Search Results (251)

Natural hydrogen is generated by fairly deep processes and/or in low-permeability rocks. In such contexts, fluids circulate mainly through the network of faults and fractures. However, hydrogen flows from these hydrogen-generating layers can reach sedimentary rocks with more typical permeability and porosity, allowing H₂ flows to spread out rather than be concentrated in fractures. In that case, three different H₂ transport modes exist: advection (displacement of water carrying dissolved gas), diffusion, and free gas Darcy flow. Numerical models have been run to compare the efficiency of these different modes and the pathway they imply for the H₂ in a sedimentary basin with active aquifers. The results show the key roles of these aquifers but also the competition between free gas flow and the dissolved gas displacement which can go in opposite directions. Even with a conservative hypothesis on the H₂ charge, a gaseous phase exists at few kilometers deep as well as free gas accumulation. Gaseous phase displacement could be the faster and diffusion is neglectable. The modeling also allows us to predict where H₂ is expected in the soil: in fault zones, eventually above accumulations, and, more likely, due to exsolution, above shallow aquifers. Full article

Differential equations need boundary conditions (BCs) for their solution. It is widely acknowledged that differential equations and BCs are representative of independent physical processes, and no correlations between them are required. Two recent studies by Hilhorst, Chung et al. argue instead that, in the specific case of diffusion equations (DEs) in bounded systems, BCs are uniquely constrained by the form of transport coefficients. In this paper, we revisit how DEs emerge as fluid limits out of a picture of stochastic transport. We point out their limits of validity and argue that, in most physical systems, BCs and DEs are actually uncorrelated by virtue of the failure of diffusive approximation near the system’s boundaries. When, instead, the diffusive approximation holds everywhere, we show that the correct chain of reasoning goes in the direction opposite to that conjectured by Hilhorst and Chung: it is the choice of the BCs that determines the form of the DE in the surroundings of the boundary. Full article

The excellent moisture transport properties of yarns play a crucial role in improving the liquid moisture transfer behavior within textiles and maintaining their thermal-wet comfort. However, the current research on the moisture management performance of fabrics made from yarns with excellent liquid transport properties primarily compares the wicking results, without considering the varying requirements of testing conditions due to differences in human sweating rates during daily activities. Moreover, the understanding of moisture transport mechanisms in yarns within fabrics under different testing conditions remains insufficient. In this study, two types of twisted combination yarns, composed of hydrophobic profiled polyester filaments and hydrophilic spun yarns to form a hydrophobic-hydrophilic gradient along the axial direction of the yarn, were developed and compared with profiled polyester filaments to understand the liquid migration behaviors in the knitted fabrics formed by these yarns. Results showed that hydrophobic profiled polyester filament yarn demonstrated superior liquid transport performance with infinite saturated liquid supply (vertical wicking test). In contrast, the twisted combination yarns exhibited better moisture diffusion properties under limited liquid droplet supply conditions (droplet diffusion test and moisture management test). These contradictory findings indicated that the amount of liquid moisture supply in testing conditions significantly affected the moisture transport performance of yarns within fabrics. It was revealed that the liquid moisture in the twisted combination yarns migrated through capillary wicking for moisture transfer. Under an infinite saturated liquid supply condition, the higher the content of hydrophilic fibers in the spun yarns, the greater the amount of moisture transferred, demonstrating an excellent liquid transport performance. Under the limited liquid droplet supply conditions, both the volume of liquid water and the moisture absorption capacity of the yarn jointly influence internal moisture migration within the yarn. It provided a theoretical reference for testing the internal moisture wicking performance of fabrics under different states of human sweating. Full article

The cryopreservation of three-dimensional (3D) biofabricated constructs is a key enabler for their clinical application in regenerative medicine. Unlike two-dimensional (2D) cultures, 3D systems such as encapsulated cell spheroids, molded hydrogels, and bioprinted tissues present specific challenges related to cryoprotectant (CPA) diffusion, thermal gradients, and ice formation during freezing and thawing. This review examines the current strategies for preserving 3D constructs, focusing on the role of biomaterials as cryoprotective matrices. Natural polymers (e.g., hyaluronic acid, alginate, chitosan), protein-based scaffolds (e.g., silk fibroin, sericin), and synthetic polymers (e.g., polyethylene glycol (PEG), polyvinyl alcohol (PVA)) are evaluated for their ability to support cell viability, structural integrity, and CPA transport. Special attention is given to cryoprotectant systems that are free of dimethyl sulfoxide (DMSO), and to the influence of hydrogel architecture on freezing outcomes. We have compared the efficacy and limitations of slow freezing and vitrification protocols and review innovative approaches such as temperature-controlled cryoprinting, nano-warming, and hybrid scaffolds with improved cryocompatibility. Additionally, we address the regulatory and manufacturing challenges associated with developing Good Manufacturing Practice (GMP)-compliant cryopreservation workflows. Overall, this review provides an integrated perspective on material-based strategies for 3D cryopreservation and identifies future directions to enable the long-term storage and clinical translation of engineered tissues. Full article

