Search Results (16,040)

Photoacoustics can provide a direct measurement of light absorption by microalgae depending on the photosynthesis pigment within them. In this study, we have performed photoacoustic flowmetry on living microalgae cells to measure their flow characteristics, which include flow speed, flow angle, flow direction, and, more importantly, the photoacoustic absorption spectrum, all by observing the photoacoustic Doppler power spectra during their flowing state. A supercontinuum pulsed laser with a high repetition frequency is used as the light source: through intensity modulation at a specified frequency, it can provide wavelength-selectable excitation of a photoacoustic signal centered around this frequency. Our approach can be useful to simultaneously measure the flow characteristics of microalgae and easily discriminate their different species with high accuracy in both static and dynamic states, thus facilitating the study of their cultivation and their role in our ecosystem. Full article

Coffee leaf rust (CLR), a fungal disease caused by Hemileia vastatrix, represents Peru’s most significant threat to coffee production. The CLR epidemic (2012–2013) led Peru to implement an emergency plan under which coffee plantations underwent renewal using rust-resistant varieties derived from a Timor hybrid (HDT; Coffea arabica × canephora hybrid) like Catimors. Nevertheless, new pathogenic rust races capable of infecting these varieties have been recorded. Eighteen rust samples from coffee genotypes, such as Caturra, Typica, and Catimor, were collected in various Peruvian regions and sent to CIFC/ISA/UL (Centro de Investigação das Ferrugens do Cafeeiro/Instituto Superior de Agronomia/Universidade de Lisboa) in Portugal for race characterization. Assessing the virulence spectra of rust samples on a set of 27 coffee differentials resulted in the identification of 5 known and 2 new races. This study emphasizes the significance of conducting surveys on the diversity of H. vastatrix races in Peru for effective disease management. Moreover, Catimor lines, widely cultivated in coffee-growing countries, are susceptible to the 2 new races and to races XXXIV and XXXV identified in this study. Thus, coffee farmers need to know the resistance spectrum of new varieties before introducing them to CLR-affected regions. Full article

The geometries and vibrational frequencies of various configurations of XO₄^m−(H₂O)_n, X = Fe, Ru, Os, m = 0; X = Mn, Tc, Re, m = 1; X = Cr, Mo, W, m = 2; and X = Nb, Ta, m = 3; n = 0–6 are calculated at various levels up to MP2/6-31+G* and B3LYP/6-31+G*. These properties are studied as a function of increasing cluster size. The experimental and theoretical bond distances and vibrational spectra are compared where available, and predictions are made where they are not. Full article

Color stability is crucial for dental materials to ensure they perfectly match a patient’s tooth color. This is particularly challenging in photoresist-based additive manufacturing. Although some studies have addressed this issue, the exact causes of discoloration and ways to minimize it remain unclear. In this study, the intrinsic causes of discoloration in materials intended for 3D printing are investigated by examining thin-film samples (1200 µm) of various compositions, which are stored under different conditions. The samples are evaluated by measuring the UV-Vis absorption spectra at regular intervals to monitor changes. The findings reveal that both the composition of the formulations and the storage conditions significantly influence the discoloration behavior. Furthermore, methods have been developed to reduce or completely prevent discoloration. The use of photoinitiators with sterically demanding benzoyl moieties, as well as the addition of stabilizers, effectively decreases the intensity of emerging discoloration. Furthermore, incorporating the oxidizing agent cumene hydroperoxide (CHP) results in materials that maintain color stability. Full article

Wetlands, as a crucial component of terrestrial ecosystems, play a significant role in global ecological services. Aboveground biomass (AGB) is a key indicator of the productivity and carbon sequestration potential of wetland ecosystems. The current research methods for remote-sensing estimation of biomass either rely on traditional vegetation indices or merely perform integer-order differential transformations on the spectra, failing to fully leverage the information complexity of hyperspectral data. To identify an effective method for estimating AGB of mixed-wetland-plant communities, we conducted field surveys of AGB from three typical wetlands within the Crested Ibis National Nature Reserve in Hanzhong, Shaanxi, and concurrently acquired canopy hyperspectral data with a portable spectrometer. The spectral features were transformed by applying fractional-order differentiation (0.0 to 2.0) to extract optimal feature combinations. AGB prediction models were built using three machine learning models, XGBoost, Random Forest (RF), and CatBoost, and the accuracy of each model was evaluated. The combination of fractional-order differentiation, vegetation indices, and feature importance effectively yielded the optimal feature combinations, and integrating vegetation indices with feature bands enhanced the predictive accuracy of the models. Among the three machine-learning models, the RF model achieved superior accuracy using the 0.8-order differential transformation of vegetation indices and feature bands (R² = 0.673, RMSE = 23.196, RPD = 1.736). The optimal RF model was visually interpreted using Shapley Additive Explanations, which revealed that the contribution of each feature varied across individual sample predictions. Our study provides methodological and technical support for remote-sensing monitoring of wetland AGB. Full article

