Search Results (106)

The electrocatalytic CO₂ reduction reaction (CO₂RR) offers an energy-saving and environmentally friendly approach to producing hydrocarbon fuels. The use of a gas diffusion electrode (GDE) flow cell has generally improved the rate of CO₂RR, while the gas diffusion layer (GDL) remains a significant challenge. In this study, we successfully engineered a novel metal–organic framework (MOF) heterojunction through the controlled coating of zeolitic imidazolate framework (ZIF-L) on ultrathin nickel—metal–organic framework (Ni-MOF) nanosheets. This innovative architecture simultaneously integrates GDL functionality and exposes abundant solid–liquid–gas triple-phase boundaries. The resulting Ni-MOF@ZIF-L heterostructure demonstrates exceptional performance, achieving a formate Faradaic efficiency of 92.4% while suppressing the hydrogen evolution reaction (HER) to 6.7%. Through computational modeling of the optimized heterojunction configuration, we further elucidated its competitive adsorption behavior and electronic modulation effects. The experimental and theoretical results demonstrate an improvement in electrochemical CO₂ reduction activity with suppressed hydrogen evolution for the heterojunction because of its hydrophobic interface, good electron transfer capability, and high CO₂ adsorption at the catalyst interface. This work provides a new insight into the rational design of porous crystalline materials in electrocatalytic CO₂RR. Full article

Mediterranean saline wetlands are significant ecological habitats defined by seasonal water availability and various biological communities, forming a unique ecotone that combines traits of both freshwater and marine environments. Moreover, they are regarded as notable natural and economic resources. Since the sustainable management of protected wetlands necessitates a multidisciplinary approach, the purpose of this study is to provide a comprehensive picture of the hydrological, hydrochemical, and ecological dynamics of a man-made groundwater dependent ecosystem (GDE) by combining remote sensing, hydrochemical data, geostatistical tools, and ecological indicators. The study area, called “Le Soglitelle”, is located in the Campania plain (Italy), which is close to the Domitian shoreline, covering a surface of 100 ha. The Normalized Difference Water Index (NDWI), a remote sensing-derived index sensitive to surface water presence, from Sentinel-2 was used to detect changes in the percentage of the wetland inundated area over time. Water samples were collected in four campaigns, and hydrochemical indexes were used to investigate the major hydrochemical seasonal processes occurring in the area. Geostatistical tools, such as principal component analysis (PCA) and independent component analysis (ICA), were used to identify the main hydrochemical processes. Moreover, faunal monitoring using waders was employed as an ecological indicator. Seasonal variation in the inundation area ranged from nearly 0% in summer to over 50% in winter, consistent with the severe climatic oscillations indicated by SPEI values. PCA and ICA explained over 78% of the total hydrochemical variability, confirming that the area’s geochemistry is mainly characterized by the saltwater sourced from the artesian wells that feed the wetland. The concentration of the major ions is regulated by two contrasting processes: evapoconcentration in summer and dilution and water mixing (between canals and ponds water) in winter. Cl⁻/Br⁻ molar ratio results corroborated this double seasonal trend. The base exchange index highlighted a salinization pathway for the wetland. Bird monitoring exhibited consistency with hydrochemical monitoring, as the seasonal distribution clearly reflects the dual behaviour of this area, which in turn augmented the biodiversity in this GDE. The integration of remote sensing data, multivariate geostatistical analysis, geochemical tools, and faunal indicators represents a novel interdisciplinary framework for assessing GDE seasonal dynamics, offering practical insights for wetland monitoring and management. Full article

