Search Results (683)

The rate constant k_OH for the reaction of 1,1,1,3,3,3-hexafluoroprop-2-imine with OH radicals was measured relative to two reference compounds, CH₃F and CH₃CHF₂, to be k_OH = (4.2 ± 1.1) × 10⁻¹⁴ cm³ molecule⁻¹ s⁻¹ at 295 K. This implies an atmospheric lifetime with respect to consumption by OH of 0.75 years. Reaction with Cl atoms yielded k_Cl = (7.9 ± 1.7) × 10⁻¹⁶ cm³ molecule⁻¹ s⁻¹ at 295 K, and reaction with O₃ has an upper limit of k_O3 < 4 × 10⁻²³ cm³ molecule⁻¹ s⁻¹, so that the atmospheric consumption by Cl and O₃ is negligibly slow. Absolute infrared cross sections of the imine yield a radiative efficiency of 0.34 W m⁻² ppb⁻¹, which is corrected to 0.23 W m⁻² ppb⁻¹ for the effects of atmospheric lifetime. The imine’s corresponding 100-year global warming potential is 64 ± 19. This value is an upper limit, given that heterogenous atmospheric removal paths, such as hydrolysis in water droplets, are not included. Full article

Tungsten is a critical metal for advanced industrial applications, yet its supply is challenged by the depletion of high-grade ores. This study presents a comprehensive optimization and mechanistic analysis of the alkaline fusion process for extracting tungsten from wolframite ((Fe,Mn)WO₄) using sodium carbonate (Na₂CO₃). Experimental investigations systematically evaluated the effects of alkali-to-ore ratio, reaction temperature (650–1000 °C), and reaction duration (30–270 min). Optimal conditions were established at a 2:1 Na₂CO₃-to-ore molar ratio, a reaction temperature of 750 °C, and a holding time of 30 min, achieving a tungsten extraction efficiency exceeding 99.9%. This represents a significant improvement in energy and process efficiency over conventional methods. A novel kinetic analysis reveals a two-stage reaction mechanism, transitioning from a slow, diffusion-controlled solid-state reaction (E_a = 243 kJ/mol) to a rapid, autocatalytic liquid-phase reaction (E_a = 212 kJ/mol) upon the formation of a Na₂WO₄–Na₂CO₃ eutectic above approximately 590 °C. The optimal temperature of 750 °C is rationalized as the point that ensures operation within this kinetically favorable liquid-phase regime. Furthermore, a thermochemical analysis of ore impurities indicates that silicon, lead, sulfur, and calcium are effectively sequestered into the slag phase as stable silicates, insoluble lead compounds, and sulfates, highlighting an intrinsic purification benefit. X-ray fluorescence (XRF) and X-ray diffraction (XRD) analyses confirmed minimal residual tungsten in the processed slag. This streamlined process, supported by a robust mechanistic understanding, reduces alkaline consumption, shortens reaction times, and maintains high yields, offering a sustainable and efficient pathway for leveraging declining wolframite resources. Full article

The consumption of clothes creates paradoxes in which values, motives, and emotions interact to generate consumption experiences. To test some of these interactions, we conducted three correlational studies, studies 1, 2, and 3, one experiment, study 4, and one qualitative study, study 5. Study 1 found negative relationships between sustainability values and materialism and positive relationships between sustainable values and the preference for experiential purchases. Study 2 found positive relationships between two components of the slow-fashion movement, equity and exclusiveness, and guilt, and a negative relationship with functionality, another component of slow fashion. Study 3 found an indirect relationship between sustainable values and guilt through their positive and significant relationship with increased awareness of the environmental impact of the fast-fashion industry, supporting a mediation model. Study 4 found that participants were was more likely, regardless of whether the purchase of clothing was labeled as fast fashion or not, to experience pride than guilt when recalling recent past purchases. Last, in study 5, we found that consumers buy clothes to look good and pay attention to quality and value without significant concerns for environmental issues. The implications for consumer behavior were discussed. Full article

