Search Results (1,165)

This study focuses on the exchange of mono- and divalent metal cations in FAU-type zeolite and their behavior in gas-phase CO₂ adsorption measurements and liquid-phase methylene blue (MB) adsorption in the absence of oxidizing agents under dark conditions. Firstly, zeolites exchanged with different cations were characterized by several techniques, such as XRD, SEM, XRF, XPS, and N₂ adsorption–desorption, to reveal the impact of the cations on the zeolite texture and structure. The adsorption studies revealed a positive effect of cation exchange on the adsorption capacity of the zeolite, particularly for silver-loaded FAU zeolite. In liquid-phase experiments, Ag-Y zeolite also demonstrated the highest MB removal, with a value of 79 mg/g. Kinetic studies highlighted that Ag-Y could reach the MB adsorption equilibrium within 1 h, with its highest rate of adsorption occurring during the first 5 min. In gas-phase adsorption studies, the highest CO₂ adsorption capacity was also achieved over Ag-Y, yielding 10.4 µmol/m² of CO₂ captured. Full article

It is well established that combining exercise with medication may benefit functionality in individuals with PD (Parkinson’s disease). However, the long-term evolution of gait biomechanics under this combination remains poorly understood. Objectives: This study aims to analyze the evolution of spatiotemporal gait parameters, kinetics, and kinematics throughout a long-term exercise program conducted in water and on dry land. Methods: We have compared the trajectories of biomechanical variables across the treatment phases using statistical parametric mapping (SPM). A cohort of fourteen individuals with PD (mean age: 65.6 ± 12.1 years) participated in 24 sessions of aquatic exercises over three months, followed by a three-month retention phase, and then 24 additional sessions of land-based exercises. Three-dimensional gait data and spatiotemporal parameters were collected before and after each phase. Two-way ANOVA with repeated measures was used to compare spatiotemporal parameters. Results: The walking speed increased while the duration of the double support phase decreased. Additionally, the knee extensor moment consistently increased in the entire interval from midstance to midswing (20% to 70% of the stride period), approaching normal gait patterns. Regarding kinematics, significant increases were observed in both hip and knee flexion angles. Furthermore, the abnormal ankle dorsiflexion observed at the foot strike disappeared. Conclusions: These findings collectively suggest positive adaptations in gait biomechanics during the observation period. Full article

Four-dimensional (4D) flow MRI has shown promise for the assessment of aortic hemodynamics. However, data analysis traditionally requires manual and time-consuming human input at several stages. This limits reproducibility and affects analysis workflows, such that large-cohort 4D flow studies are lacking. Here, a fully automated artificial intelligence (AI) 4D flow analysis pipeline was developed and evaluated in a cohort of over 350 subjects. The 4D flow MRI analysis pipeline integrated a series of previously developed and validated deep learning networks, which replaced traditionally manual processing tasks (background-phase correction, noise masking, velocity anti-aliasing, aorta 3D segmentation). Hemodynamic parameters (global aortic pulse wave velocity (PWV), peak velocity, flow energetics) were automatically quantified. The pipeline was evaluated in a heterogeneous single-center cohort of 379 subjects (age = 43.5 ± 18.6 years, 118 female) who underwent 4D flow MRI of the thoracic aorta (n = 147 healthy controls, n = 147 patients with a bicuspid aortic valve [BAV], n = 10 with mechanical valve prostheses, n = 75 pediatric patients with hereditary aortic disease). Pipeline performance with BAV and control data was evaluated by comparing to manual analysis performed by two human observers. A fully automated 4D flow pipeline analysis was successfully performed in 365 of 379 patients (96%). Pipeline-based quantification of aortic hemodynamics was closely correlated with manual analysis results (peak velocity: r = 1.00, p < 0.001; PWV: r = 0.99, p < 0.001; flow energetics: r = 0.99, p < 0.001; overall r ≥ 0.99, p < 0.001). Bland–Altman analysis showed close agreement for all hemodynamic parameters (bias 1–3%, limits of agreement 6–22%). Notably, limits of agreement between different human observers’ quantifications were moderate (4–20%). In addition, the pipeline 4D flow analysis closely reproduced hemodynamic differences between age-matched adult BAV patients and controls (median peak velocity: 1.74 m/s [automated] or 1.76 m/s [manual] BAV vs. 1.31 [auto.] vs. 1.29 [manu.] controls, p < 0.005; PWV: 6.4–6.6 m/s all groups, any processing [no significant differences]; kinetic energy: 4.9 μJ [auto.] or 5.0 μJ [manu.] BAV vs. 3.1 μJ [both] control, p < 0.005). This study presents a framework for the complete automation of quantitative 4D flow MRI data processing with a failure rate of less than 5%, offering improved measurement reliability in quantitative 4D flow MRI. Future studies are warranted to reduced failure rates and evaluate pipeline performance across multiple centers. Full article

