Search Results (826)

This study introduces Au-TiO₂ as a promising photocatalyst. This photocatalyst was synthesized by a sustainable method using mushroom and studied for the degradation of methyl orange (MO) dye and ciprofloxacin (CIPF) under simulated solar light. The structural, morphological, and surface chemical properties of the prepared Au-TiO₂ material were characterized. The photocatalytic performance of Au-TiO₂ was investigated and compared with TiO₂ without gold using methyl orange as a model dye. Pristine TiO₂ exhibited limited photocatalytic activity (around 30% MOD removal after 330 min). In contrast, Au-TiO₂ enhanced the degradation efficiency to 77% MOD. The improved performance was attributed to the effective separation of photogenerated electron-hole pairs. Kinetic analysis of MOD degradation indicated that the process was better described by a pseudo-first-order model, with a rate constant of 3.62 × 10⁻³ min⁻¹ and a correlation coefficient (R²) of 0.9796. The Au-TiO₂ photocatalyst was also evaluated for ciprofloxacin (CIPF) degradation and achieved 84% removal. The enhanced photocatalytic activity of Au-TiO₂ was attributed to improved charge separation and increased generation of reactive oxidative species. These results demonstrate that Au incorporation is a promising strategy for improving the photocatalytic performance of TiO₂ toward both dye and pharmaceutical contaminants. Full article

Efficient photocatalytic degradation of azo dyes requires coordinated control of nanostructure, surface chemical environment, and interfacial charge transport. In this work, rare-earth-modified copper-oxalate-derived CuO nanostructures (RE = Ce, Sm, Er, Tm, and Yb) were prepared through hydrothermal synthesis of a copper oxalate precursor, followed by calcination and ultrasonic-assisted RE modification. Structural, spectroscopic, optical, and electrochemical analyses show RE-associated apparent lattice perturbation, modified surface oxygen environments, stronger visible-region optical responses, higher apparent majority-carrier-density descriptors, and lower fitted interfacial charge-transfer resistance relative to pristine CuO. Among the samples, Ce-CuO exhibited the best performance, with a band gap of 1.48 eV, an apparent majority-carrier density of (1.94 ± 0.04) × 10²¹ cm⁻³, and a charge-transfer resistance of 216 Ω·cm². It achieved 91.59% methyl orange (MO) decolorization within 9 min under simulated solar irradiation, corresponding to a 23.7-fold higher apparent rate constant than pristine CuO, and retained 75.8% decolorization efficiency after eight cycles. Scavenger experiments suggested that h⁺ was the principal oxidative species under the investigated conditions, while ·OH and ·O₂⁻ also contributed to MO transformation. Overall, the results show that rare-earth modification is associated with changes in the structural, surface-chemical, optical, and interfacial electrochemical characteristics of copper-oxalate-derived CuO photocatalysts. Full article

This work investigates the photo-Fenton degradation of azo dyes in an aqueous medium rich in sodium dodecyl sulfate (SDS), using methyl orange (MO) as a model contaminant. Chemometric modeling was integrated with mechanistic analysis to elucidate the role of the micellar pseudophase in radical distribution, dye partitioning, and overall reaction efficiency. A sequential experimental design strategy was applied, combining fractional factorial design and response surface methodology (RSM) to evaluate the individual and interactive effects of key operational variables and determine the optimal operating conditions for maximum degradation efficiency. Under optimized conditions (pH 2.85, [H₂O₂] = 100 mM, [Fe²⁺] = 0.5 mM, [SDS] = 13 mM, [MO] = 0.01 mM), MO degradation reached 98.4% in 5 min. Spectroscopic and partitioning studies revealed a strong affinity of MO for the micellar interface, indicating preferential localization in a microheterogeneous environment. Radical scavenging experiments confirmed that hydroxyl radicals are the dominant oxidizing species in water, while the reduction observed in the presence of SDS suggested secondary radical pathways derived from the surfactant under micellar conditions. Kinetic analyses highlighted the role of intermolecular interactions and micellar compartmentalization in radical generation. The optimized system was successfully extended to other azo dyes, underscoring the potential of surfactant-rich organized media to enhance photo-Fenton reactions in complex aqueous environments. Full article

