Search Results (119)

The rising demand for platinum-group metals, driven by their essential applications in catalysis, energy storage, and chemical conversion, underscores the need to identify new sources for their recovery. Waste solutions originating from industrial processes offer a promising alternative source of noble metals. However, due to their typically low concentrations, effective recovery requires a highly targeted approach. In this study, we present a synthetic waste solution containing trace amount of Rh(III) ions as both a medium for metal ion recovery and a direct precursor for catalyst synthesis. Using a bimodal water–ethanol solvent system, ultra-small rhodium nanoparticles were synthesized and subsequently immobilized onto activated carbon fibers (ACFs) within a microreactor system. The resulting Rh@ACF catalyst demonstrated high efficiency in the reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP), serving as a model catalytic reaction. The Rh@ACF catalyst, containing 4.24 µg Rh per milligram of sample, exhibited notable catalytic activity, achieving 75% conversion of 4-NP to 4-AP within 1 h. Full conversion to 4-AP was also reached within 5 min, but requires extra NaBH₄ addition to the catalytic mixture. Full article

At present, it is a real challenge to measure brain signals outside of the lab with portable systems that are robust, comfortable and easy to use. We propose in this article a bimodal electroencephalography–functional near-infrared spectroscopy (EEG-fNIRS) sensor whose spatial geometry allows the robust estimation of colocated electrical and hemodynamic brain activity. The geometry allows for the correction of extra-cerebral activity (short-channel distance) as well as the computation of the spatial gradient of absorbance required in the spatially resolved spectroscopy (SRS) method. The complete system is described, detailing the technical solutions implemented to provide signals at 250 Hz for both synchronized modalities and without crosstalk. The system performances are validated during an N-Back mental workload protocol. Full article

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. Full article

Ultra-high-molecular-weight polyethylene (UHMWPE) is a pivotal material in engineering and biomedical applications due to its exceptional mechanical strength, wear resistance, and impact performance. However, its extreme melt viscosity, caused by extensive chain entanglements, severely limits processability via conventional melt-processing techniques. Recent advances in catalytic synthesis have enabled the production of disentangled UHMWPE (dis-UHMWPE), which exhibits enhanced processability while retaining superior mechanical properties. Notably, heterogeneous catalytic systems, utilizing supported fluorinated bis (phenoxy-imine) titanium (FI) catalysts, polyhedral oligomeric silsesquioxanes (POSS)-modified Z-N catalysts, and other novel catalysts, have emerged as promising solutions, combining structural control with industrial feasibility. Moreover, optimizing polymerization conditions further enhances chain disentanglement while maintaining ultra-high molecular weights. These systems utilize nanoscale supports and ligand engineering to spatially isolate active sites, tailor the chain propagation/crystallization kinetics, and suppress interchain entanglement during polymerization. Furthermore, characterization techniques such as melt rheology and differential scanning calorimetry (DSC) provide critical insights into chain entanglement, revealing distinct reorganization kinetics and bimodal melting behavior in dis-UHMWPE. This development of hybrid catalytic systems opens up new avenues for solid-state processing and industrial-scale production. This review highlights recent advances concerning interaction between catalyst design, polymerization control, and material performance, ultimately unlocking the full potential of UHMWPE for next-generation applications. Full article

Extensive numerical simulations were performed in MATLAB R2020b based on the classical nonlinear Thomson scattering theory and single-electron model, to systematically examine the influence of initial phase in tightly focused linearly polarized laser pulses on the radiation characteristics of multi-energy-level electrons. Through our research, we have found that phase variation from 0 to 2π induces an angular bifurcation of peak radiation intensity, generating polarization-aligned symmetric lobes with azimuthal invariance. Furthermore, the bimodal polar angle decreases with the increase of the initial energy. This phase-controllable bimodal distribution provides a new solution for far-field beam shaping. Significantly, high-harmonic intensity demonstrates π-periodic phase-dependent modulation. Meanwhile, the time-domain pulse width also exhibits 2π-cycle modulation, which is synchronized with the laser electric field period. Notably, electron energy increase enhances laser pulse peak intensity while compressing its duration. The above findings demonstrate that the precise control of the driving laser’s initial phase enables effective manipulation of the radiation’s spatial characteristics. Full article