Traditional villages are irreplaceable cultural heritages, embodying complex human–environment interactions. This study uses historical geography analysis, kernel density estimation, centroid migration modeling, and Geodetector techniques to analyze the 2000-year spatiotemporal evolution and formation mechanisms of 224 nationally designated traditional villages in China’s Qinling-Daba Mountains. The findings are as follows: (1) These villages significantly cluster on sunny slopes of hills and low mountains with moderate gradients. They are also closely located near waterways, ancient roads, and historic cities. (2) From the embryonic stage during the Qin and Han dynasties, through the diffusion and transformation phases in the Wei, Jin, Song, and Yuan dynasties, to the mature stage in the Ming and Qing dynasties, the spatial center of these villages shifted distinctly southwestward. This migration was accompanied by expansion along waterway transport corridors, an enlarged spatial scope, and a decrease in directional concentration. (3) The driving forces evolved from a strong coupling between natural conditions and infrastructure in the early stage to human-dominated adaptation in the later stage. Agricultural innovations, such as terraced fields, and sociopolitical factors, like migration policies, overcame environmental constraints through the synergistic effects of cultural and economic networks. Full article

Artificial intelligence (AI) has the potential to significantly advance the management of nonpoint source pollution (NPSP), a critical environmental issue characterized by diffuse sources and complex transport mechanisms. This study systematically examines current AI applications addressing NPSP through bibliometric and systematic analyses. A total of 124 studies were included after rigorous identification, screening, and eligibility assessments based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Key findings from the bibliometric analysis include publication trends, regional research contributions, author and journal contributions, and core concepts in NPSP. The systematic analysis further provided: (a) a comprehensive synthesis of NPSP characterization, covering pollution sources, key drivers, pollutants, transport pathways, and environmental impacts; (b) identification of emerging AI technologies such as the Internet of Things, unmanned aerial vehicles, and geographic information systems, and their potential applications in NPSP contexts; (c) a detailed classification of AI models used in NPSP assessment, highlighting predictors, predictands, and performance metrics specifically in water quality prediction and monitoring, groundwater vulnerability mapping, and pollutant-specific modeling; and (d) a critical assessment of knowledge gaps categorized into AI model development and validation, data constraints, governance and policy challenges, and system integration, alongside proposed targeted future research directions emphasizing adaptive governance, transparent AI modeling, and interdisciplinary collaboration. The findings from this study provide essential insights for researchers, policymakers, environmental managers, and communities aiming to implement AI-driven strategies to mitigate NPSP. Full article

We investigate the transport coefficients $\alpha$ and $\beta$ in plasma systems with varying Reynolds numbers while maintaining a unit magnetic Prandtl number (${Pr}_{\mathrm{M}}$). The $\alpha$ and $\beta$ tensors parameterize the turbulent electromotive force (EMF) in terms of the large-scale magnetic field $\overline{\mathbf{B}}$ and current density as follows: $⟨\mathbf{u}\times \mathbf{b}⟩=\alpha \overline{\mathbf{B}}-\beta \nabla \times \overline{\mathbf{B}}$. In astrophysical plasmas, high fluid Reynolds numbers ($Re$) and magnetic Reynolds numbers ($R{e}_{\mathrm{M}}$) drive turbulence, where $Re$ governs flow dynamics and $R{e}_{\mathrm{M}}$ controls magnetic field evolution. The coefficients ${\alpha }_{\mathrm{semi}}$ and ${\beta }_{\mathrm{semi}}$ are obtained from large-scale magnetic field data as estimates of the $\alpha$ and $\beta$ tensors, while ${\beta }_{\mathrm{theo}}$ is derived from turbulent kinetic energy data. The reconstructed large-scale field $\overline{B}$ agrees with simulations, confirming consistency among $\alpha$, $\beta$, and $\overline{B}$ in weakly nonlinear regimes. This highlights the need to incorporate magnetic effects under strong nonlinearity. To clarify $\alpha$ and $\beta$, we introduce a field structure model, identifying $\alpha$ as the electrodynamic induction effect and $\beta$ as the fluid-like diffusion effect. The agreement between our method and direct simulations suggests that plasma turbulence and magnetic interactions can be analyzed using fundamental physical quantities. Moreover, ${\alpha }_{\mathrm{semi}}$ and ${\beta }_{\mathrm{semi}}$, which successfully reproduce the numerically obtained magnetic field, provide a benchmark for future theoretical studies. Full article

