Search Results (487)

To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and extrinsic camera parameters accurately. Based on the pinhole imaging model, disparity maps were obtained via pixel matching to reconstruct high-precision 3D ablation morphology. A mathematical model was established to analyze how key imaging parameters—baseline distance, focal length, and depth of field—affect reconstruction accuracy in micro-imaging environments. Focusing on trace element detection in WC-Co alloy samples, the reconstructed ablation craters enabled the precise calculation of ablation volumes and revealed their correlations with laser parameters (energy, wavelength, pulse duration) and the physical-chemical properties of the samples. Multivariate regression analysis was employed to investigate how ablation morphology and plasma evolution jointly influence LIBS quantification. A nonlinear calibration model was proposed, significantly suppressing matrix effects, achieving R² = 0.987, and reducing RMSE to 0.1. This approach enhances micro-scale LIBS accuracy and provides a methodological reference for high-precision spectral analysis in environmental and materials applications. Full article

Highly integrable silicon carbide (SiC) has emerged as a promising platform for photonic integrated circuits (PICs), offering a comprehensive set of material and optical properties that are ideal for the integration of nonlinear devices and solid-state quantum defects. However, despite significant progress in nanofabrication technology, the development of SiC on an insulator (SiCOI)-based photonics faces challenges due to fabrication-induced material optical losses and complex processing steps. An alternative approach to mitigate these fabrication challenges is the direct deposition of amorphous SiC on an insulator (a-SiCOI). However, there is a lack of systematic studies aimed at producing high optical quality a-SiC thin films, and correspondingly, on evaluating and determining their optical properties in the telecom range. To this end, we have studied a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C₃H₁₂Si₃), and chemical vapor deposition (CVD) processes for the deposition of SiC thin films in a low-temperature range (650–800 °C) on a multitude of different substrates. We have successfully demonstrated the fabrication of smooth, uniform, and stoichiometric a-SiCOI thin films of 20 nm to 600 nm with a highly controlled growth rate of ~0.5 Å/s and minimal surface roughness of ~5 Å. Spectroscopic ellipsometry and resonant micro-photoluminescence excitation spectroscopy and mapping reveal a high index of refraction (~2.7) and a minimal absorption coefficient (<200 cm⁻¹) in the telecom C-band, demonstrating the high optical quality of the films. These findings establish a strong foundation for scalable production of high-quality a-SiCOI thin films, enabling their application in advanced chip-scale telecom PIC technologies. Full article

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Full article

Cardiac fibrosis following myocardial infarction plays a critical role in the formation of scar tissue and contributes to ventricular arrhythmias, including ventricular tachycardia and sudden cardiac death. Current clinical diagnostics use electrical and structural markers, but lack precision due to low spatial resolution and absence of molecular information. In this paper, we employed line scan Raman microspectroscopy to classify sheep myocardial tissue into muscle, necrotic, granulated, and fibrotic tissue types, using collagen as a molecular biomarker. Three spectral regions were evaluated: region A (600–2960 cm⁻¹), region B (600–1399 cm⁻¹ and 1751–2960 cm⁻¹), and region C (1400–1750 cm⁻¹), which includes the prominent collagen-associated peaks at 1448 cm⁻¹ and 1652 cm⁻¹. Linear and nonlinear principal component analysis (PCA) and support vector machines (SVMs) were applied for dimensionality reduction and classification, with nonlinear models specifically addressing the nonlinearity of collagen formation during fibrogenesis. Histological validation was performed using Masson’s trichrome staining. Raman bands associated with collagen in region C consistently outperformed regions A and B, achieving the highest explained variance and best class separation in both binary and multiclass PCA models for both linear and nonlinear approaches. The ratio of collagen-related peaks enabled stage-dependent tissue characterization, confirming the nonlinear nature of fibrotic remodeling. Our findings highlight the diagnostic potential of collagen-associated Raman bands for characterizing myocardial fibrosis. The proposed PCA-SVM framework demonstrates robust performance even with limited sample size and has the potential to lay the foundation for real-time intraoperative diagnostics. Full article

Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Full article

The design of emulsions at the nanoscale is a significant application of nanotechnology. For spherical droplets and a given volume of dispersed phase, the nanometre size of droplets inversely increases the total area, $A={\displaystyle \frac{3V}{r}}$, allowing greater contact with organic and inorganic materials during application. In topical applications, not only is cell contact increased, but also permeability in the cell membrane. Nanoemulsions typically achieve kinetic stability rather than thermodynamic stability, so their commercial application requires reasonable resistance to flocculation and coalescence, which can be affected by temperature changes. Therefore, their thermoresponsive characterisation becomes relevant. In this work, we analyse this response in an O/W nanoemulsion of Palmarosa for antibacterial purposes that has already shown stability for one year at controlled room temperature. We now study hysteresis processes and the behaviour of the statistical distribution in droplet size by Dynamic Light Scattering, obtaining remarkable stability under temperature changes up to 50 °C. This includes a maintained chemical composition observed using Fourier Transform Infrared Spectroscopy and the preservation of antibacterial properties analysed through optical density tests on cultures and the Spread-Plate technique for bacteria colony counting. We obtain practically closed hysteresis curves for some tracers of droplet size distributions through controlled thermal cycles between 10 °C and 50 °C, exhibiting a non-linear behaviour in their distribution. In general, the results show notable physical, chemical, and antibacterial stability, suitable for commercial applications. Full article

Laser-induced breakdown spectroscopy (LIBS) has played a critical role in Mars exploration missions, substantially contributing to the geochemical analysis of Martian surface substances. However, the complex nonlinearity of LIBS processes can considerably limit the quantification accuracy of conventional LIBS chemometric methods. Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. The hidden layer activation functions are key to ANN model performance, yet common activation functions usually suffer from problems such as gradient vanishing (e.g., Sigmoid and Tanh) and dying neurons (e.g., ReLU). In this study, we propose a novel LIBS quantification method, named the Bayesian optimization-based tunable Softplus backpropagation neural network (BOTS-BPNN). Based on a dataset comprising 1800 LIBS spectra collected by a laboratory duplicate of the MarSCoDe instrument onboard the Zhurong Mars rover, we have revealed that a BPNN model adopting a tunable Softplus activation function can achieve higher prediction accuracy than BPNN models adopting other common activation functions if the tunable Softplus parameter β is properly selected. Moreover, the way to find the proper β value has also been investigated. We demonstrate that the Bayesian optimization method surpasses the traditional grid search method regarding both performance and efficiency. The BOTS-BPNN model also shows superior performance over other common machine learning models like random forest (RF). This work indicates the potential of BOTS-BPNN as an effective chemometric method for analyzing Mars in situ LIBS data and sheds light on the use of chemometrics for data analysis in future planetary explorations. Full article

In this study, natural kaolin was modified with hexadecyltrimethylammonium bromide (HDTMA-Br) at two levels corresponding to 50% and 90% of its cation exchange capacity. The resulting materials, designated as HKR-50 and HKR-90, were used as adsorbents for the mycotoxins ochratoxin A (OCHRA) and zearalenone (ZEN). The characterization of the HKRs with several methods (X-ray diffraction, DRIFT spectroscopy, thermal analysis (DTA/TG), SEM, zeta potential measurements, and the determination of the point of zero charge and textural properties) confirmed the presence of surfactant ions on the organokaolinites’ surfaces. The adsorption of ZEN and OCHRA by HKRs followed nonlinear adsorption isotherms, suggesting a complex adsorption mechanism. The adsorption capacities of ZEN and OCHRA were similar for HKR-50 and HKR-90 at pH 3, with higher adsorption observed for ZEN (~13.0 mg/g for HKR-50 and HKR-90 for ZEN and ~8.0 mg/g for HKR-50 and HKR-90 for OCHRA). At pH 7, the adsorption of ZEN and OCHRA was lower than at pH 3, especially for OCHRA, but slightly increased with increased amounts of surfactant on the kaolinite surface (8.5 mg/g for HKR-50 and 10.8 mg/g for HKR-90 for ZEN and 2.6 mg/g for HKR-50 and 4.1 mg/g for HKR-90 for OCHRA). Special attention was paid to the safety assessment of the natural kaolin and HKR-90, and toxicological tests confirmed the safety of both materials, as no adverse effects were observed in rats. Full article

