Search Results (15,438)

Background/Objectives: Non-invasive methods for evaluating rehabilitation outcomes in Parkinson’s disease (PD) remain limited. This preliminary study proposes a mechanics-informed machine learning (ML) framework integrating force-plate data with dimensionality reduction, clustering, and statistical analysis to objectively assess motor control and the effects of a targeted intervention. Methods: Twelve PD patients were randomly assigned to a PD control group performing standard exercises or an intervention group incorporating additional transverse-plane trunk motion exercises for 10 weeks. Ground reaction forces and center of pressure (COP) signals were recorded pre- and post-intervention using a force plate, alongside data from six healthy individuals as a benchmark. Features related to postural sway and COP dynamics were extracted and refined using Forward Feature Selection. Dimensionality reduction (t-SNE) and unsupervised clustering (K-means) identified group-level patterns. SHAP values and Cohen’s d quantified feature importance and effect size. Clustering robustness was assessed with bootstrapping, nested cross-validation, and permutation testing. Results: K-means clustering revealed clear pre/post-intervention separation in five of six intervention patients, with post-intervention states shifting toward the control cluster. Clustering showed strong performance (Silhouette 0.77–0.79; Calinski–Harabasz 100.8–184.9; Davies–Bouldin 0.29–0.45). The most predictive features (RMS-SML and PL-SAP) showed large effect sizes (Cohen’s d = –12.1 and –4.53, respectively) distinguishing PD patients from healthy controls. Traditional statistical tests (e.g., ANOVA) failed to detect within-group changes (p > 0.05), but ML-based methods captured subtle, nonlinear postural adaptations. Conclusions: This preliminary mechanics-informed ML framework detects PD-related motor deficits and rehabilitation-induced improvements using force-plate data, warranting validation in larger cohorts. Full article

Octavinyl polyhedral oligomeric silsesquioxane (POSS) can be used to improve the thermal stability of silicone rubber (SR). However, POSS nanoparticles tend to agglomerate in SR matrix, negatively affecting the reinforcement role of POSS for SR, and consequently limiting the practical application of SR/POSS composite. To address the issue, multifunctional POSS (m-POSS) was synthesized via a thiol-ene click reaction and used as a novel heat-resistant filler for SR. The results demonstrate that m-POSS containing both vinyl and siloxane groups was successfully synthesized, with the main product exhibiting a molecular weight of approximately 1587 g mol⁻¹. At the POSS loading of 1.5 phr, SR/m-POSS (100/1.5) composite has much better mechanical properties and thermal stability than SR/POSS (100/1.5) composite. With increasing m-POSS loading from 1.5 to 4.5 phr, the thermal stability of SR/m-POSS becomes better, while the tensile strength decreases. SR composite filled with 1.5 phr m-POSS has an excellent balance in thermal stability and mechanical properties, with a tensile strength of 9.2 MPa and an elongation at break of 587%. To fill multifunctional polyhedral oligomeric silsesquioxane containing vinyl and siloxane groups into SR is an effective approach to producing composites with excellent properties. Full article

Calcined clays are a promising route to lower-carbon binders, but widescale adoption of limestone calcined clay cements (LC³) requires using low-kaolinitic resources due to the limited availability and pricing of high-grade sources. This study evaluates the long-term hydration of two locally available kaolinite–illite composite clays (kaolinite contents 18% and 13%) in binary (30% SCM) and ternary LC³-type (30% SCM + 15% limestone) binders, benchmarked against OPC and fly ash systems. Over 1 year, thermogravimetric analysis showed lower portlandite (CH) and increasing bound water in SCM systems relative to OPC, reflecting ongoing secondary hydration reactions of the SCMs. XRD/Rietveld confirmed formation of hemi- and monocarboaluminate, enhanced in LC³ versus the corresponding binaries. The degree of hydration (DoH) for clay blends exceeded OPC from 7 days onward and reached comparable hydration levels after 1 year, indicating a beneficial later-age contribution from illite. Mortar tests showed that binary clay mixes approximated the 42.5 N class target at 28 days, while all LC³ mixes exhibited lower early strength but additional strength gain from 28 to 90 days. Overall, low-grade kaolinite–illite clays combined with limestone deliver LC³ systems with hydration development and mechanical performance comparable to commonly used fly ash-based cements, supporting their feasibility as regionally sourced, low-carbon binders. Full article