Two-dimensional transition metal carbides/nitrides (MXenes) have shown potential in biosensors, cancer theranostics, microbiology, electromagnetic interference shielding, photothermal conversion, and thermal energy storage due to their unique electronic structure, ability to absorb a wide range of light, and tunable surface chemistry. In spite of the growing interest in MXenes, there are relatively few studies on their applications in phase-change materials for enhancing thermal conductivity and weak photo-responsiveness between 0 °C and 150 °C. Thus, this study aims to provide a current overview of recent developments, to examine how MXenes are made, and to outline the combined effects of different processes that can convert light into heat. This study illustrates the mechanisms that include enhanced broadband photon harvesting through localized surface plasmon resonance, electron–phonon coupling-mediated nonradiative relaxation, and interlayer phonon transport that optimizes thermal diffusion pathways. This study emphasizes that MXene-engineered 3D thermal networks can greatly improve energy storage and heat conversion, solving important problems with phase-change materials (PCMs), like poor heat conductivity and low responsiveness to light. This study also highlights the real-world issues of making MXene-based materials on a large scale, and suggests future research directions for using them in smart thermal management systems and solar thermal grid technologies. Full article

Membrane tubes, a class of soft biological confinement for ubiquitous transport intermediates, are essential for cell trafficking and intercellular communication. However, the confinement interaction and directional migration of diffusive nanoparticles (NPs) are widely dismissed as improbable due to the surrounding environment compressive force. Here, combined with the mechanics analysis of nanoparticles (such as extracellular vesicles, EVs) to study their interaction in confinement, we perform dissipative particle dynamics (DPD) simulations to construct a model that is as large as possible to clarify the submissive behavior of NPs. Both molecular simulations and mechanical analysis revealed that the interactions between NPs are controlled by confinement deformation and the centroid distance of the NPs. When the centroid distance exceeds a threshold value, the degree of crowding variation becomes invalid for NPs motion. The above conclusions are further supported by the observed dynamics of multiple NPs under confinement. These findings provide new insights into the physical mechanism, revealing that the confinement squeeze generated by asymmetric deformation serves as the key factor governing the directional movement of the NPs. Therefore, the constraints acting on NPs differ between rigid confinement and soft confinement environments, with NPs maintaining relative stillness in rigid confinement. Full article

Extended connectivity in graphs can be analyzed through k-path Laplacian matrices, which permit the capture of long-range interactions in various real-world networked systems such as social, transportation, and multi-agent networks. In this work, we present several alternative methods based on machine learning methods (LSTM, xLSTM, Transformer, XGBoost, and ConvLSTM) to predict the final consensus value based on directed networks (Erdös–Renyi, Watts–Strogatz, and Barabási–Albert) and on the initial state. We highlight how different k-hop interactions affect the performance of the tested methods. This framework opens new avenues for analyzing multi-scale diffusion processes in large-scale, complex networks. Full article

This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and optimizing the robustness of vehicle detection in real traffic scenarios remains challenging. When data distributions change, such as the impact of adverse weather or sensor damages, model reliability cannot be guaranteed. We first conducted a large-scale robustness benchmark evaluation for vehicle detection. Analysis revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. The impact of color changes and noise, while present, is relatively less pronounced. Moreover, the robustness of vehicle detection is closely linked to its baseline performance and model size. And as the severity of corruption intensifies, the performance of models experiences a sharp drop. When the data distribution of images changes, the features of the vehicles that the model focuses on are weakened, making the activation level of the targets significantly reduced. By evaluation, we provided guidance and direction for optimizing detection robustness. Based on these findings, we propose TDIRM, a traffic-degraded image restoration model based on stable diffusion, designed to efficiently restore degraded images in real traffic scenarios and thereby enhance the robustness of vehicle detection. The model introduces an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Through a triple condition control mechanism, TDIRM achieves restoration results with high fidelity and consistency. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration. Full article

As a zero-carbon fuel, ammonia can be directly employed in its liquid form. However, its unique physical and chemical properties pose challenges to its application in engines. The direct injection of liquid ammonia is considered a promising technique for internal combustion engines, yet its combustion behavior is still not well understood. In this work, the combustion characteristics of liquid ammonia direct injection under high-pressure conditions were investigated using direct numerical simulation (DNS) in a Eulerian–Lagrangian framework. The ammonia spray was injected via a circular nozzle and underwent combustion under high-temperature and high-pressure conditions, resulting in complex turbulent spray combustion. It was found that the peaks of mass fraction of important species, heat release rate, and gaseous temperature increase with increasing axial distance, and the peaks shifted to richer mixtures. The distribution of scalar dissipation rate at various locations is nearly log-normal. The budget analysis of species transport equations shows that the reaction term is much larger than the diffusion term, suggesting that auto-ignition plays a predominant role in turbulent ammonia spray flame stabilization. It can be observed that both non-premixed and premixed combustion modes co-exist in the ammonia spray combustion. Moreover, the contribution of premixed combustion becomes more significant as the axial distance increases. Full article