LiTaO₃ crystals doped with Cr³⁺ and Nd³⁺ ions are promising for developing active nonlinear laser media. In this work, the defect structure of LiTaO₃ crystals, including those doped with Cr³⁺ and Nd³⁺, is examined. X-ray patterns of all six investigated LiTaO₃:Cr:Nd crystals are identical and correspond to a highly perfect structure. Using optical microscopy, the presence of defects of various shapes, microinhomogeneities, and lacunae was revealed. The optical absorption and Raman scattering spectra of a series of nonlinear, optical, double-doped LiTaO₃:Cr³⁺:Nd³⁺ (0.06 ≤ [Cr³⁺] ≤ 0.2; 0.2 ≤ [Nd³⁺] ≤ 0.45 wt%) crystals showed that at concentrations of doping Cr³⁺ ions less than 0.09 wt% and Nd³⁺ ions less than 0.25 wt%, the crystal structure is characterized by a low level of defects, and the optical transmission spectra characterized by narrow lines corresponding to electron transitions in Nd³⁺ ions. In this case, for the radiative transition in the cation sublattice, the existence of three nonequivalent neodymium centers is observed, and for the radiative transition, two nonequivalent centers are observed. IR absorption spectroscopy in the OH⁻-stretching vibration range revealed two main spectral regions: 3463–3465 cm⁻¹, associated with stoichiometry changes, and 3486–3490 cm⁻¹, linked to complex defects such as (V^-_Li)-OH and (Ta⁴⁺_Li)-OH. A distinct low-intensity line at ~3504 cm⁻¹ was observed only in doped crystals, attributed to (Nd²⁺_Li)-OH defects that significantly distort the oxygen-octahedral clusters due to the larger ionic radius of Nd³⁺ compared to Ta⁵⁺. In contrast, Cr-related defects cause only minor distortions. The Klauer method indicated that the highest concentration of OH⁻-groups occurs in the LiTaO₃:Cr³⁺ (0.09 wt%):Nd³⁺ (0.25 wt%) crystal, where multiple complex defects are present. Full article

Visible and near-infrared (Vis–NIR) spectroscopy enables the rapid prediction of soil properties but faces three limitations with conventional machine learning: information loss and overfitting from high-dimensional spectral features; inadequate modeling of nonlinear soil–spectra relationships; and failure to integrate multi-scale spatial features. To address these challenges, we propose ReSE-AP Net, a multi-scale attention residual network with spatial pyramid pooling. Built on convolutional residual blocks, the model incorporates a squeeze-and-excitation channel attention mechanism to recalibrate feature weights and an atrous spatial pyramid pooling (ASPP) module to extract multi-resolution spectral features. This architecture synergistically represents weak absorption peaks (400–1000 nm) and broad spectral bands (1000–2500 nm), overcoming single-scale modeling limitations. Validation on the LUCAS2009 dataset demonstrated that ReSE-AP Net outperformed conventional machine learning by improving the R² by 2.8–36.5% and reducing the RMSE by 14.2–69.2%. Compared with existing deep learning methods, it increased the R² by 0.4–25.5% for clay, silt, sand, organic carbon, calcium carbonate, and phosphorus predictions, and decreased the RMSE by 0.7–39.0%. Our contributions include statistical analysis of LUCAS2009 spectra, identification of conventional method limitations, development of the ReSE-AP Net model, ablation studies, and comprehensive comparisons with alternative approaches. Full article