This study presents a novel approach for model-based calibration transfer and drift compensation for metal oxide semiconductor (MOS) gas sensors. The sensors are lab-calibrated and different calibration models for each of the eight volatiles contained in the calibration are trained to allow for an interpretable quantification of individual volatiles in complex mixtures. Calibration transfer and drift compensation are used to compensate for domain shifts that particularly affect the model accuracy. Here, several domain shifts are considered, e.g., sensor-to-sensor variation among different production batches (calibration transfer) or time-related changes in sensor response like poisoning and aging (drift compensation). Such domain shifts can lead to a substantial performance degradation and are critical for reliable field deployment. Since interpretable and robust machine learning algorithms based on feature extraction, feature selection, and regression (FESR) are not inherently capable of model-based calibration transfer and drift compensation, recalibration typically requires time-consuming and labor-intensive laboratory calibration procedures. To address this challenge, a novel approach represents the interpretable FESR machine learning models as a deep neural network (IDNNRep), enabling the application of transfer learning techniques from the field of deep neural networks (DNNs). This allows the reuse of knowledge gained in an initial calibration domain and facilitates model transfer using only a small amount of new calibration data, thereby reducing calibration effort and time. The proposed method is evaluated across multiple gases, including acetone and toluene, for four domain-shift scenarios and compared with FESR models retrained exclusively on data from the new domain and orthogonal signal correction (OSC). The results demonstrate that the proposed approach reduces the root mean square error (RMSE) compared to the initial model, achieving values of 18.0–28.0 ppb (normalized RMSE: 6.3–9.3%) for both gases with only 0.1 of the calibration data, resulting in a reduction of up to 93% compared to the initial calibration model and 89% compared to the OSC. Furthermore, due to the interpretable nature of the underlying FESR structure, the calibration transfer enables additional sensor- and gas-specific insights. Full article

The self-lubricating MoS₂ coating is highly susceptible to degradation in marine environments due to oxidative corrosion. To expand its application in high-humidity and high-salt-fog conditions, this study mixed W with MoS₂ and prepared W-MoS₂ self-lubricating coatings on the surface of CrNi3MoVA steel by electrospark deposition technology. The electrochemical corrosion behaviors of these coatings, with varying W/MoS₂ mass ratios, were examined using an electrochemical workstation in a 3.5 wt.% NaCl solution. The findings indicated that as the MoS₂ content increased, the low-frequency impedance modulus (LIMs) of the W-MoS₂ coating initially rose and then declined. At a MoS₂ content of 20 wt.%, the coating exhibited the highest LIM and the greatest corrosion resistance. In comparison to the CrNi3MoVA steel substrate, the corrosion current density was reduced by 67.4%, a result attributed to the coating’s dense microstructure and improved charge transfer resistance, thereby demonstrating its optimal protective performance. These results provide a laboratory electrochemical basis for designing corrosion-resistant self-lubricating ESD coatings for steel components exposed to chloride-containing environments; however, long-term immersion, cyclic salt-spray, field-exposure, and quantitative adhesion tests are still required before direct long-term marine-service durability can be confirmed. Full article

Industrial wastewater containing synthetic dyes is difficult to treat because many dyes are chemically stable, highly colored, and resistant to natural degradation. In this study, a Ag₂CO₃/montmorillonite (Ag₂CO₃/MMT) composite photocatalyst was synthesized by an in situ precipitation method using acid-activated MMT as a low-cost clay support. The prepared material was characterized by XRD, FTIR, SEM, BET, and UV–Vis diffuse reflectance spectroscopy to evaluate its structural, morphological, textural, and optical properties. XRD and FTIR confirmed the formation of crystalline Ag₂CO₃ on the MMT support, while SEM analysis showed dispersed Ag₂CO₃ surface particles/deposits on the clay sheets. BET analysis indicated that the composite retained mesoporosity after Ag₂CO₃ deposition, which is beneficial for dye adsorption and diffusion. The photocatalytic performance was evaluated using methylene blue (MB) and methyl orange (MO) under visible-light irradiation. The Ag₂CO₃/MMT composite showed efficient dye decolorization, with faster degradation of cationic MB than anionic MO, mainly due to stronger electrostatic interaction between MB and the negatively charged catalyst surface. Kinetic analysis followed a pseudo-first-order model, with apparent rate constants of 0.036 min⁻¹ for MB and 0.021 min⁻¹ for MO. Operational parameters, including solution pH, irradiation time, and catalyst dosage, strongly influenced photocatalytic efficiency. Reactive species trapping experiments indicated that superoxide radicals (•O₂⁻) and photogenerated holes (h⁺) were the dominant oxidative species. The catalyst retained more than 91% activity after four cycles, indicating good recyclability under the tested conditions. These results suggest that Ag₂CO₃/MMT is a promising visible-light-responsive photocatalyst for dye-contaminated wastewater treatment. Full article