Background: Although hydrolysis and photolysis are important pathways for penicillin antibiotics degradation in aquatic ecosystems, the degradation mechanism of penicillin antibiotics in real natural waters is rarely reported. Furthermore, the dominant factors influencing this process are poorly understood. Methods: Therefore, five natural waters were selected to simulate both the hydrolysis and photolysis processes of penicillin G (PG) in aqueous environments. Results: Our results demonstrated that the half-life of PG hydrolysis ranged from 44 h to 141 h in natural water, and aqueous Ca²⁺ ion was the most important factor controlling the hydrolytic degradation of PG. Moreover, several biological dissolved organic matter (DOM, microbial by-product compounds) could also promote the PG hydrolysis reaction. Direct photolysis of PG is dominated in natural water, for which half-life photodegradation rates were 6 h in both blank and natural water, suggesting that salinity and DOM have little influence on penicillin photolysis. The hydrolysis reaction mainly involved the cleavage of the ester bond in the β-lactam ring and a decarboxylation process, while photolysis degradation principally included the hydroxylation of the benzene ring and destruction of the thiazole ring. Conclusions: This study demonstrates the significant factors influencing hydrolysis and photolysis of penicillin antibiotics in an aquatic ecosystem, which can improve the estimates of ecological risk of antibiotic pharmaceuticals in a realistic environment. Full article

Brilliant blue, as a pigment food additive, has all the characteristics of printing and dyeing wastewater and belongs to persistent and refractory organic compounds. The photocatalysis–Fenton reaction system consists of two parts: photocatalytic reaction and Fenton reaction. Electrons promote the decomposition of H₂O₂ to produce •OH. In addition, the effective separation of e- and h⁺ by light strengthens the direct oxidation of h⁺, and h⁺ reacts directly with OH⁻ to produce •OH, which can further promote the removal of organic pollutants. In this paper, g-C₃N₄ and Fe/g-C₃N₄ photocatalysts were prepared by the thermal polycondensation method. Fe/g-C₃N₄ of 15 wt% can reach 98.59% under the best degradation environment, and the degradation rate of g-C₃N₄ is only 7.6% under the same conditions. The photocatalytic activity of the catalysts was further studied. Through active species capture experiments, it is known that •OH and •O₂⁻ are the main active species in the system, and the action intensity of •OH is greater than that of •O₂⁻. The degradation reaction mechanism is that H₂O₂ combines with Fe²⁺ in Fe/g-C₃N₄ to generate a large amount of •OH and Fe³⁺, and the combination of Fe-N bonds accelerates the cycle of Fe³⁺/Fe²⁺ and promotes the formation of •OH, thereby accelerating the degradation of target pollutants. •O₂⁻ can reduce Fe³⁺ to Fe²⁺, Fe²⁺ reacts with H₂O₂ to produce •OH, which promotes degradation, and •O₂⁻ itself also plays a role in degradation. In addition, under the optimal experimental conditions obtained by response surface experiments, the fitting degree of first-order reaction kinetics is 0.96642, and the fitting degree of second-order reaction kinetics is 0.57884. Therefore, this reaction is more in line with first-order reaction kinetics. The adsorption rate is only proportional to the concentration of Fe/g-C₃N₄. Full article

Microbial metabolism is intricately governed by thermodynamic constraints that dictate energetic efficiency, growth dynamics, and metabolic pathway selection. Previous research has primarily examined these principles under carbon-limited conditions, demonstrating how microbes optimize their proteomic resources to balance metabolic efficiency and growth rates. This study extends this thermodynamic framework to explore microbial metabolism under various non-carbon nutrient limitations (e.g., nitrogen, phosphorus, sulfur). By integrating literature data from a range of species, it is shown that growth under anabolic nutrient limitations consistently yields more negative Gibbs free energy ($\Delta G$) values for the net catabolic reaction (NCR) per unit of biomass than carbon-limited scenarios. The findings suggest three potentially complementary hypotheses: (1) proteome allocation hypothesis: microbes favor faster enzymes to reduce the proteome fraction used for catabolism, thus freeing proteome resources for additional nutrient transporters; (2) coupled transport contribution hypothesis: the more negative $\Delta G$ of the NCR may in part stem from the increased reliance on ATP-coupled or energetically driven transport mechanisms for nutrient uptake under limitation; (3) bioenergetic efficiency hypothesis: microbes prefer pathways with a more negative $\Delta G$ to enhance the cellular energy status, such as membrane potentials or the ATP/ADP ratio, to support nutrient uptake under anabolic limitations. This integrative thermodynamic analysis broadens the understanding of microbial adaptation strategies and offers valuable insights for biotechnological applications in metabolic engineering and microbial process optimization. Full article