Visual field (VF) testing remains a cornerstone in assessing retinal function by measuring how well different parts of the retina detect light. It is essential for early detection, monitoring, and management of many retinal diseases. By mapping retinal sensitivity, VF exams can reveal functional loss before structural changes become visible. This review summarizes how VF testing is applied across key conditions: hydroxychloroquine (HCQ) retinopathy, age-related macular degeneration (AMD), diabetic retinopathy (DR) and macular edema (DME), and inherited disorders including inherited dystrophies such as retinitis pigmentosa (RP). Traditional methods like the Goldmann kinetic perimetry and simple tools such as the Amsler grid help identify large or central VF defects. Automated perimetry (e.g., Humphrey Field Analyzer) provides detailed, quantitative data critical for detecting subtle paracentral scotomas in HCQ retinopathy and central vision loss in AMD. Frequency-doubling technology (FDT) reveals early neural deficits in DR before blood vessel changes appear. Microperimetry offers precise, localized sensitivity maps for macular diseases. Despite its value, VF testing faces challenges including patient fatigue, variability in responses, and interpretation of unreliable results. Recent advances in artificial intelligence, virtual reality perimetry, and home-based perimetry systems are improving test accuracy, accessibility, and patient engagement. Integrating VF exams with these emerging technologies promises more personalized care, earlier intervention, and better long-term outcomes for patients with retinal disease. Full article

The remarkable advantages of zinc anodes render aqueous zinc-ion batteries (ZIBs) a highly promising energy storage solution. Nevertheless, the uncontrolled growth of zinc dendrites and side reactions pose significant obstacles to the practical application of ZIBs. To address these issues, a straightforward strategy has been proposed, involving the addition of a minute quantity of AgNO₃ to the electrolyte to stabilize zinc anodes. This additive spontaneously forms a hierarchically porous Ag interphase on the zinc anodes, which is characterized by its zinc-affinitive nature. The interphase offers abundant zinc nucleation sites and accommodation space, leading to uniform zinc plating/stripping and enhanced kinetics of zinc deposition/dissolution. Moreover, the chemically inert Ag interphase effectively curtails side reactions by isolating water molecules. Consequently, the incorporation of AgNO₃ enables zinc anodes to undergo cycling for extended periods, such as over 4000 h at a current density of 0.5 mA/cm² with a capacity of 0.5 mAh/cm², and for 450 h at 2 mA/cm² with a capacity of 2 mAh/cm². Full zinc-ion cells equipped with this additive not only demonstrate increased specific capacities but also exhibit significantly improved cycle stability. This research presents a cost-effective and practical approach for the development of reliable zinc anodes for ZIBs. Full article

Au-Ag alloy nanoshells (ANSs) were synthesized via chemical reduction, exhibiting superior plasmonic photocatalytic performance. TEM revealed uniform hollow structures (~80 nm), while EDS mapping confirmed homogeneous Au-Ag distribution throughout the shell. According to EDX analysis, the alloy contained 71.40% Ag by weight. XRD verified the formation of a substitutional solid solution without phase separation. The photocatalytic activity was evaluated using p-nitrothiophenol (PNTP) to 4,4′-dimercapto-azobenzene (DMAB) conversion monitored by SERS. UV-Vis spectroscopy showed LSPR peaks of ANSs between Au and Ag NPs, confirming effective alloying. Kinetic studies revealed that ANSs exhibited reaction rates 250–351 times higher than those of Au NPs and 5–10 times higher than those of Ag NPs. This resulted from the synergistic catalysis of Au-Ag and enhanced electromagnetic fields. ANSs demonstrated dual functionality as SERS substrates and photocatalysts, providing a foundation for developing multifunctional nanocatalytic materials. Full article