We demonstrate a novel bidirectional mode-locked ultrafast fiber laser based on an asymmetric dual-cavity architecture that enables freely tunable repetition rate differentials at the kilohertz level, while maintaining inherent common-mode noise suppression through precision thermomechanical stabilization. Through cascaded amplification and nonlinear temporal compression, we obtained bidirectional pulse durations of 33.2 fs (clockwise) and 61.6 fs (counterclockwise), respectively. The developed source demonstrates exceptional capability for asynchronous optical sampling applications, particularly in enabling the compact implementation of real-time measurement systems such as terahertz time-domain spectroscopy (THz-TDS) systems. Full article

This fundamental understanding of molecular structure–NLO property relationships provides critical design principles for next-generation optical limiting materials, quantum photonic devices, and ultrafast nonlinear optical switches, addressing the growing demand for high-performance organic optoelectronic materials in laser protection and photonic computing applications. In this study, it was observed that selenophene-incorporated fused D-A-D architectures exhibit a remarkable enhancement in two-photon absorption characteristics. By strategically modifying the heteroatomic composition of the Y6-derived fused-ring core, replacing thiophene (BDS) with selenophene (BDSe), the optimized system achieves unprecedented NLO performance. BDSe displays a nonlinear absorption coefficient (β) of 3.32 × 10⁻¹⁰ m/W and an effective two-photon absorption cross-section (σ_TPA) of 2428.2 GM under 532 nm with ns pulse excitation. Comprehensive characterization combining Z-scan measurements, transient absorption spectroscopy, and DFT calculations reveals that the heavy atom effect of selenium induces enhanced spin–orbit coupling, optimized intramolecular charge transfer dynamics and stabilized excited states, collectively contributing to the superior reverse saturable absorption behavior. It is believed that this molecular engineering strategy establishes critical structure–property relationships for the rational design of organic NLO materials. Full article

Water ingress and penetration of aggressive fluids undermines the integrity of many concrete structures. For this reason, optimal performance of such structures up to their designed life cannot be guaranteed. This study introduces nano silicon as an alternative waterproofing admixture for increasing life span of cementitious materials, due to its non-vulnerability to deterioration, which is common to traditional surface coating solutions. Therefore, nano silicon was characterized using Field Emission Scanning Electron Microscope (FESEM), Energy Dispersion Spectroscopy (EDS), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), and surface Zeta potential. The Central Composite Design (CCD) tool was adopted to plan the experiment and further used to model the relationship between experimental variables and experimental response. The model was found to be nonlinear quadratic based on Analysis of Variance (ANOVA). Also, the validity of the model was evaluated and found to have accurate prediction with mean absolute percentage error (MAPE) of 1.62%. The optimum mix ratio necessary to increase resistance to capillary water absorption was established at a nano silicon dosage of 6.6% by weight of cement and w/c of 0.42. In conclusion, the overall results indicate that resistance to capillary water absorption was increased by 62%. Furthermore, while gas permeability was reduced by 31%, on the other hand, volume of water permeable voids decreased by 10%. Full article

This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D₁–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D₁–RF reached 5.9 for protein, and CARS–SG + D₂–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications. Full article

We report on the preparation and characterization of a new family of hydrogen-bonded nematogenic liquid crystalline dimers. The dimers are supramolecular complexes that consist of a benzoic acid derivative, acting as the proton donor, featuring a spacer with seven methylene groups and a terminal decyloxy chain, paired with an azopyridine derivative as the proton acceptor. The latter was either fluorinated or nonfluorinated with variable alkoxy chain length. The formation of a hydrogen bond between the individual components was confirmed using FTIR and ¹H NMR spectroscopy. All supramolecules were investigated for their liquid crystalline behaviour via a polarized optical microscope (POM) and differential scanning calorimetry (DSC). All materials exhibit enantiotropic nematic phases as confirmed by X-ray diffraction (XRD) and POM investigations. The nematic phase range depends strongly on the degree and position of fluorine atoms. Additionally, the supramolecules demonstrated a rapid and reversible transition between the liquid crystal phase and the isotropic liquid state because of trans-cis photoisomerization upon light irradiation. Therefore, this study presents a straightforward approach to design photo-responsive nematic materials, which could be of interest for nonlinear optics applications. Full article