In this study, platinum (Pt) nanocatalysts were synthesized via a sol-gel method over the non-stoichiometric, Magnéli phase titanium oxides (TinO_2n−1) at varying Pt loadings (10–40 wt.%). Their structural and morphological properties were characterized, and after preliminary electrochemical screening, the catalysts were integrated into commercially available gas diffusion electrodes (GDEs) with a three-layer structure to enhance mass transport and catalyst utilization. Membrane electrode assemblies (MEAs) were fabricated using a Nafion^® 117 polymer membrane and tested in a laboratory PEM cell under controlled conditions. The electrochemical activity toward the hydrogen reduction reaction (HRR) was evaluated at room temperature and at elevated temperatures to determine the catalytic efficiency and stability. The optimal Pt loading was determined to be 30 wt.%, achieving a current density of approximately 0.12 A cm⁻² at 0.25 V, demonstrating a balance between catalyst efficiency and material utilization. The chronoamperometry tests showed minimal degradation over prolonged operation, suggesting that the catalysts were durable. These findings highlight the potential of Pt-based catalysts supported on Magnéli phase titanium oxides (TinO_2n−1) for efficient HRRs in electrochemical hydrogen pumps/compressors, offering a promising approach for improving hydrogen compression efficiency and advancing sustainable energy technologies. Full article

The conversion of CO₂ through the electrochemical reduction reaction (ECO₂RR) into chemicals or fuels is regarded as one of the effective ways to decrease atmospheric CO₂ concentrations. In this study, a Cu-Sn bimetallic electrocatalyst (ER-Sn_mCu_nO_x-t/CC) was successfully prepared via a chemical foaming method and electrochemical reduction. SEM showed that ER-Sn₁Cu₁O_x-500 nanoparticles were uniformly distributed on the carbon cloth, which benefited from foaming. The XPS results demonstrated the synergistic interaction between Cu and Sn and the existence of oxygen vacancies originating from the electroreduction. Due to the above features, ER-Sn₁Cu₁O_x-500/CC achieved 84.1% FE for HCOOH at −1.1 V vs. RHE, and the corresponding J_HCOOH was up to 32.4 mA·cm⁻² in the H-type cell. Especially in the flow cell, ER-Sn₁Cu₁O_x-500/GDE could reach a high J_HCOOH of 190 mA·cm⁻² at −1.1 V vs. RHE and maintained J_HCOOH higher than 100 mA·cm⁻² for 24 h with a formic acid selectivity over 70%, indicating both excellent catalytic activity and high HCOOH selectivity. In situ FTIR results revealed that synergism between Cu and Sn could regulate the adsorption of intermediates, thus enhancing the catalytic performance of ER-Sn₁Cu₁O_x-500 for ECO₂RR. Full article

Developing efficient and durable non-precious metal catalysts for oxygen electrocatalysis in fuel cells and zinc–air batteries remains an urgent issue to be addressed. Herein, a bimetallic CoCe-NC catalyst is synthesized through pyrolysis of Co/Ce co-doped metal–organic frameworks (MOFs), retaining the inherently high surface area of MOFs to maximize the exposure of Co-N and Ce-N active sites. The electronic interaction between Co and Ce atoms effectively modulates the adsorption/desorption behavior of oxygen-containing intermediates, thereby enhancing intrinsic catalytic activity. In alkaline media, the CoCe-NC catalyst exhibits E_1/2 = 0.854 V electrocatalytic capability comparable to commercial Pt/C, along with superior methanol resistance and durability. Notably, CoCe-NC demonstrates an overpotential 84 mV lower than Pt/C at 300 mA cm⁻² in a GDE half-cell. When the catalyst is employed as a cathode in zinc–air batteries, it demonstrates an open-circuit voltage of 1.47 V, a peak power density of 202 mW cm⁻², and exceptional cycling durability. Full article

While groundwater-dependent ecosystems (GDEs) occupy only a small portion of the Earth’s surface, they hold significant ecological value by providing essential ecosystem services such as habitat for flora and fauna, carbon sequestration, and erosion control. However, GDE functionality is increasingly threatened by human activities, rainfall variability, and climate change. To address these challenges, various methods have been developed to assess, monitor, and understand GDEs, aiding sustainable decision-making and conservation policy implementation. Among these, remote sensing and advanced machine learning (ML) techniques have emerged as key tools for improving the evaluation of dryland GDEs. This study provides a comprehensive overview of the progress made in applying advanced ML algorithms to assess and monitor GDEs. It begins with a systematic literature review following the PRISMA framework, followed by an analysis of temporal and geographic trends in ML applications for GDE research. Additionally, it explores different advanced ML algorithms and their applications across various GDE types. The paper also discusses challenges in mapping GDEs and proposes mitigation strategies. Despite the promise of ML in GDE studies, the field remains in its early stages, with most research concentrated in China, the USA, and Germany. While advanced ML techniques enable high-quality dryland GDE classification at local to global scales, model performance is highly dependent on data availability and quality. Overall, the findings underscore the growing importance and potential of geospatial approaches in generating spatially explicit information on dryland GDEs. Future research should focus on enhancing models through hybrid and transformative techniques, as well as fostering interdisciplinary collaboration between ecologists and computer scientists to improve model development and result interpretability. The insights presented in this study will help guide future research efforts and contribute to the improved management and conservation of GDEs. Full article