Introduction: Two-stage revision with an antibiotic-loaded, temporary static cement spacer is a common treatment for periprosthetic joint infection (PJI) of the knee. However, limited data exists on in vivo antibiotic elution kinetics after spacer implantation. This pilot study uses the technique of microdialysis (MD) to collect intra-articular knee samples. The aim was to evaluate MD as an intra-articular sampling method to detect spacer-eluted antibiotics within 72 h after surgery and to determine whether they show specific elution kinetics. Methods: Ten patients (six male, four female; age median 71.5 years) undergoing two-stage revision for knee PJI were included. A MD catheter was inserted into the joint during explantation of the infected inlying implant and implantation of a custom-made static spacer coated with COPAL cement (0.5 g gentamicin (G) and 2 g vancomycin (V)). Over 72 h postoperatively, samples were collected and analyzed for spacer-eluted antibiotics, intravenously administered antibiotics (e.g., cefazolin and cefuroxime), metabolic markers (glucose and lactate), and Interleukin-6 (IL-6). Local and systemic levels were compared. Results: All catheters were positioned successfully and well tolerated for 72 h. Antibiotic concentrations in MD samples peaked within the first 24 h (G: median 9.55 µg/mL [95% CI: 0.4–17.36]; V: 37.57 µg/mL [95% CI: 3.26–81.6]) and decreased significantly over 72 h (for both p < 0.05, G: 4.27 µg/mL [95% CI: 2.26–7.2]; V: 9.69 µg/mL [95% CI: 3.86–24]). MD concentrations consistently exceeded blood levels (p < 0.05), while intravenously administered antibiotics showed higher blood concentrations. Glucose in MD samples decreased from 17.71 mg/dL to 0.89 mg/dL (p < 0.05). IL-6 and lactate concentrations showed no difference between MD and blood samples. Conclusions: Monitoring antibiotics eluted by a static spacer with intra-articular MD for 72 h is feasible. Gentamicin and vancomycin levels remained above the minimal inhibitory concentration. Differentiating infection from surgical response using metabolic and immunological markers remains challenging. Prolonged in vivo studies with MD are required to evaluate extended antibiotic release in two-stage exchanges. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

Wastewater treatment plants (WWTPs) face increasing pressure to handle higher volumes of water due to climate change causing storm surges, which current infrastructure cannot handle. Aerobic granular sludge (AGS) is a promising alternative to activated sludge systems due to their improved settleability property, lowering the land footprint and improving efficiency. This research investigates the optimisation of a lab-scale sequencing batch reactor (SBR) into a continuous flow reactor through mathematical modelling, sensitivity analysis, and a computational fluid dynamic model. This is all applied for the future goal of scaling up the model designed to a full-scale continuous flow reactor. The mathematical model developed analyses microbial kinetics, COD degradation, and mixing flows using Reynolds and Froude numbers. To perform a sensitivity analysis, a Python code was developed to investigate the stability when influent concentrations and flow rates vary. Finally, CFD simulations on ANSYS Fluent evaluated the mixing within the reactor. An 82% COD removal efficiency was derived from the model and validated against the SBR data and other configurations. The sensitivity analysis highlighted the reactor’s inefficiency in handling high-concentration influents and fast flow rates. CFD simulations revealed good mixing within the reactor; however, they did show issues where biomass washout would be highly likely if applied in continuous flow operation. All of these results were taken under deep consideration to provide a new reactor configuration to be studied that may resolve all these downfalls. Full article