To clarify the characteristics of CO₂–formation water–rock reactions in tight sandstones and their effects on CO₂-enhanced oil recovery (EOR) efficiency and storage efficiency, this study takes the tight oil reservoirs of the Changqing Jiyuan Oilfield as the research object. A variety of experimental techniques, including ICP-OES elemental analysis, powder X-ray diffraction, and scanning electron microscopy, were employed to systematically investigate the mechanisms and main influencing factors of water–rock reactions during CO₂ geological storage. The study focused on analyzing the roles of mineral composition, reservoir pore structure, and formation water chemistry in the reaction process. It explored the potential impacts of reaction products on reservoir properties. Furthermore, based on the experimental results, a coupled reservoir numerical simulation of CO₂ injection for EOR and storage was conducted to comprehensively evaluate the influence of mineralization processes on CO₂ EOR performance and long-term storage efficiency. Results show that the tight sandstone reservoirs in Jiyuan Oilfield are mainly composed of calcite, quartz, and feldspar. The dominant water–rock reactions during CO₂ formation–water interactions are calcite dissolution and feldspar dissolution. Among these, calcite dissolution is considered the controlling reaction due to its significant effect on the chemical composition of formation water, and the temporal variation in other elements shows a clear correlation with the calcite dissolution process. Further analysis reveals that water–rock reactions lead to permeability reduction in natural fractures near injection wells, thereby effectively improving CO₂ EOR efficiency, enhancing sweep volume, and increasing reservoir recovery. At the end of the EOR stage, mineralized CO₂ storage accounts for only 0.53% of the total stored CO₂. However, with the extension of time, mineralized storage gradually increases, reaching a substantial 31.08% after 500 years. The study also reveals the effects of reservoir temperature, pressure, and formation water salinity on mineralization rates, emphasizing the importance of mineral trapping for long-term CO₂ storage. These findings provide a theoretical basis and practical guidance for the joint optimization of CO₂ EOR and geological sequestration. Future research may further focus on the dynamic evolution of water–rock reactions under different geological conditions to enhance the applicability and economic viability of CO₂ storage technologies. Full article

This study investigates neutron radiation sources in medical cyclotrons used for PET isotope production, focusing on differences between ¹⁸F and ¹¹C. Neutron and gamma dose rates were measured in the bunker and operator control room during routine production with an 11 MeV Eclipse cyclotron. ¹⁸F production generated approximately 2.5 times higher neutron levels in the bunker than ¹¹C. Shielding performance also varied: the same wall reduced neutron fluxes by factors of k_F = 14,000 for ¹⁸F and k_C = 86,000 for ¹¹C, while gamma shielding was similar for both isotopes (k_γ ≈ 28,000). However, the neutron shielding factor calculated from the data for ¹⁸F should be taken as k_F ≥ 1.4 × 10⁴, because several neutron readings reached the upper limit of the detector range, which indicates a partial underestimation of the dose in the bunker. Consequently, neutron levels in the control room during ¹⁸F production were about 15-fold higher than during ¹¹C production. These differences result from distinct neutron generation mechanisms. The ¹⁸O(p,n)¹⁸F reaction produces primary neutrons with a Maxwellian spectrum (~2.5 MeV), while ¹¹C neutrons arise solely from secondary interactions in structural materials. The findings emphasize the need for composite shielding adapted to isotope-specific spectra. Annual dose estimates (260 ¹⁸F and 52 ¹¹C productions) showed neutron exposure (3.78 mSv/year, 57%) exceeded gamma exposure (2.82 mSv/year, 43%). The total dose of 6.6 mSv/year is ~33% of regulatory limits, supporting compliance but underscoring the need for dedicated neutron dosimetry. Full article