Understanding the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its drivers is critical to sustainable land -carbon management, carbon-neutral development, and ecological restoration in fragile karst landscapes. This study proposes a Pearson Correlation—Deep Transformer (PCADT) model that integrates attention mechanisms and geospatial covariates to enhance NPP estimation accuracy in Guangxi, China—a global karst hotspot. Leveraging multisource remote sensing data (2015–2020), PCADT achieves 10.7% higher predictive accuracy (R² = 0.83 vs. conventional models) at 500 m resolution, thereby capturing the fine-scale heterogeneity essential for sustainability planning. The results reveal a significant annual NPP increase (4.14 gC·m⁻²·a⁻¹, p < 0.05), with eastern areas exhibiting higher baseline productivity (1129 gC·m⁻² in Wuzhou) but western regions showing steeper growth rates (5.2% vs. 2.1%). Vegetation carbon sequestration capacity, validated against MOD17A3HGF data (R² = 0.998), demonstrates spatial consistency (east > west), with forest-dominated Wuzhou contributing 6.5 TgC annually. Mechanistic analyses identify precipitation as the dominant climatic driver (partial r = 0.62, p < 0.01), surpassing temperature sensitivity, while bimodal NPP-altitude peaks (300 m and 900 m) and land -use transitions (e.g., forest-to-cropland conversions) explain 18.5% of NPP variability and reduce regional carbon stocks by 4593 tC. The PCADT framework offers a scalable solution for precision carbon management by emphasizing the role of anthropogenic interventions—such as afforestation—in alleviating climatic constraints. It advocates for spatially adaptive strategies to optimize water resource utilization, enhance forest conservation, and promote sustainable land -use transitions. By identifying areas where water -scarcity relief and targeted afforestation would yield the highest carbon returns, the PCADT framework directly supports SDG 13 (Climate Action) and SDG 15 (Life on Land), providing a strategic blueprint for sustainable development in water-limited karst regions globally. Full article

The traditional preparation of polyvinyl alcohol (PVA) grinding wheels typically involves hazardous chemicals such as formaldehyde and hydrochloric acid, posing significant health risks to operators and contributing to environmental pollution. In this study, we utilized the freeze-drying method to fabricate PVA grinding wheels, optimizing both the manufacturing process and the structure of the porous composite materials. The results demonstrate that phenolic resin (PF) participates in constructing a hydrogen-bonded network with PVA and pure terephthalic acid (PTA), which synergistically enhances the esterification efficiency between PTA and PVA. Furthermore, the incorporation of PTA as a crosslinking agent led to a more concentrated pore distribution, reducing the average pore size while enhancing mechanical strength. The freeze-drying duration of 42 h and 10% solid content of the PVA solution yields the favorable comprehensive porosity and mechanical performance of the grinding wheel with a unique bimodal pore structure and porosity exceeding 50%. The maximum grinding ratio was achieved at 0.81, while the surface roughness (Sa) was 0.308 μm. The freeze-drying approach significantly enhances pore uniformity and adjustability, producing grinding wheels with superior mechanical properties and performance consistency. This study presents a novel and environmentally friendly alternative to traditional PVA grinding wheel fabrication methods. Full article

The effects of different processes for heat treatment on microstructures and mechanical properties of a Ti-Al-V-Cr-Fe-based alloy (TLC002) were investigated based on the Ti-6411 alloy designed by Northwest Institute for Nonferrous Metals Research. The results show that the TLC002 alloy treated with solid solution and aging has high strength and low impact toughness. For the annealed specimens, both strength and impact toughness are high. With the rising annealing temperature from 800 °C to 880 °C, the tensile strength (UTS), yield strength (YS), and impact toughness (α_u2) increase, especially for the α_u2 from 48.7 J/cm² to 86.0 J/cm². The tensile and impact specimens treated with both solid solution and aging and annealing are all typical ductile fractures. Both the size dimension and depth of the dimples for the equiaxed structures are greater than those of the bimodal structures, indicating that the plasticity of the equiaxed structures is superior to that of the bimodal structures. The heat treatment that annealing at 880 °C for 1.5 h and then air cooling leads to qualified mechanical properties and a good match of the strength and plasticity of the TLC002 alloy. Full article