Fitness motions present some of the most challenging cases for monocular 3D human reconstruction: extreme articulations, heavy self-occlusion, and frequent self-contact. The Fit3D dataset captures these motions at large scale via a 12-camera VICON motion capture system synchronized with 4 RGB cameras, but in its original release provides only 3D skeletons. This paper contributes three studies built on top of Fit3D. First, we design and validate a methodology for constructing dense GHUM and SMPL-X pseudo-ground-truth shape and pose annotations on top of the raw MoCap: an optimization-based fitting pipeline that combines markers, multi-view 2D keypoints, separate body and hand normalizing-flow priors, and a self-collision loss, which we show improves on the marker-only MoSh++ baseline on the hands and extremities. Second, we define a standardized evaluation protocol—metric set, frame sampling, and coordinate conventions—for monocular 3D human reconstruction on Fit3D, served through the IMAR-hosted Fit3D evaluation resource, and use it to conduct a comparative benchmark study of 19 representative methods spanning optimization-based, single-frame, and video-based families; the two trained on Fit3D (NLF and SMPLest-X) lead the position and orientation metrics, respectively. Third, a controlled fine-tuning experiment shows that adding Fit3D to the training mixture of a strong baseline (HMR2.0) sharply lowers error on the hardest fitness poses without degrading out-of-domain generalization. The Fit3D dataset and the GHUM/SMPL-X annotations are available, under a non-commercial research license, through the IMAR Fit3D resource; as of June 2026, 1088 academics have registered for access. Full article

Molybdenum-doped xerogel composites were prepared and applied in the electro-degradation of tetracycline (TTC), an antibiotic commonly prescribed for the treatment of bacterial infections. Xerogels containing 1, 6, and 14 wt% Mo were synthesized using an RF sol–gel polymerization method in cylindrical molds and were subsequently characterized in terms of their textural, chemical, and electrochemical properties, focusing on the oxygen reduction reaction (ORR). Textural characterization revealed well-developed surface areas and mesoporosity. Electrochemical analysis showed that Mo loading plays a vital role in the ORR mechanism: lower metal content favors the four-electron pathway with lower hydrogen peroxide selectivity, whereas higher Mo loadings promote bifunctional behavior, enabling both in situ H₂O₂ generation and hydroxyl radical production. Undoped and doped xerogels were very active for TTC degradation via the electro-Fenton process, but the presence of MoO₃ and Mo₂C phases improved up to 12% in removal efficiency after 480 min of treatment. Full article

Temper embrittlement (TE) degrades the toughness of high-strength Ni-Cr-Mo steels, typically driven by competing mechanisms of impurity segregation and carbide precipitation. To decouple these effects, this study investigates the influence of prior austenite grain size (PAGS) on TE kinetics in a low-phosphorus (0.0038 wt.%) steel. Varying PAGS microstructures were subjected to isothermal aging and characterized using impact testing and atom probe tomography (APT). APT confirmed negligible phosphorus segregation, proving TE is driven primarily by M₂₃C₆ carbide precipitation. Compositional profiling revealed that carbide growth is kinetically governed by chromium (Cr) diffusion. Kinetic modeling via the Johnson-Mehl-Avrami-Kolmogorov (JMAK) equation demonstrated that coarse-grained steel exhibits a higher initial embrittlement rate due to continuous intergranular carbide networks that facilitate crack propagation. Conversely, fine-grained structures promote discontinuous precipitation, delaying early-stage embrittlement, although both microstructures reach comparable degradation after prolonged exposure. By isolating precipitation kinetics from impurity effects, this research demonstrates that PAGS critically dictates the rate of carbide-driven TE, providing predictive insights for the microstructural design and lifetime optimization of high-strength structural alloys. Full article