Groundwater plays a vital role in sustaining dryland ecosystems, yet our understanding of the spatiotemporal dynamics of groundwater–vegetation interactions in endorheic river basins remains limited. In this study, the covariation between the normalized difference vegetation index (NDVI) and water table depth (WTD) in the Heihe River Basin (HRB), a representative endorheic system, is investigated via multisource data and generalized additive models (GAMs). The results indicate that the NDVI peaks in summer (July), with a corresponding decline in the WTD, indicating a basin-wide negative correlation. Spatial analysis reveals distinct upstream–downstream gradients: upstream regions exhibit strong seasonal synchronization, whereas midstream and downstream areas show weaker correlations because of mixed surface and groundwater influences. Landcover and climate significantly affect these interactions, with arid zones showing the strongest negative correlations (ρ = −0.38), particularly in wetlands, whereas humid regions show nonsignificant relationships. Geomorphological analysis highlights stronger correlations in mountainous areas than in low-relief plains. Positive correlations are the most prevalent in arid regions (54.5%), followed by hyper-arid regions (28.9%), while negative correlations also dominate arid regions (54.6%), followed by semiarid regions (27.6%). Cross-correlation analysis reveals synchronous NDVI–WTD changes at 95% of the grid points, with 5% exhibiting time lags (1–3 months), indicating localized hydrogeological feedback. Notably, 32% of the zones with negative correlations overlap with groundwater-dependent ecosystems (GDEs). GAM analysis reveals that 87.9% of the spatial variability in the NDVI–WTD correlations is attributed to environmental factors, with climate (26.6%) and hydrogeology (19.5%) as the dominant contributors. These findings provide critical insights into groundwater–vegetation interactions in arid ecosystems and offer valuable implications for sustainable water resource management. Full article

Linguistic tasks such as Part-of-Speech (PoS) tagging can be tedious, but are crucial for the development of Natural Language Processing (NLP) tools. Games With A Purpose (GWAPs) aim to reduce the monotony of the task for native speakers and non-experts who contribute to crowdsourcing projects. This study focuses on revising and correcting PoS tags in the Corpus Oral y Sonoro del Español Rural (COSER), the largest collection of oral data in the Spanish-speaking world, to create a parsed corpus of European Spanish dialects. It also examines how game design elements (GDEs) affect players’ enjoyment. Three games—Agentes, Tesoros, and Anotatlón—were developed, incorporating different GDEs, such as rewards and challenges. The results show two levels of enjoyment: at the concept level with Anotatlón, and at the level of individual GDEs with Tesoros. This suggests that certain GDEs influence player enjoyment and, consequently, their preference for certain games. However, the study also shows the complexity of evaluating triggers for player enjoyment in games with more than one implemented GDE. Full article

