle unsensitivity due to the metasurface structural symmetry and the characteristics of the electromagnetic field distribution at different incidence angles. Due to the form regularity of the nanoring–nanowire metasurface structure, the light acts similar in different polarization directions, and the surface plasmon resonance plays a key role. Using FDTD electromagnetic field analysis to visualize the electric field and magnetic field strength distribution within the absorber, the electromagnetic field at the interface in the nanoring–nanowire metasurface structure, promote the surface plasmon resonance and interaction with damaged materials, and improve the light absorption efficiency. Moreover, the different microstructures and the electrical and optical properties of different top materials affect the light absorption. Meanwhile, adjusting the absorption layer thickness and periodic geometry parameters will also change the absorption spectrum. The absorber has high practical value in thermal electronic devices, infrared imaging, and thermal detection. Full article

A new hyphenated technique using thin-layer chromatography (TLC) to separate analytes in mixtures, coupled with mid-infrared (MIR) laser spectroscopy for identification and quantification, is presented. The method, which provides a means for rapid screening of analytes that is practical, low-cost, fast, robust, and reproducible, was tested using nitroaromatic and aliphatic nitro high explosives (HEs) as target analytes. HEs are anthropogenic contaminants containing an -NO₂ group. For validation of the new technique, a direct comparison of the 2,4,6-trinitrotoluene (TNT) spectrum, obtained by attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with TLC, was carried out. The MIR laser spectroscopy-based method was evaluated by calculating the analytical figures of merit regarding the calibration curves’ linearity and the method’s sensitivity and precision. The TNT spectrum obtained by the MIR laser method showed two prominent and characteristic bands of the explosive at approximately 1350 cm⁻¹ and 1550 cm⁻¹ compared to the spectrum acquired by ATR-FTIR. The detection limit calculated for TNT was 84 ng, while the quantification limit was 252 ng. Multivariate analysis was used to evaluate the spectroscopic data to identify sources of variation and determine their relation. Partial least squares (PLS) regression analysis and PLS combined with discriminant analysis (PLS-DA) were used for quantification and classification. The new technique, TLC-QCL, is amenable to a smaller footprint with further developments in MIR laser technology, making it portable for fieldwork. Full article

With the rapid development of multispectral detection technology, realizing the synergistic camouflage and thermal management of materials in multi-band has become a major challenge. In this paper, a multifunctional radiation-selective hierarchical metamaterial (RSHM) is designed to realize the modulation of optical properties in a wide spectral range through the delicate design of microstructures and nanostructures. In the atmospheric windows of 3–5 μm and 8–14 μm, the emissivity of the material is as low as 0.14 and 0.25, which can effectively suppress the radiation characteristics of the target in the infrared band, thus realizing efficient infrared stealth. Simultaneously, it exhibits high emissivity in the 2.5–3 μm (up to 0.80) and 5–8 μm (up to 0.98) bands, significantly improving thermal radiation efficiency and enabling active thermal management. Notably, RSHM achieves low reflectivity at 1.06 μm (0.13) and 1.55 μm (0.005) laser wavelengths, as well as in the 8–14 μm (0.06) band, substantially improving laser stealth performances. Additionally, it maintains high transmittance in the visible light range, ensuring excellent visual camouflage effects. Furthermore, the RSHM demonstrates exceptional incident angle and polarization stability, maintaining robust performances even under complex detection conditions. This design is easy to expand relative to other frequency bands of the electromagnetic spectrum and holds significant potential for applications in military camouflage, energy-efficient buildings, and optical devices. Full article

The harmful effects of solar radiation on the skin are known and scientifically proven, with recent studies indicating that not only ultraviolet (UV) radiation but also infrared (IR) radiation contributes to skin photoaging and increases the risk of carcinogenesis. Infrared radiation is also responsible for the degradation of protective carotenoids in the skin, the disruption of calcium homeostasis, and the activation of apoptosis pathways. The biological mechanisms underlying these effects include an increased level of reactive oxygen species and increased expression of metalloproteinases in the skin. The aim of this study was to evaluate the photoprotective properties of 10 cold-pressed plant oils in the infrared spectral range from 1000 nm to 2500 nm by assessing their impact on the directional–hemispherical reflectance (DHR) of human skin after their topical application. This study was conducted in vivo on the skin of 12 volunteers, with non-invasive DHR measurements taken before and directly after the application of the oil and 30 min later. Additionally, the correlation between the oil’s compounds (chlorophyll a, chlorophyll b, lycopene, and β-carotene) and antioxidant activity, expressed as the DPPH free radical scavenging capacity, was analyzed in relation to the differences in the skin’s DHR observed. An interesting result was obtained in the context of protecting the skin against IR radiation. A statistically significant increase in the skin’s reflectance after the penetration of the oil (p < 0.05) was observed in the 1700–2500 nm range for the chokeberry, fig, pomegranate, and perilla oils, suggesting their potential as photoprotective agents against IR. These findings indicate that chokeberry, fig, pomegranate, and perilla oils may serve as ingredients in cosmetic formulations designed for broad-spectrum skin photoprotection, complementing traditional UV filters with additional protection against infrared radiation. However, further research is needed to confirm these findings in a larger population. Full article

Cortisol, known as the “stress hormone”, is vital for stress response, metabolism regulation, and immune function, and salivary cortisone reflects serum cortisol levels. The measurement of salivary cortisone levels has been proposed as an effective alternative method for estimating serum cortisol levels. Objective: This study aimed to evaluate the use of Fourier Transform Infrared Spectroscopy (FTIR) for salivary cortisone identification and quantification and to assess the impact of adding the surfactant TWEEN 80 to the analysis. Methods: Initially, cortisone was diluted in chloroform and methanol (5,000,000 µg/dL). FTIR spectra were obtained, and absorbance characteristics and peaks were identified. The spectrum of this initial dilution was processed using the Savitzky-Golay filter to evaluate peak heights at 1655 cm⁻¹ and 1700 cm⁻¹, and the effect of signal processing on these peaks was assessed. Additionally, two series of dilutions were performed by adding the surfactant TWEEN 80 at two different concentrations, and the effect of the surfactant on the cortisone spectra was evaluated to reduce noise and enhance the signal. Results: The spectra obtained from the cortisone solution were similar to those found in the literature for solid samples. The peak corresponding to the wavenumber range of 1600–1680 cm⁻¹, related to the stretching bands of C=C, was found to be reliable for use in cortisone quantification studies. The standard deviation between the spectra of the same sample was less than 0.01. It was not possible to detect cortisone when TWEEN 80 was added; however, with signal processing, TWEEN 80 could be detected in quantities as low as 0.0033% of the solution. Conclusions: FTIR demonstrates potential as a non-invasive method for cortisone analysis. While Tween 80 aids in the dilution of cortisone in water, it obscures its spectrum. Full article