We investigate the effects of the magnetostatic ($B$) field topology on the plasma behavior in a 2D collisionless simulation setup that represents an axial–azimuthal cross-section of a Hall thruster. The influence of the $B$-field topology is assessed in terms of two principal design properties of the field in a typical Hall thruster, i.e., the field’s peak intensity along the axial direction, and the field’s axial distribution. The effects of the field’s intensity are investigated for three propellants—xenon, krypton, and argon. Whereas, the effects of the axial profile of the magnetic field are studied only for the xenon propellant as an example. We primarily aim to understand how the changes in the $B$-field topology affect the spectra of the resolved instabilities as well as the electrons’ transport characteristics and the contributions of various momentum terms to transport. The numerical observations on the instabilities’ characteristics are compared against the relevant existing theories to determine the extent to which the simulated and the theoretically predicted characteristics are consistent across the studied parameter space. It was, most notably, found that modes related to ion acoustic instability are dominantly present across the simulation cases. The ion transit time instability additionally develops at the highest $B$-field intensities as a long-wavelength structure. The main influence of the axial profile of the $B$ field on the plasma discharge was observed to be in terms of the electrons’ transport characteristics. Where possible, the insights from the simulations are discussed with respect to the relevant experimental observations available in the literature. Full article

Mid-infrared spectral (MIR) patterns of serum and synovial fluid (SF) are candidate biomarkers of osteoarthritis (OA). The impact of long-term storage on MIR spectral patterns was previously unknown. MIR spectra of canine serum (52 knee-OA, 49 control) and SF (51 knee-OA, 51 control) were obtained after short-term and long-term storage in −80 °C. Multilevel simultaneous component analysis and partial least squares discriminant analysis were used to evaluate the effect of time and compare the performance of predictive models for discriminating OA from controls. The median interval of storage between sample measurements was 5.7 years. Spectra obtained at two time points were significantly different (p < 0.0001); however, sample aging accounted for only 1.61% and 2.98% of the serum and SF profiles’ variability, respectively. Predictive models for discriminating serum of OA from controls for short-term storage showed 87.3 ± 3.7% sensitivity, 88.9 ± 2.4% specificity, and 88.1 ± 2.3% accuracy, while for long-term storage, they were 92.5 ± 2.6%, 97.1 ± 1.7%, and 94.8 ± 1.4%, respectively. Predictive models of short-term stored SF spectra had 97.3 ± 1.6% sensitivity, 89.4 ± 2.6% specificity, and 93.4 ± 1.6% accuracy, while for long-term storage they were 95.7 ± 2.1%, 95.7 ± 0.8%, and 95.8 ± 1.1%, respectively. Long-term storage of serum and SF resulted in significant differences in MIR spectral variables without significantly altering the performance of predictive algorithms for discriminating OA from controls. Full article

Converting carbon dioxide (CO₂) into high-value-added chemicals using solar energy is a promising approach to reducing carbon dioxide emissions; however, single photocatalysts suffer from quick the recombination of photogenerated electron–hole pairs and poor photoredox ability. Herein, silver (Ag) nanoparticles featuring with localized surface plasmon resonance (LSPR) are combined with g-C₃N₄ to form a Schottky junction for photothermal catalytic CO₂ reduction. The Ag/g-C₃N₄ exhibits higher photocatalytic CO₂ reduction activity under UV-vis light; the CH₄ and CO evolution rates are 10.44 and 88.79 µmol·h⁻¹·g⁻¹, respectively. Enhanced photocatalytic CO₂ reduction performances are attributed to efficient hot electron transfer in the Ag/g-C₃N₄ Schottky junction. LSPR-induced hot electrons from Ag nanoparticles improve the local reaction temperature and promote the separation and transfer of photogenerated electron–hole pairs. The charge carrier transfer route was investigated by in situ irradiated X-ray photoelectron spectroscopy (XPS). The three-dimensional finite-difference time-domain (3D-FDTD) method verified the strong electromagnetic field at the interface between Ag and g-C₃N₄. The photothermal catalytic CO₂ reduction pathway of Ag/g-C₃N₄ was investigated using in situ diffuse reflectance infrared Fourier transform spectra (DRIFTS). This study examines hot electron transfer in the Ag/g-C₃N₄ Schottky junction and provides a feasible way to design a plasmonic metal/polymer semiconductor Schottky junction for photothermal catalytic CO₂ reduction. Full article