While 3D Gaussian Splatting (3DGS) has significantly advanced large-scale 3D reconstruction and novel view synthesis, it still suffers from high memory consumption and slow training speed. To address these issues without compromising reconstruction quality, we propose a novel 3DGS-based framework tailored for large-scale scenes. Specifically, we introduce a visibility-aware camera selection strategy within a divide-and-conquer training approach to dynamically adjust the number of input views for each sub-region. During training, a spatially aware densification strategy is employed to improve the reconstruction of distant objects, complemented by depth regularization to refine geometric details. Moreover, we apply an enhanced Gaussian pruning method to re-evaluate the importance of each Gaussian, prune redundant Gaussians with low contributions, and improve efficiency while reducing memory usage. Experiments on multiple large-scale scene datasets demonstrate that our approach achieves superior performance in both quality and efficiency. With its robustness and scalability, our method shows great potential for real-world applications such as autonomous driving, digital twins, urban mapping, and virtual reality content creation. Full article

This paper develops a mathematically grounded neuro-fuzzy control framework for IoT-enabled irrigation systems in precision agriculture. A discrete-time, physically motivated model of soil moisture is formulated to capture the nonlinear water dynamics driven by evapotranspiration, irrigation, and drainage in the crop root zone. A Mamdani-type fuzzy controller is designed to approximate the optimal irrigation strategy, and an equivalent Takagi–Sugeno (TS) representation is derived, enabling a rigorous stability analysis based on Input-to-State Stability (ISS) theory and Linear Matrix Inequalities (LMIs). Online parameter estimation is performed using a Recursive Least Squares (RLS) algorithm applied to real IoT field data collected from a drip-irrigated orchard. To enhance prediction accuracy and long-term adaptability, the fuzzy controller is augmented with lightweight artificial neural network (ANN) modules for evapotranspiration estimation and slow adaptation of membership-function parameters. This work provides one of the first mathematically certified neuro-fuzzy irrigation controllers integrating ANN-based estimation with Input-to-State Stability (ISS) and LMI-based stability guarantees. Under mild Lipschitz continuity and boundedness assumptions, the resulting neuro-fuzzy closed-loop system is proven to be uniformly ultimately bounded. Experimental validation in an operational IoT setup demonstrates accurate soil-moisture regulation, with a tracking error below 2%, and approximately 28% reduction in water consumption compared to fixed-schedule irrigation. The proposed framework is validated on a real IoT deployment and positioned relative to existing intelligent irrigation approaches. Full article

The transition to renewable energy technologies is one of the most important ways to achieve the sustainable development goals (SDGs) of affordable and clean energy (SDG7); industry, innovation and infrastructure (SDG9); responsible production and consumption (SDG12); and climate action (SDG13). The widespread use of renewable energy technologies in developing countries will reduce dependence on imported fossil resources, increase industrial competitiveness, and support low-carbon development. Despite all their advantages, the integration of renewable energy technologies into industrial and domestic systems in developing countries remains slow due to a number of barriers. Financial constraints, technical and technological deficiencies, political restrictions and uncertainties, and organizational and managerial inadequacies are some of the barriers to the widespread adoption of renewable energy technologies. This study aims to identify, classify, and prioritize the barriers to the implementation of renewable energy technologies by applying multi-criteria decision-making methods in a fuzzy environment, with Türkiye considered as a case study. The relative importance of the barriers identified using the Single-Valued Spherical Fuzzy SWARA method was assessed, and their interconnections and significance were systematically demonstrated. The findings will contribute to the development of policy and management strategies aligned with global sustainability goals, thereby facilitating a more effective and equitable transition to clean and resilient energy systems. Full article

This article documents abrupt lower distribution limits of sessile invertebrates and seaweeds from rocky intertidal habitats on Pacific and Atlantic shores from both hemispheres. The common feature of these striking patterns is that they are caused primarily by slow-moving predators or herbivores coming from lower elevations. This contribution aims at stimulating comparative studies on these fascinating systems as well as providing visual materials of educational value. Full article