The main goal of this study is to optimize the treatment of produced water (PW) using a pilot-scale advanced electrochemical oxidation unit. The electro-cell is outfitted with a boron-doped diamond BDD anode and gas diffusion (GDE) cathode. Synthetic PW was prepared in the laboratory following a protocol designed to closely replicate the characteristics of real PW. The PW used in this study had a total dissolved solids (TDS) concentration of 16,000 mg/L and a total organic carbon (TOC) concentration of 250 mg/L. The effect of various electrooxidation parameters on the reduction in TOC was investigated including pH (2–12), electric current (I) (50–200 mA/cm²), and airflow rate (0–4 NL/min). Response surface method RSM with a Box–Behnken design at a confidence level of 95 percent was employed to analyze the impact of the above factors, with TOC removal used as a response variable. The results revealed that the TOC level decreased by 84% from 250 to 40 mg/L in 4 h, current density of 200 mA/cm², pH of 12, and airflow rate 2 (NL/min). The investigation verified the influential role of diverse operational factors in the treatment process. RSM showed that reducing the airflow rate and increasing pH levels and electric current significantly enhanced the TOC removal. The obtained results demonstrated profound TOC removal, confirming the substantial potential of treating PW using the electrochemical method. Full article

This study presents the integration and evaluation of commercially available gas diffusion electrodes (GDEs), specifically designed for high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) within membrane electrode assemblies (MEA) for electrochemical hydrogen pump/compressor applications (EHP/C). Using Nafion 117 as a solid polymer electrolyte, the MEAs were analyzed for cell efficiency, hydrogen evolution, and hydrogen oxidation reactions (HER and HOR) under differential pressure up to 16 bar and a temperature ranging from 20 °C to 60 °C. Key properties of the GDEs, such as electrode thickness and conductivity, were investigated. The catalytic layer was characterized via XRD and EDX analyses to assess its surface and bulk composition. Additionally, the effects of increasing MEA’s geometric size (from 1 cm² to 5 cm²) and hydrogen crossover phenomena on the efficiency were examined in a single-cell setup. Electrochemical performance tests conducted in a single electrochemical hydrogen pump/compressor cell under hydrogen flow rates from 36.6 Ml·min⁻¹·cm⁻² to 51.3 mL·min⁻¹ cm⁻² at atmospheric pressure provided insights into the optimal operational parameters. For a double-stage application, the MEAs demonstrated enhanced current densities, achieving up to 0.6 A·cm⁻² at room temperature with further increases to 1 A·cm⁻² at elevated temperatures. These results corroborated the single-cell data, highlighting potential improvements in system efficiency and a reduction in adverse effects. The work underscores the potential of HT-PEMFC-based GDEs for the integration of MEAs applicable to advanced hydrogen compression technologies. Full article

With the development of algorithms for autonomous decision-making in the field of structural engineering, the design of precast concrete segment (PCS) box girder bridges faces new challenges. This paper proposes using a multi-objective optimization method based on genetic algorithms for the rapid design of PCS box girder bridges with small and medium spans. By considering 20 design parameters such as the physical dimensions of the box girder cross-section, material properties, and prestressing parameters, the paper formulates and quantifies three objective functions: cost, safety, and structural performance. The multi-objective optimization was conducted using four optimization algorithms (NSGA-II, NSGA-III, GDE3, and PSO). An optimization evaluation index ($\phi \left[F\right(x\left)\right]$) was established and weights were assigned to different optimization objectives. A specific design case based on the general diagram of a 3 × 25 m-long continuous PCS box girder bridge was carried out. The results indicate that genetic algorithms performed exceptionally well on this problem, with the NSGA-III algorithm achieving the best $\phi \left[F\right(x\left)\right]$ value of 0.2789 among all algorithms. A performance analysis was conducted on various optimization models using box plots and sensitivity studies. Scatter plots and surface plots of the Pareto front of the optimized solutions were generated, and corresponding cross-sectional design drawings were created based on the two proposed solutions. Compared with the general graph, the design cases provided by the NSGA-III algorithm model have a change rate of 8.03%, −0.29%, and 75.49% in the three optimization objectives, respectively, indicating a significant improvement effect. The research content of this paper provides a reasonable direction for future studies on intelligent bridge design methodologies. Full article