The oxidative degradation of anthocyanins in red wine was investigated under controlled conditions using hydroxyl radicals generated in the presence of Cu (II) as a catalyst. A full factorial experimental design with 23 replicates was used to evaluate the effects of hydrogen peroxide concentration, catalyst dosage, and reaction temperature on anthocyanin degradation over a fixed time. Statistical analysis (ANOVA and multiple regression) showed that all three variables and the main interactions significantly affected anthocyanin loss, with temperature identified as the most influential factor. The combined effects were described by a first-order polynomial model. The activation energies for degradation ranged from 56.62 kJ/mol (cyanidin-3-O-glucoside) to 40.58 kJ/mol (peonidin-3-O-glucoside acetate). Increasing the temperature from 30 °C to 40 °C accelerated the degradation kinetics, almost doubled the rate constants and shortened the half-life of the pigments. At 40 °C, the half-lives ranged from 62.3 min to 154.0 min, depending on the anthocyanin structure. These results contribute to a deeper understanding of the stability of anthocyanins in red wine under oxidative stress and provide insights into the chemical behavior of derived pigments. The results are of practical importance for both oenology and viticulture and support efforts to improve the color stability of wine and extend the shelf life of grape-based products. Full article

Real-time, high-resolution monitoring of chemically diverse water pollutants remains a critical challenge for smart water management. Here, we report a fully integrated, multi-modal nano-sensor array, combining graphene field-effect transistors, Ag/Au-nanostar surface-enhanced Raman spectroscopy substrates, and CdSe/ZnS quantum dot fluorescence, coupled to an edge-deployable CNN-LSTM architecture that fuses raw electrochemical, vibrational, and photoluminescent signals without manual feature engineering. The 45 mm × 20 mm microfluidic manifold enables continuous flow-through sampling, while 8-bit-quantised inference executes in 31 ms at <12 W. Laboratory calibration over 28,000 samples achieved limits of detection of 12 ppt (Pb²⁺), 17 pM (atrazine) and 87 ng L⁻¹ (nanoplastics), with R² ≥ 0.93 and a mean absolute percentage error <6%. A 24 h deployment in the Cherwell River reproduced natural concentration fluctuations with field R² ≥ 0.92. SHAP and Grad-CAM analyses reveal that the network bases its predictions on Dirac-point shifts, characteristic Raman bands, and early-time fluorescence-quenching kinetics, providing mechanistic interpretability. The platform therefore offers a scalable route to smart water grids, point-of-use drinking water sentinels, and rapid environmental incident response. Future work will address sensor drift through antifouling coatings, enhance cross-site generalisation via federated learning, and create physics-informed digital twins for self-calibrating global monitoring networks. Full article

The use of gait analysis to differentiate among paediatric populations with neurological and developmental conditions such as idiopathic toe walking (ITW), cerebral palsy (CP), and hereditary spastic paraplegia (HSP) remains challenging due to the insufficient precision of current diagnostic approaches, leading in some cases to misdiagnosis. Existing methods often isolate the analysis of gait variables, overlooking the whole complexity of biomechanical patterns and variations in motor control strategies. While previous studies have explored the use of statistical physics principles for the analysis of impaired gait patterns, gaps remain in integrating both kinematic and kinetic information or benchmarking these approaches against Deep Learning models. This study evaluates the robustness of statistical physics metrics in differentiating between normal and abnormal gait patterns and quantifies how the data source affects model performance. The analysis was conducted using gait data sets from two research institutions in Madrid and Dublin, with a total of 81 children with ITW, 300 with CP, 20 with HSP, and 127 typically developing children as controls. From each kinematic and kinetic time series, Shannon’s entropy, permutation entropy, weighted permutation entropy, and time irreversibility metrics were derived and used with Random Forest models. The classification accuracy of these features was compared to a ResNet Deep Learning model. Further analyses explored the effects of inter-laboratory comparisons and the spatiotemporal resolution of time series on classification performance and evaluated the impact of age and walking speed with linear mixed models. The results revealed that statistical physics metrics were able to differentiate among impaired gait patterns, achieving classification scores comparable to ResNet. The effects of walking speed and age on gait predictability and temporal organisation were observed as disease-specific patterns. However, performance differences across laboratories limit the generalisation of the trained models. These findings highlight the value of statistical physics metrics in the classification of children with different toe walking conditions and point towards the need of multimetric integration to improve diagnostic accuracy and gain a more comprehensive understanding of gait disorders. Full article