To address the energy limitations, long-term operation demands, and load imbalance in fragment velocity measurement wireless sensor networks, this paper proposes an Energy-Balanced and Stability-Oriented Grey Wolf Optimization (EBSIGWO) algorithm. The algorithm employs a multi-objective fitness function that jointly considers residual energy, intra-cluster load balance, and long-term communication cost, ensuring both energy efficiency and clustering stability. A dynamic elite ratio strategy is further introduced to adaptively balance global exploration and local exploitation, thereby mitigating cluster-head overload and slowing energy depletion. Simulation results show that EBSIGWO significantly extends network lifetime compared with LEACH, HEED, GWO, and FIGWO, improving the half-node-death (HND) round by 518.0%, 200.1%, 111.2%, and 30.5%, respectively. Moreover, EBSIGWO reduces energy variance and slows energy consumption, demonstrating superior energy balance and overall efficiency. These results indicate that EBSIGWO provides an effective solution for reliable fragment velocity measurement applications. Full article

The solidified natural gas (SNG) technology presents a prospective strategy for CH₄ storage and transportation. Low gas storage capacity and slow formation rate remain the key challenges for its field applications. This study suggested a compound system of cyclopentane (CP) + graphite nanoparticle (GNP) nanofluid to enhance the formation kinetics of CH₄ hydrate. Results indicated that both gas consumption and hydrate formation rate were higher at a higher CP concentration, peaking at 14 wt%, where t₉₀ (the time to reach 90% of the final gas uptake) was 65.7 min, and the gas uptake reached 0.1346 mol/mol. However, an excessive CP (21 wt%) negatively affected CH₄ hydrate generation kinetics due to the excessive cage occupancy of CP in 5¹²6⁴ cavities. A lower temperature was determined to be more favorable for CH₄ hydrate formation within nanofluids, which was visually demonstrated by the denser hydrate crystals formed at 275.15 K. Moreover, storage stability analysis revealed that CH₄ hydrate formed in CP + GNP nanofluids can be preserved at atmospheric pressure and 268.15 K without significant decomposition. This work provides a superior scheme for hydrate-based CH₄ storage, offering great contributions to SNG technology advancement. Full article

The consumption of virgin olive oil has been associated with a broad spectrum of beneficial effects. These health outcomes are attributed not only to its high monounsaturated fatty acid content but also to its bioactive components. Nowadays, the flavoring of olive oil has gained popularity to improve its antioxidant properties, modify its sensory characteristics, and enhance its oxidative stability. This study explores spectrofluorometry as a fast, non-destructive, and eco-friendly tool to monitor oxidation and predict shelf life in virgin olive oils (VOOs). Both unflavored and flavored rosemary and basil samples were studied. Over nine months of storage, monthly autofluorescence measurements at 330 nm excitation revealed dynamic spectral changes. These changes were mapped into three distinct emission zones (I, II, and III), providing a spectral fingerprint of oil freshness and stability. Autofluorescence analysis revealed that oxidation-related emission increased while pigment-related emission decreased over time, especially within the first five months. Rosemary and basil flavoring slowed degradation due to antioxidant migration from the herbs. It is proposed that a ratio between the fluorescence intensity of Zone III/Zone II of the spectrum of less than 0.6 indicates oils stored for more than three months. Full article

The construction industry significantly contributes to global sustainability challenges, producing 30–40 percent of global carbon dioxide emissions and consuming large amounts of natural resources. Pervious concrete has emerged as a sustainable alternative to conventional pavements due to its ability to promote stormwater infiltration and groundwater recharge. However, the absence of fine aggregates creates a highly porous structure that results in reduced compressive strength, limiting its broader structural use. Determining compressive strength traditionally requires destructive laboratory testing of concrete specimens, which demands considerable material, energy, and curing time, often up to 28 days—before results can be obtained. This makes iterative mix design and optimization both slow and resource intensive. To address this practical limitation, this study applies Machine Learning (ML) as a rapid, preliminary estimation tool capable of providing early predictions of compressive strength based on mix composition and curing parameters. Rather than replacing laboratory testing, the developed ML models serve as supportive decision-making tools, enabling engineers to assess potential strength outcomes before casting and curing physical specimens. This can reduce the number of trial batches produced, lower material consumption, and minimize the environmental footprint associated with repeated destructive testing. Multiple ML algorithms were trained and evaluated using data from existing literature and validated through laboratory testing. The results indicate that ML can provide reliable preliminary strength estimates, offering a faster and more resource-efficient approach to guiding mix design adjustments. By reducing the reliance on repeated 28-day test cycles, the integration of ML into previous concrete research supports more sustainable, cost-effective, and time-efficient material development practices. Full article