Real-world speech restoration must handle coupled distortions, including acoustic noise and reverberation, codec artifacts, clipping, and artifacts left by upstream enhancement systems. Token-based generative systems offer a flexible route for such universal restoration, but discrete audio tokens can discard fine acoustic detail, and aggressive generative decoding may over-process inputs that are already close to clean speech. We propose AudioVAE-MASR, a continuous-latent masked autoregressive framework for multi-distortion speech restoration. A frozen AudioVAE maps clean and degraded speech into paired continuous latent sequences; a Conformer-based branch extracts the degraded-condition sequence $C_y$ from degraded latents; a two-stream masked autoregressive encoder-decoder conditions masked clean-latent recovery on both degraded context and visible clean tokens; and a lightweight diffusion head models the masked clean tokens in the continuous latent space. On the released CCF AATC 2025 blind test set, the main inference setting ($K=16$, temperature $0.5$) achieved WAcc 0.793, SIG 3.401, BAK 3.987, OVRL 3.111, PESQ 1.780, and ESTOI 0.798. Relative to the degraded input, these results improved WAcc and DNSMOS but did not improve PESQ; relative to the organizer baseline, they improved WAcc, SIG, OVRL, and PESQ but remained lower in BAK. A local subjective MOS evaluation with five listeners gave an overall mean score of 4.08 for AudioVAE-MASR, compared with 3.70 for the degraded input and 4.59 for the clean reference. Distortion-type, ablation, and parameter-sensitivity analyses further show that codec inputs remain vulnerable to over-restoration and that longer iterative decoding does not provide a consistent gain. The study therefore presents AudioVAE-MASR as a transparent continuous-latent restoration framework and identifies the fidelity-control problems that must be solved before such generative restoration can match the strongest lightweight discriminative systems. Full article

The Z-scheme Ag₂MoO₄/BiOCl heterojunction with oxygen vacancies was successfully fabricated at a low temperature via a simple in situ precipitation method. The morphological, structural, and optical characteristics of the Ag₂MoO₄/BiOCl heterojunction were systematically examined. The optimized synthesized Ag₂MoO₄/BiOCl heterojunction achieved a removal rate of 80.44% for ciprofloxacin within 180 min of simulated solar irradiation, which was 3.27 and 1.90 times higher than that of pure Ag₂MoO₄ and BiOCl, respectively. The fabricated Z-scheme heterojunction and oxygen vacancies optimize the electron transfer route, enhancing the separation efficiency of photogenerated electrons and holes. Moreover, the active species trapping experiments and ESR analyses demonstrated that holes were the primary reactive species involved in the photocatalytic process. It was hypothesized that the Ag₂MoO₄/BiOCl heterojunction adhered to a Z-scheme mechanism for charge transfer. The straightforward approach opened up novel avenues for the synthesis of efficient BiOCl-based photocatalysts aimed at environmental remediation. Full article