Background/Objectives: Advances in cochlear implant (CI) technology have led to the expansion of the implantation criteria. As a result, more CI candidates may have greater residual hearing in one or two ears. Many of these candidates will perform better with a CI in one ear and a hearing aid (HA) in the other ear, the so-called bimodal solution. The bimodal solution often requires patients to switch to HAs that are compatible with the CI. However, this can be a challenging decision, not least because it remains unclear whether this impacts hearing performance. Our aim is to determine whether speech perception in noise remains unchanged or improves with new replacement HAs compared to original HAs in CI candidates with residual hearing. Methods: Fifty bilateral HA users (mean age 63.4; range 23–82) referred for CI were recruited. All participants received new replacement HAs. The new HAs were optimally fitted and verified using Real Ear Measurement (REM). Participants were tested with the Hearing in Noise Test (HINT), which aimed at determining the signal-to-noise ratio (SNR) required for a 70% correct word recognition score at a speech sound pressure level (SPL) of 65 dB. HINT testing was performed with both their original and new replacement HAs. During HINT, pupillometry was used to control for task engagement. Results: Replacing the original HAs with new replacement HAs after one month was not statistically significant with a mean change of SRT70 by −1.90 (95% CI: −4.69;0.89, p = 0.182) dB SNR. Conclusions: New replacement HAs do not impact speech perception scores in CI candidates prior to the decision of cochlear implantation. Full article

K-means clustering is a fundamental tool in data mining, yet its scalability and efficacy decline when faced with massive datasets. In this work, we introduce BiModalClust, a novel clustering algorithm that leverages a bimodal optimization paradigm to overcome these challenges. Our approach simultaneously optimizes two interdependent modalities: the input data stream and the neighborhood structure of the solution landscape, which emerges from iterative restrictions of the Minimum Sum-of-Squares Clustering (MSSC) objective function to sampled subsets of the data. By integrating the Variable Neighborhood Search (VNS) metaheuristic, we systematically explore and refine these landscapes through dynamic reinitialization of degenerate centroids and adaptive exploration of expanding neighborhoods. This dual-stream optimization not only transforms traditional local search into a more global and robust process but also ensures computational scalability and precision. Extensive experimentation on diverse real-world datasets demonstrates that BiModalClust achieves superior clustering performance among K-means-based methods in big data environments. Full article

Tracking the microstructural evolution during high-pressure die casting of Al-Si alloys is challenging due to the rapid solidification, varying thermal conditions, and severe turbulence. The process involves a transition from slower cooling in the shot sleeve to rapid cooling in the die cavity, resulting in a bimodal dendritic microstructure and nucleation of new finer dendrite arms on fragmented externally solidified crystals. In this study, a two-dimensional phase-field model was employed to investigate the solidification behaviour of a hypoeutectic Al-7% Si alloy during high-pressure die casting. The model is based on thermodynamic formulations that account for temperature changes due to phase transformation heat, thermal boundary conditions, and solute diffusion in both liquid and solid phases. To replicate the observed bimodal microstructure, solid–liquid interface properties such as thickness, energy, and mobility were systematically varied to reflect the transition from the shot sleeve to the die cavity. The results demonstrated the model’s ability to capture the growth of dendrites under shot sleeve conditions and nucleation and development of new dendrite arms under the rapid cooling conditions of the die cavity. Full article

New quaternary ammonium derivatives (quats) based on apple pectin (PA) were synthesized by the chemical modification of native polysaccharides with various quaternization mixtures containing epichlorohydrin (ECH) and a tertiary amine. Pectin derivatives (QPAs) were studied by elemental analysis, conductometric titration, Fourier-transform infrared spectroscopy (FTIR), and ¹³C nuclear magnetic resonance (¹³C NMR). Viscosity measurements enabled the evaluation of the viscosity average molar mass (M_v) for the unmodified polysaccharide, as well as its intrinsic viscosity ([η]) value. Dynamic light scattering (DLS) analysis revealed that the PA and its quats formed aggregates in an aqueous solution with either a unimodal (PA) or bimodal (QPAs) distribution. Scanning transmission electron microscopy analysis (STEM) of the PA and its derivatives demonstrated the presence of individual polymeric chains and aggregates in aqueous solution, with the smallest sizes being specific to amphiphilic polymers. Thermal stability, as well as wide-angle X-ray diffraction (WAXD) studies, generally indicated a lower thermal stability and crystallinity of the QPAs compared with those of the PA. Antipathogenic activity demonstrated that the PA and its derivatives exhibited effectiveness against S. aureus ATCC 25923 bacterium and C. albicans ATCC 10231 pathogenic yeast. Full article