Thin-film lithium niobate (TFLN) electro-optic modulators (EOMs) offer distinct advantages, including high speed, broad bandwidth, and low power consumption. However, their large size hinders the density of integration, which trades off with the half-wave voltage. Photonic crystal (PC) structures can effectively reduce the device footprint via the slow-light effect; however, they experience significant losses due to fabrication defects and sharp corners. Here, we theoretically demonstrate an ultracompact Mach–Zehnder interferometer (MZI) EOM based on a TFLN valley photonic crystal (VPC) structure. The design can achieve a high forward transmittance (>0.8) due to defect-immune unidirectional propagation in the VPC, enabled by the unique spin-valley locking effect. The EOM, with a small footprint of 21 μm × 17 μm, achieves an extinction ratio of 16.13 dB and a modulation depth of 80%. The design can be experimentally fabricated using current nanofabrication techniques, making it suitable for broad applications in optical communications. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

This study addresses the challenges of low-pressure oil well workover operations, namely, severe loss of water-based workover fluid, significant reservoir damage from conventional temporary plugging agents, and slow production recovery, by focusing on the yet-mechanistically unclear “fuzzy-ball workover fluid.” Laboratory experiments combined with field data were used to evaluate its plugging performance and reservoir-protective mechanisms. In sand-filled tubes (diameter 25 mm, length 20–100 cm) sealed with the fuzzy-ball fluid, the formation’s bearing capacity increased by 3.25–18.59 MPa, showing a positive correlation with the plugging radius. Compatibility tests demonstrated that mixtures of crude oil and workover fluid (1:1) or crude oil, workover fluid, and water (1:1:1) held at 60 °C for 80 h exhibited only minor apparent viscosity reductions of 4 mPa·s and 2 mPa·s, respectively, indicating good stability. After successful plugging, a 1% ammonium persulfate solution was injected for 2 h to break the gel; permeability recovery rates reached 112–127%, confirming low reservoir damage and effective gel-break de-blocking. Field data from five wells (formation pressure coefficients 0.49–0.64) showed per-well fluid consumption of 33–83 m³ and post-workover liquid production index recoveries of 5.90–53.30%. Multivariate regression established mathematical relationships among bearing capacity, production index recovery, and fourteen geological engineering parameters, identifying the plugging radius as a key factor. Larger radii enhance both temporary plugging strength and production recovery without harming the reservoir, and they promote production by expanding the cleaning zone. In summary, the fuzzy-ball workover fluid achieves an integrated “high-efficiency plugging–low-damage gel-break–synergistic cleaning” mechanism, resolving the trade-off between temporary-plugging strength and production recovery in low-pressure wells and offering an innovative, environmentally friendly solution for the sustainable and efficient exploitation of oil–gas resources. Full article

Direct Air Capture (DAC) technology plays a crucial role in reducing atmospheric CO₂, but large-scale deployment faces challenges such as high energy consumption, operational costs, and slow material development. This study provides a comprehensive review of DAC principles, including chemical and solid adsorption methods, with a focus on emerging technologies like Metal–Organic Frameworks (MOFs) and graphene aerogels. MOFs have achieved adsorption capacities up to 1.5 mmol/g, while modified graphene aerogels reach 1.3 mmol/g. Other advancing approaches include DAC with Methanation (DACM), variable-humidity adsorption, photo-induced swing adsorption, and biosorption. The study also examines global industrialization trends, noting a significant rise in DAC projects since 2020, particularly in the U.S., China, and Europe. The integration of DAC with renewable energy sources, such as photovoltaic/electrochemical regeneration, offers significant cost-reduction potential and can cut reliance on conventional heat by 30%. This study focuses on the integration of Artificial Intelligence (AI) for accelerating material design and system optimization. AI and Machine Learning (ML) are accelerating DAC R&D: high-throughput screening shortens material design cycles by 60%, while AI-driven control systems optimize temperature, humidity, and adsorption dynamics in real time, improving CO₂ capture efficiency by 15–20%. The study emphasizes DAC’s future role in achieving carbon neutrality through enhanced material efficiency, integration with renewable energy, and expanded CO₂ utilization pathways, providing a roadmap for scaling DAC technology in the coming years. Full article