The precise estimation of above-ground biomass (AGB) is imperative for the advancement of breeding programs. Optical variables, such as vegetation indices (VI), have been extensively employed in monitoring AGB. However, the limited robustness of inversion models remains a significant impediment to the widespread application of UAV-based multispectral remote sensing in AGB inversion. In this study, a novel stem–leaf separation strategy for AGB estimation is delineated. Convolutional neural network (CNN) and transfer learning (TL) methodologies are integrated to estimate leaf biomass (LGB) across multiple growth stages, followed by the development of an allometric growth model for estimating stem biomass (SGB). To enhance the precision of LGB inversion, the large-scale remote sensing data and image simulation framework over heterogeneous scenes (LESS) model, which is a three-dimensional (3D) radiative transfer model (RTM), was utilized to simulate a more extensive canopy spectral dataset, characterized by a broad distribution of canopy spectra. The CNN model was pre-trained in order to gain prior knowledge, and this knowledge was transferred to a re-trained model with a subset of field-observed samples. Finally, the allometric growth model was utilized to estimate SGB across various growth stages. To further validate the generalizability, transferability, and predictive capability of the proposed method, field samples from 2022 and 2023 were employed as target tasks. The results demonstrated that the 3D RTM + CNN + TL method outperformed best in LGB estimation, achieving an R² of 0.73 and an RMSE of 72.5 g/m² for the 2022 dataset, and an R² of 0.84 and an RMSE of 56.4 g/m² for the 2023 dataset. In contrast, the PROSAIL method yielded an R² of 0.45 and an RMSE of 134.55 g/m² for the 2022 dataset, and an R² of 0.74 and an RMSE of 61.84 g/m² for the 2023 dataset. The accuracy of LGB inversion was poor when using only field-measured samples to train a CNN model without simulated data, with R² values of 0.30 and 0.74. Overall, learning prior knowledge from the simulated dataset and transferring it to a new model significantly enhanced LGB estimation accuracy and model generalization. Additionally, the allometric growth model’s estimation of SGB resulted in an accuracy of 0.87 and 120.87 g/m² for the 2022 dataset, and 0.74 and 86.87 g/m² for the 2023 dataset, exhibiting satisfactory results. Separate estimation of both LGB and SGB based on stem and leaf separation strategies yielded promising results. This method can be extended to the monitor and inversion of other critical variables. Full article

This study examined the effects of low-temperature heat treatment on the characteristics of the rubies from Mong Hsu, Myanmar. Five ruby samples were heated to 400, 600, 900 and 1200 °C for different durations, respectively. Before and after each heating step, a visual examination was conducted with a gem microscope under different illumination conditions. Various spectral analyses such as UV-Vis, FTIR, Raman and PL were also used to examine the effect of heating on the ruby samples. The low-temperature heat treatment enhanced the ruby samples by causing the dark blue core to partially or completely fade away. It then increased the overall light transmittance and enhanced the fluorescence peak around 694 nm but did not improve the red hue of the samples. Two major changes were found in the experiments. One was the dark blue core of the samples that faded as the heating temperature increased. They were verified by the spectra to be the variation in the intervalence charge transfer between Fe²⁺ and Ti⁴⁺. The variation in the intervalence charge transfer of Mong Hsu ruby was not noticeable before heating to 900 °C but changed dramatically when heated to 1200 °C. The other was the shift of the FTIR peak, which is caused by decomposition of minerals due to heating. An FTIR 630 cm⁻¹ peak proved to be sensitive to the low-temperature heating and might be helpful for detecting low-temperature treatment. Full article