Background/Objectives: The primary goal of our research is to develop a process to prepare an aqueous dispersion of Cisplatin, an important anticancer drug, with increased solubility and storage stability. Method: In this context, we report the use of a customized RESS process for the synthesis of a novel, amber-colored and viscous aqueous cisplatin solution, an important anticancer drug, which we have denoted as “liquid” cisplatin. Results: Using specialized liquid cell in situ transmission electron microscopy (Liquid in situ TEM) and Raman spectroscopy, we demonstrated that “liquid” cisplatin comprises a bi-modal distribution of a highly solvated network of stable cisplatin nanoclusters in water and exhibited 27 times greater water solubility than standard cisplatin. More importantly, “liquid” cisplatin was stable at ambient conditions for over two years. Extensive analytical characterization of “liquid” cisplatin confirmed that it retained the original chemical identity of cisplatin. Cell viability and apoptosis studies on human lung adenocarcinoma A549 cells provided compelling evidence that “liquid” cisplatin demonstrated a more sustained anticancer effect compared to standard cisplatin. Conclusions: Aqueous cisplatin solubility was increased by 27X in the “liquid” cisplatin medium which retained its bio efficacy over a 2-year period. Our experimental results suggest the possibility of developing non-invasive and highly effective novel cisplatin drug-delivery platforms. Full article

Dynamic light scattering (DLS) is a highly efficient approach for extracting particle size distributions (PSDs) from autocorrelation functions (ACFs) to measure nanoparticle particles. However, it is a technical challenge to get an exact inversion of the PSD in DLS. Generally, Tikhonov regularization is widely used to address this issue; it uses the L₂ norm for both the data fitting term (DFT) and the regularization constraint term. However, the L₂ norm’s DFT has poor robustness, and its regularization term lacks sparsity, making the solution susceptible to noise and a reduction in accuracy. To solve this problem, the L_p,q norm restrictive model is formulated to examine the impact of various norms in the DFT and regularization term on the inversion results. On this basis, combined with the robustness of DFT and the sparsity of regularization terms, an L_1,∞-constrained Tikhonov regularization model was constructed. This model improves the inversion accuracy of PSD and offers a better noise-resistance performance. Simulation tests reveal that the L_1,∞ model has strong noise resistance, exceptional inversion precision, and excellent bimodal resolution. The inversion outcomes for the 33 nm unimodal particles, the 55 nm unimodal, and the 33 nm/203 nm bimodal experimental particles show that L_1,∞ reduces peak errors by at most 6.06%, 5.46%, and 12.12%/3.94% compared to L_2,2, L_1,2, and L_2,∞ models, respectively. These simulations are validated by experimental data. Full article

The kinetics of phase transformations in the nitriding process α-Fe → γ’-Fe₄N → ε-Fe_3-2N of the pre-reduced iron ammonia synthesis catalyst was investigated under in situ conditions (atmospheric pressure, 350 °C) by measuring changes of mass, gas phase composition, and magnetic permeability in a differential tubular reactor. The iron nanocrystallite size distribution according to their specific active surface areas was measured, and it was found that the catalyst is bimodal as the sum of two Gaussian distributions, also differing in the value of the relative magnetic permeability. Relative magnetic permeability of small α-Fe crystals in relation to large crystals is higher by 0.02. In the area of α → γ’ transformation, the magnetic permeability dependencies change, proving the existence of two mechanisms of the α-Fe structure change in the α-Fe → γ’-Fe₄N transformation. In the first area, a solution of α-Fe (N) is formed with a continuous and insignificant change of the crystal lattice parameters of the iron lattice. In the second area, there is a step, oscillatory change in the parameters of the iron crystal lattice in Fe_xN (x = 0.15, 0.20, 0.25 mol/mol). In the range of γ’-Fe₄N → ε-Fe_3-2N transformation, a solution is formed, with nitrogen concentration varying from 0.25–0.45 mol/mol. During the final stage of the nitriding process, at a constant value of the relative magnetic permeability, only the concentration of nitrogen in the solution ε_r increases. The rate of the phenomenon studied is limited by a diffusion rate through the top layer of atoms on the surface of iron nanocrystallite. The estimated value of the nitrogen diffusion coefficient varied exponentially with the degree of nitriding. In the area of the solution, the diffusion coefficient is approximately constant and amounts to 5 nm²/s. In the area of oscillatory changes, the average diffusion coefficient changes in the range of 3–11 nm²/s, and is inversely proportional to the nitrogen content degree. The advantage of the research method proposed in this paper is the possibility of simultaneously recording, under reaction conditions, changes in the values of several process parameters necessary to describe the process. The research results obtained in this way can be used to develop such fields of knowledge as heterogeneous catalysis, materials engineering, sensorics, etc. Full article