Electrocatalytic water splitting offers a promising route to sustainable H₂, but the oxygen evolution reaction (OER) in alkaline media remains the principal bottleneck for activity and durability. This review focuses on alkaline OER and integrates mechanism, kinetics, materials design, and cell-level considerations. Reaction mechanisms are outlined, including the adsorbate evolution mechanism (AEM) and the lattice oxygen mediated mechanism (LOM), together with universal scaling constraints and operando reconstruction of precatalysts into active oxyhydroxides. Strategies for electronic tuning, defect creation, and heterointerface design are linked to measurable kinetics, including iR-corrected overpotential, Tafel slope, charge transfer resistance, and electrochemically active surface area (ECSA). Representative catalyst families are critically evaluated, covering Ir and Ru oxides, Ni-, Fe-, and Co-based compounds, carbon-based materials, and heterostructure systems. Electrolyte engineering is discussed, including control of Fe impurities and cation and anion effects, and gas management at current densities of 100–500 mA·cm⁻² and higher. Finally, we outline challenges and directions that include operando discrimination between mechanisms and possible crossover between AEM and LOM, strategies to relax scaling relations using dual sites and interfacial water control, and constant potential modeling with explicit solvation and electric fields to enable efficient, scalable alkaline electrolyzers. Full article

Magnetic nanomaterials with enhanced enzymatic activities have garnered significant attention from researchers worldwide. Magnetic nanomaterials, including nanozymes and immobilized enzymes, can initiate specific catalytic reactions in the diseased microenvironment for cancer treatment. In this review, we aim to present the significant advancements in synthesizing various types of magnetic nanomaterials with enhanced enzymatic activities and their antitumor therapy applications in the past five years. We first show the representative magnetic nanomaterials and elucidate their fundamental mechanisms related to magnetic properties and electromagnetic effects (such as magneto-thermal, magneto-mechanical, and magneto-electric effects). Secondly, we introduce magnetic nanozymes and magnetic immobilized enzymes and discuss the creative methods allowing the enzymatic activities of nanomaterials to be remotely enhanced by various electromagnetic effects. We also discuss some innovative magnetic nanomaterials that exhibit unique responsiveness to external energies (such as X-rays and ultrasounds) for killing cancer cells. Finally, we address future research suggestions in rationally designing advanced magnetic nanomaterials with remote increased enzymatic activities and discuss challenges and opportunities for efficient cancer therapy. Full article

Aqueous zinc-ion batteries (AZIBs) have attracted considerable attention due to their intrinsic safety, low cost, and environmental friendliness. However, drastic volume expansion, sluggish reaction kinetics, and the insufficient structural stability of electrode materials still remain key challenges. In this work, a cascade structure-guided electron transport strategy was used to construct a vanadium nitride@carbon nanosheet/carbon nanofiber (VN@CNS/CNF) composite as a high-performance cathode for AZIBs. In this rationally engineered architecture, carbon-coated VN nanoparticles are uniformly anchored on a conductive carbon nanofiber network, forming a multidimensional interconnected structure that enables fast electron/ion transport and robust mechanical stability. The carbon shell effectively alleviates volume expansion and prevents VN nanoparticle agglomeration, while the continuous carbon fiber backbone reduces charge transfer resistance and enhances reaction kinetics. Benefiting from this synergistic structural design, the VN@CNS/CNF electrode delivers a high specific capacity of 564 mAh g⁻¹ at 0.1 A g⁻¹, maintains 99% capacity retention after 50 cycles, and retains 280 mAh g⁻¹ even at 8 A g⁻¹ after prolonged cycling. This study provides a new structural engineering strategy for vanadium nitride-based electrodes and provides strategic guidance for the development of fast-charging, durable aqueous zinc-ion batteries. Full article