In this study, AgI/Ag₃PO₄/WO₃ ternary composite photocatalysts with dual Z-scheme heterojunction were fabricated via the in situ loading of Ag₃PO₄ onto WO₃ followed by anion exchange. Compared to single photocatalysts and binary composites, the AgI/Ag₃PO₄/WO₃ composites exhibited enhanced photocatalytic activity in the photodegradation of chlortetracycline hydrochloride (CTC) under visible-light irradiation. Notably, the AAW-40 photocatalyst, which contained an AgI/Ag₃PO₄ molar ratio of 40%, degraded 75.7% of the CTC within 75 min. Moreover, AAW-40 demonstrated an excellent performance in the cyclic degradation of CTC over four cyclic degradation experiments. The separation and transfer kinetics of the AgI/Ag₃PO₄/WO₃ composite were investigated with photoluminescence spectroscopy, time-resolved photoluminescence spectroscopy, and electrochemical measurements. The improved photocatalytic performance was primarily due to the creation of a silver-bridged dual Z-scheme heterojunction, which facilitated the efficient separation of photoinduced electron–hole pairs, retained the strong reducing capability of electrons in AgI, and ensured the strongly oxidizing nature of the photoexcited holes in WO₃. The dual Z-scheme charge-transfer mechanism was further validated using in situ X-ray photoelectron spectroscopy. This study provides a foundation for developing innovative dual Z-scheme photocatalytic systems aimed at the efficient degradation of antibiotics in wastewater. Full article

This study investigated how long-term aging (750 °C and 950 °C) affects the microstructure and room-temperature tensile properties of the Ni-Co-Cr superalloy GH3617. Characterization (SEM, EDS, EBSD) showed that initial aging (750 °C, 500 h) formed discontinuous M₂₃C₆ carbides, pinning grain boundaries and improving strength. Prolonged aging (750 °C, 5000 h) caused M₂₃C₆ to coarsen into brittle chain-like structures (width up to 1.244 μm) and precipitated M₆C carbides, degrading grain boundaries. Aging at 950 °C accelerated this coarsening via LSW kinetics (rate constant: 6.83 × 10⁻² μm³/s), with Mo segregation promoting M₆C formation. Tensile properties resulted from competing γ′ precipitation strengthening (post-aging strength increased up to 23.3%) and grain boundary degradation (elongation dropped from 70.1% to 43.3%). Fracture shifted from purely intergranular (cracks along M₂₃C₆/γ interfaces at 750 °C) to mixed mode (cracks initiated by M₆C fragmentation at 950 °C). These insights support superalloy microstructure optimization and lifetime prediction. Full article

Background: Core stability is a cornerstone of optimum athletic performance, and its reduction is a risk factor for athletic injuries. Evidence has shown that core impairments can alter lower-limb mechanics through the kinetic chains. Additionally, plantar pressure can be influenced by proximal conditions, such as core muscle fatigue. Therefore, this study aimed to investigate the correlation between core endurance and plantar pressure distribution (PPD) during double-leg stance, single-leg stance, and single-leg squat positions in healthy male athletes. Methods: A total of 21 healthy male recreational athletes between 19 and 26 years of age volunteered to participate in this correlational study. The McGill core endurance test was used to measure the endurance of their core flexors, extensors, and lateral flexors. The participants’ PPD was evaluated using the Tekscan Mobile Mat pressure measurement system in three positions (double-leg stance, single-leg stance, and single-leg squat) for both the dominant and non-dominant feet. Results: There was a poor and insignificant correlation (p > 0.05) between the core flexors’, extensors’, and side flexors’ endurance and the peak and total PPD in all the tested positions for both the dominant and non-dominant feet. Conclusions: Core muscle endurance is neither a component that affects nor is affected by the PPD in this study population. Thus, the endurance of core flexors, extensors, and side flexors may not be considered in screening, examination, or intervention for the total and peak pressure during double-leg stance, single-leg stance, and single-leg squat positions for both the dominant and non-dominant feet in the study population. Further similar studies are warranted in various sports and during dynamic tasks to better understand the different dimensions of the studied relationship in athletes. Full article