Photocatalysis is the most promising green approach to solve antibiotic pollution in water, but the actual treatment effect is limited by photocatalytic activity. Herein, Bi and BiOCl were loaded onto the surface of Ce-MOF (metal–organic framework) using an electrostatic adsorption method, and a special ternary heterojunction of Ce/Bi/BiOCl was successfully prepared as a photocatalyst for the degradation of tetracycline (TC). FTIR demonstrated that the obtained photocatalyst contains functional groups such as -COOH belonging to Ce-MOF and characteristic crystal planes of Bi and BiOCl, indicating the successful construction of a ternary photocatalyst. The results of UV–vis absorption spectra confirm that the band gap of Ce/Bi/BiOCl heterojunction is reduced from 3.35 eV to 2.7 eV, resulting in an enhanced light absorption capability in the visible light region. The special ternary heterojunction constructed by Ce-MOF, Bi, and BiOCl could achieve a narrow band gap and reasonable band structure, thereby enhancing the separation of photogenerated charges. Consequently, the photocatalytic performance of the Ce/Bi/BiOCl ternary heterojunction was significantly enhanced compared to Ce-MOF, Bi, and BiOCl. Therefore, Ce/Bi/BiOCl can achieve a photocatalytic degradation rate of 97.7% within 20 min, which is much better than Bi (14.8%) and BiOCl (67.9%). This work successfully constructed MOF-based ternary photocatalysts and revealed the relationship between ternary heterojunctions and photocatalytic activity. This provides inspiration for constructing other heterogeneous catalysts for use in the field of photocatalysis. Full article

Photocatalytic processes offer promising solutions for environmental remediation and clean energy production, yet their efficiency under the visible light spectrum remains a significant challenge. Here, we report a novel silver–graphene (Ag-G) modified TiO₂ (Ag-G-TiO₂) nanocomposite photocatalyst that demonstrates remarkably enhanced photocatalytic activity for both dye wastewater degradation and hydrogen production under visible and UV light irradiation. Through comprehensive characterization and performance analysis, we reveal that the Ag-G modification narrows the TiO₂ bandgap from 3.12 eV to 1.79 eV, enabling efficient visible light absorption. The nanocomposite achieves a peak hydrogen production rate of 191 μmolesg⁻¹h⁻¹ in deionized (DI) water dye solution under visible light, significantly outperforming unmodified TiO₂. Intriguingly, we observe an inverse relationship between dye degradation efficiency and hydrogen production rates in dye solutions with tap water versus DI water, highlighting the critical role of water composition in photocatalytic processes. This work not only advances the understanding of fundamental photocatalytic mechanisms but also presents a promising photocatalyst for solar-driven environmental remediation and clean energy production. The Ag-G-TiO₂ nanocomposite’s enhanced performance across both visible and UV spectra, coupled with its dual functionality in dye degradation and hydrogen evolution, represents a significant step towards addressing critical challenges in water treatment and sustainable energy generation. Our findings highlight the complex interplay between light absorption and reaction conditions, offering new insights for optimizing photocatalytic systems. This research paves the way for developing more efficient and versatile photocatalysts, potentially contributing to the global transition towards sustainable technologies and circular economy in waste management and energy production. Full article

Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R²), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study’s requirements, with R² > 0.6, RPQ > 2.5, and RIQP > 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R² = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a “quick”, inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions. Full article

Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data. Full article

The metallocenes ferrocene (Cp₂Fe, 1), nickelocene (Cp₂Ni, 2), and cobaltocene (Cp₂Co, 3) crystallize in the same space group (P2₁/a) and they have the same shape and similar size. Therefore, they form solid solutions with random distribution of the different molecules when crystallized from solution. Alternatively, the solid metallocenes can be ground together manually, and the solid solutions form at any molar ratio within minutes. The metallocenes 2 and 3 are paramagnetic. Solid solutions of 1/3 and 2/3 have been studied by paramagnetic solution and solid-state NMR spectroscopy. The effect of the paramagnetic species on the other components in the solid solutions has been investigated. The impact on the chemical shifts is limited. However, the halfwidths and the signal shapes, as defined by the rotational sideband intensities, change with increasing amounts of paramagnetic components. The ¹H T₁ relaxation times are shortened for diamagnetic protons in the presence of paramagnetic metallocenes in the solid solutions. It has been demonstrated that all metallocenes mix at the molecular level within the polycrystalline samples. The EPR spectra of the solid solutions are dominated by the most intensive signal of any paramagnetic metallocene in the solid samples. Full article