There are many obstacles to the industrial application of CO₂ hydrogenation reduction technology, the most important of which is the high economic cost. The purpose of this study is to explore the interaction mechanism between the active component CuO-ZnO-Al₂O₃(CZA) and the zeolite carrier Zeolite Socony Mobil-5(ZSM-5), screen the simplified preparation method of catalysts with high catalytic performance, and further promote the industrial application of CO₂ hydrogenation reduction technology. In this study, the effects of the gas velocity of the feedstock, the reaction temperature, the content of acidic sites in the carrier, the filling amount of active component, and the mixing mode of the active component and the carrier on catalytic CO₂ hydrogenation reduction were investigated. The structure of the catalysts was analyzed by X-ray diffractometer (XRD), Brunauer-Emmett-Teller (BET), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) and transmission electron microscopy (TEM). The catalyst surface properties were analyzed by X-ray photoelectron spectroscopy (XPS), ammonia temperature programmed desorption (NH₃-TPD), hydrogen temperature programed reduction (H₂-TPR) and other characterization methods. The research found that the grinding treatment led to the insertion of CZA between ZSM-5 zeolite particles in CZA@HZ5-20-GB, which was prepared via grinding both CZA and H-ZSM-5 with an Si/Al ratio of 20, inhibiting the action of strongly acidic sites in the zeolite, resulting in only CO and MeOH in the catalytic products, with no Dimethyl Ether (DME) generation. Full article

Triphenylamine and phenol derivatives are two types of antimicrobial molecules with broad application prospects. Through functional modification, these compounds integrate fluorescence imaging and photodynamic antibacterial therapy, thereby achieving theranostic capabilities. They exert broad-spectrum and highly efficient antimicrobial activity via a membrane-disrupting mechanism, which consequently reduces the propensity for inducing drug resistance. In this work, triphenylamine-phenol derivatives (TPO) were designed and synthesized through three consecutive reactions: Wittig reaction, Heck reaction, and substitution reaction. Double bonds, hydroxyl groups, and brominated alkyl chains were gradually introduced to finally obtain the target product (E)-4-(4-((8-bromooctyl)oxy)styryl)-N,N-diphenylaniline (5). This study provides new insights into the development of novel and highly efficient antibacterial agents. Full article

We present a combined experimental and modeling study of premixed atmospheric-pressure tetralin flames. Chemical speciation in near-stoichiometric (φ = 0.8–1.0) tetralin/O₂/Ar flames was characterized by probe-sampling molecular-beam mass spectrometry (MBMS) with soft ionization (12.3–18 eV). Total ionization cross-sections (TICSs) for heavy intermediates were computed ab initio to enable quantitative MBMS processing. Laminar burning velocities (LBVs) of tetralin/air flames were measured in a range of equivalence ratios (φ = 0.75–1.5) on a nozzle burner via the stretch-corrected total area method. This is the first reported LBV data for tetralin/air flames (maximum LBV was 47.3 ± 2 cm/s at φ = 1.1). The experimental mole fraction profiles and LBVs were interpreted using three detailed mechanisms. None of the mechanisms were able to correctly describe the LBV profile, and a number of discrepancies were observed in the mole fraction profiles. Reaction network and sensitivity analyses were performed to identify specific sub-mechanisms requiring refinement. In particular, the subchemistry of naphthalene and indene strongly affects the accuracy of model predictions, whereas the flame speciation data indicate large uncertainties in the simulated concentrations of these species. Full article

Ammonia is a highly promising zero-carbon fuel for engines. However, it exhibits high ignition energy, slow flame propagation, and severe pollutant emissions, so it is usually burned in combination with highly reactive fuels such as hydrogen. An accurate understanding and modeling of ammonia–hydrogen combustion is of fundamental and practical significance to its application. Deep Neural Networks (DNNs) demonstrate significant potential in autonomously learning the interactions between high-dimensional inputs. This study proposed a deep neural network-based method for optimizing chemical reaction mechanism parameters, producing an optimized mechanism file as the final output. The novelty lies in two aspects: first, it systematically compares three DNN structures (Multi-layer perceptron (MLP), Convolutional Neural Network, and Residual Regression Neural Network (ResNet)) with other machine learning models (generalized linear regression (GLR), support vector machine (SVM), random forest (RF)) to identify the most effective structure for mapping combustion-related variables; second, it develops a ResNet-based surrogate model for ammonia–hydrogen mechanism optimization. For the test set (20% of the total dataset), the ResNet outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R²) of 0.9923 and root mean square error (RMSE) of 135. The surrogate model uses the trained ResNet to optimize mechanism parameters based on a Stagni mechanism by mapping the initial conditions to experimental IDT. The results show that the optimized mechanism improves the prediction accuracy on laminar flame speed (LFS) by approximately 36.6% compared to the original mechanism. This method, while initially applied to the optimization of an ammonia–hydrogen combustion mechanism, can potentially be adapted to optimize mechanisms for other fuels. Full article