Coordinating a shared fleet across multiple owners under tight time windows is a challenging multi-objective problem balancing cost, timeliness, and equity. We study it for multi-cooperative agricultural machinery dispatch, formulating the Multi-Cooperative Agricultural Machinery Scheduling Problem under Continuous Workload Sharing (MAMSP-CWS) as a three-objective model that minimizes inter-area transfer cost, time-window violation, and cross-cooperative workload imbalance. To approximate the Pareto front, we develop a Multi-Objective Hybrid Particle Swarm Optimization with Tabu Search and Sparsity Repair (MO-HPSO-TS-SR), which couples particle-swarm search, tabu-search refinement, and a sparsity-repair operator within an external crowding-distance archive. The method is evaluated on three scales (a real instance from Liyang, China, and two synthetic ones) against NSGA-II-CWS and HTSMOGA-CWS over 20 independent runs each. MO-HPSO-TS-SR attains the best mean value on every metric-by-scale combination, with a decisive convergence advantage (hypervolume and IGD; Holm-adjusted $p<0.001$, Cliff’s $\delta \approx 1$). A mechanism decomposition identifies Sparsity Repair as the dominant contributor to hypervolume, with Tabu Search as a complementary refiner. The advantage over NSGA-II-CWS widens with problem scale, from 19.4% on the Small instance to 74.2% on the Large instance, reflecting the disproportionate degradation of the genetic baseline rather than a growing advantage of the proposed method. Beyond agriculture, the framework extends to other continuous-encoding scheduling problems, providing a transferable decision-support tool. Full article

In this study, multilayer modified structures were fabricated on the surface of nuclear-grade 2.25Cr–1Mo steel via salt bath nitriding at different temperatures. Fretting corrosion tests were subsequently conducted in liquid sodium at 723 K. The results indicate that the multilayer structures formed by salt bath nitriding effectively enhance the cross-sectional hardness and improve the wear resistance of the substrate. However, after prolonged exposure to liquid sodium at 723 K, these multilayer structures undergo failure, primarily manifesting as cracking, spalling, and corrosion micropores. Material degradation of the nitrided steel is governed by the synergistic effects of tribological removal, chemical corrosion, and thermal acceleration. Notably, the QPQ 550 treatment, featuring a thinner compound layer, exhibited superior tribological performance during extended testing. This is attributed to the fact that while a higher salt bath nitriding temperature (QPQ 590) yields a thicker multilayer structure, it simultaneously induces premature failure—characterized by microcracking and the formation of corrosive channels—which ultimately compromises the wear performance. Full article

Time series forecasting plays a fundamental role across diverse domains including energy systems, transportation, healthcare, etc. Despite significant advancements in forecasting models, their practical application often encounters challenges due to distribution shifts, where the statistical properties of testing data deviate from those of training data. Such shifts can severely degrade model performance, necessitating frequent retraining with the latest data to maintain performance. To address this limitation, we introduce FreqMoE, a framework designed for out-of-distribution (OOD) scenarios. Our proposed method applies random masking as a regularization strategy and utilizes a Mixture of Experts (MoE) network to encourage robust frequency-domain representations. Each expert focuses on modeling specific frequency characteristics and the MoE dynamically selects experts based on spectrum embedding, capturing frequency-domain patterns by modeling sequences in the frequency domain. Experimental results on diverse real-world datasets demonstrate that FreqMoE achieves competitive or leading performance compared to existing approaches in both prediction accuracy and robustness under OOD scenarios, demonstrating improved robustness under naturalistic temporal distribution shifts. Full article

Waste cooking oil (WCO) is generated globally in large quantities, and improper disposal contributes to significant environmental problems. Recently, WCO has attracted increasing attention as a sustainable base fluid for lubricating grease due to its biodegradability, low cost, and favorable physicochemical properties after thermal degradation. This study presents a systematic review conducted with reference to the PRISMA 2020 guidelines of WCO-based grease and lubrication systems published between 2000 and 2025. Scopus was systematically searched, resulting in 22 peer-reviewed studies meeting the inclusion criteria. The review shows that thermal degradation increases WCO viscosity, polarity, and the relative proportion of saturated fatty acids, thereby enhancing boundary lubrication behavior. Tribological performance was found to depend more strongly on formulation strategy than feedstock variability, provided that appropriate pre-treatment is applied. Optimized WCO-based greases achieved coefficient of friction (COF) values as low as 0.0253 and wear scar diameters (WSD) of 467 µm, demonstrating performance comparable to conventional mineral oil greases. Non-soap thickeners exhibited thermal stability exceeding 350 °C, while additives such as molybdenum disulfide (MoS₂) improved friction and wear performance. Overall, this review establishes a structure–property–performance framework linking thermal degradation chemistry, formulation design, and tribological behavior in WCO-based lubrication systems while highlighting challenges related to standardization, long-term stability, and industrial validation. Full article