This work explores the role of cetyltrimethylammonium bromide (CTAB) as a morphology-directing agent in the hydrothermal synthesis of NiFe₂O₄ electrodes for high-performance supercapacitor applications. By fine-tuning CTAB concentrations (0.5%, 1%, and 1.5%), a tunable nanosheet morphology was achieved, with the NiFe-1 sample exhibiting uniformly interconnected nanosheets that enhanced ion diffusion, charge transport, and surface redox activity. Structural and surface analyses confirmed the formation of single-phase cubic NiFe₂O₄ and the presence of Ni²⁺ and Fe³⁺ oxidation states. Electrochemical characterization in a 2 M KOH electrolyte revealed that the NiFe-1 electrode achieved an areal capacitance of 8.21 F/cm² at 20 mA/cm², with an energy density of 0.34 mWh/cm² and a power density of 5.5 mW/cm². The electrode retained 79.61% of its capacitance after 10,000 cycles, demonstrating excellent stability. An asymmetric pouch-type supercapacitor device (APSD), assembled using NiFe-1 and activated carbon, exhibited an areal capacitance of 1.215 F/cm² and delivered an energy density of 0.285 mWh/cm² at a power density of 6.5 mW/cm² across a wide 0–1.8 V voltage window. These results confirm that CTAB-assisted nanostructuring significantly improves the electrochemical performance of NiFe₂O₄ electrodes, offering a scalable and effective approach for next-generation energy storage applications. Full article

Wastewater treatment plants (WWTPs) have been confirmed as reservoirs of antibiotic resistance genes (ARGs). This study systematically investigated the distribution patterns of ARGs across different treatment units in municipal WWTPs, along with the environmental drivers, dissemination characteristics, and exposure risks of aerosol-borne ARGs in aerated tank environments. The results revealed a high compositional similarity in aerosol-borne ARGs across the sampling sites, with multidrug ARGs predominating at an average relative abundance of 52%, followed sequentially by tetracycline (11%), MLS (10%), and glycopeptide resistance genes (7%). The diffusion of aerosol-borne ARGs is significantly influenced by environmental factors including temperature, relative humidity, wind speed, and total suspended particulate (TSP) concentration, with temperature being the most dominant factor affecting the dispersion of ARGs. The atmospheric dispersion model demonstrates that aerosol-borne ARGs decay with increasing downwind distance, showing potential for transport from aeration tanks to locations exceeding 1500 m along the prevailing wind direction. Both within wastewater treatment units and downwind areas, adult males had higher respiratory exposure doses but lower skin contact doses compared to females, with respiratory doses exceeding skin contact by 3–4 orders of magnitude. This study highlights the potential health risks posed by aerosol-borne ARG transmission from WWTP operations. Full article

Empirical equations were developed by many investigators to determine the reaeration coefficients (Ka) required for predicting dissolved oxygen concentrations (DO) in surface waters, especially rivers, lakes, and reservoirs. However, these equations yield a wide range of Ka values. In this paper, an integrated numerical-modeling-based technique was developed to check the validity of the equations before using them in water quality modeling for rivers, lakes, and reservoirs. Depending on direct field measurements at the Hilla River headwater (Saddat Al-Hindiyah Reservoir, Iraq), the temporal oxygen mass transport at the water surface was estimated numerically by solving the one-dimensional advection diffusion equation and then using each Ka empirical equation separately in the numerical model obtained the best specific-waterbody equation. The DO modeling results showed that using a reservoir reaeration coefficient of 0.1 day⁻¹ at 20 °C predicts the best DO simulation with low MAEs of 0.4987 and 0.7880 mg/L during the study years 2021 and 2022, respectively, compared to the field data. However, using the Ka empirical equations simulates the DO with wide-ranging statistical errors even though the temporal Ka values have a similar trend during the year. It was noticed that the empirical equations produced maximum Ka values of (0.0080–0.0967 day⁻¹) and minimum Ka values of (0.00052–0.0267 day⁻¹) in 2021 and maximum Ka values of (0.0079 to 0.0951 day⁻¹) and minimum Ka values of (0.00012 and 0.0231 day⁻¹) in 2022. The present equation selection technique revealed that Broecker et al.’s equation followed by Smith’s equation, developed in 1978, are the best selection for water quality modeling at the Hilla River headwater (MAEs: 0.1347 and 0.1686 mg/L in 2021, respectively; and MAEs: 0.1400 and 0.1744 mg/L in 2022, respectively). Hence, it is necessary to find good agreement for the equation-based prediction of DO, DO source–sink, and Ka values compared to the validated model before making selection. Full article