Powder-based fire extinguishing methods are widely used to suppress fires of all kinds efficiently. However, these methods have several drawbacks, the most significant being the large powder residue left behind, which can complicate cleanup and damage sensitive equipment. The present paper investigates reacting flows and develops a methodology for analyzing the interaction of powder particles with fire, addressing both homogeneous and heterogeneous fire inhibition mechanisms. To achieve this, a simplified model was developed using the common principles of the general dynamic equation (GDE) and the population balance equation (PBE) coupled with the reacting flow equations. The model examines the interplay between the initial particles’ diameter and their extinguishing flow rate (concentration), also known as minimal extinguishing concentration (MEC), establishing the relation between the two. Notably, the relation exhibits three different zones, each influenced by different governing mechanisms of combustion inhibition, providing critical insights into optimizing powder-based extinguishing systems. A minimal value of the MEC is found where there is no significant change with the MEC in terms of particle diameter, and the chemical homogeneous mechanism is dominating. The methodology also offers a pathway for finding the maximal extinguishing particle diameter (MED) when the heterogeneous extinguishing mechanism acquires its maximal impact.There is no benefit with a larger particle diameter as it would not practically achieve better extinguishment, but would lead to a potential waste of powder and hence damage equipment. A significant advantage of using extinguishing powders with micro-sized/ultrafine particles is demonstrated where the homogeneous inhibition mechanism becomes predominant. The developed methodology and finding suggest that micro-sized powders are more effective in extinguishing fires, as they offer improved dispersion and reactivity, enhancing the overall efficiency of the fire suppression process. However, considering economic factors such as micron-sized-powder production cost and maintenance may require considering a shift of this set point. Full article

This paper introduces a novel lightweight pose estimation model, GDE-pose, which addresses the current trade-off between accuracy and computational efficiency in existing models. GDE-pose builds upon the baseline YOLO-pose model by incorporating Ghost Bottleneck, a Dynamic Feature Fusion Module (DFFM), and ECA Attention to achieve more effective feature representation and selection. The Ghost Bottleneck reduces computational complexity, DFFM enhances multi-scale feature fusion, and ECA Attention optimizes the selection of key features. GDE-pose improves pose estimation accuracy while preserving real-time performance. Experimental results demonstrate that GDE-pose achieves higher accuracy on the COCO dataset, with a substantial reduction in parameters, over 80% fewer FLOPs, and an increased inference speed of 31 FPS, underscoring its exceptional lightweight and real-time capabilities. Ablation studies confirm the independent contribution of each module to the model’s overall performance. GDE-pose’s design highlights its broad applicability in real-time pose estimation tasks. Full article

Glioblastoma Multiforme (GBM) is the most aggressive primary tumor of the Central Nervous System (CNS) with a low survival rate. The malignancy of GBM is sustained by a bidirectional crosstalk between tumor cells and the Tumor Microenvironment (TME). This mechanism of intercellular communication is mediated, at least in part, by the release of exosomes. Glioma-Derived Exosomes (GDEs) work, indeed, as potent signaling particles promoting the progression of brain tumors by inducing tumor proliferation, invasion, migration, angiogenesis and resistance to chemotherapy or radiation. Given their nanoscale size, exosomes can cross the blood–brain barrier (BBB), thus becoming not only a promising biomarker to predict diagnosis and prognosis but also a therapeutic target to treat GBM. In this review, we describe the structural and functional characteristics of exosomes and their involvement in GBM development, diagnosis, prognosis and treatment. In addition, we discuss how exosomes can be modified to be used as a therapeutic target/drug delivery system for clinical applications. Full article

Flow cells featuring a gas diffusion electrode (GDE) have emerged as an attractive platform for electrochemical CO₂ reduction, offering high current densities (~300 mA·cm⁻²) and low energy consumption. However, the formation of salt precipitates, particularly carbonate and bicarbonate, poses a significant deficiency by reducing the cell’s operational longevity. In this study, we present a novel approach to mitigate salt precipitates in real-time through acid–base interaction. Recovery efficiency and partial current density of the cell were used to evaluate the capability of removing salt precipitates and the maintenance of CO₂ reduction reactions (CO₂RRs). It was suggested that the direct treatment of intermittent acid rinse recovers the performance of CO₂RRs to a large extent (>97%), and the modification of the proton exchange resin reduces the reduction rate of partial current densities to 1/15 than that of the unmodified. This improvement enhances the cell’s catalytic performance, enabling the stability test for catalysts within the GDE-based flow cell. Full article