Search Results (2,185)

Solar-driven interfacial water evaporation has been recognized as an effective measure to address freshwater scarcity. Photothermal materials lie at the core of this process and have been extensively studied. However, conventional carbon-based materials typically suffer from high thermal emissivity, leading to significant heat loss. Here, we report a tungsten carbide/carbon composite polyvinyl alcohol hydrogel evaporator (PWC) for solar-driven interfacial seawater evaporation. Specifically, a tungsten carbide/carbon (WC/C) composite was synthesized via a straightforward one-step molten salt coating method and exhibited a remarkable photothermal conversion efficiency of 67.1%, attributed to the plasmon resonance absorption effect of WC nanoparticles. When incorporated into a polyvinyl alcohol (PVA) hydrogel via a physical-chemical dual-crosslinking strategy, the resulting PWC evaporator achieved a high evaporation rate of 2.99 kg m⁻² h⁻¹ and a conversion efficiency of 90.9% in a 5 wt% NaCl solution under 1 kW m⁻² illumination. In addition, the evaporator can purify seawater and effectively remove a variety of organic dyes. This study provides a viable strategy for a sustainable freshwater supply. Full article

The inclusion of the cement industry into China’s national carbon emissions trading system in 2025 has fundamentally altered the compliance environment for high-emission enterprises, transforming carbon allowances from passive regulatory instruments into dynamic assets whose management directly affects financial performance. We develop a multi-scenario carbon asset management decision model tailored to the intensity-based benchmarking mechanism adopted by the national market. The model centres on the quota surplus-deficit variable EA4, which is computed from enterprise-level emission intensity relative to the industry benchmark, and decomposes the management problem into sequential selling and buying subproblems linked by coupled decision boundaries. A systematic parameter framework is constructed, and the model is applied to two cement enterprises—Enterprise A, a leading producer with a clear allowance surplus, and Enterprise B, a mid-tier producer operating near the benchmark boundary—through historical backtesting over the 2024–2025 period. Three principal findings emerge. First, the intensity benchmarking mechanism creates a dual-leverage effect whereby a 1.4% improvement in emission intensity (from 0.8112 to 0.8000 t/t) increases the quota surplus by 27%, a nonlinearity not captured by conventional compliance-cost models. Second, the model-driven strategy outperforms traditional experience-based approaches by 36.8% (baseline scenario, +95.20 vs. +69.58 MRMB) and 37.3% (risk scenario, −44.55 vs. −71.08 MRMB), with the improvement rate remaining consistent across both enterprises, suggesting that trading timing outweighs instrument selection in determining compliance cost outcomes. Third, dynamic CEA–CCER allocation captures an incremental 2.33 MRMB through the exploitation of a transient price inversion, a gain invisible to single-instrument strategies. Sensitivity analysis confirms that the relative advantage is robust to carbon price variations (±30%) and CCER offset caps (2–10%), while emission intensity and carry-over allowances represent the most consequential parameters for strategy direction, with EA4 crossing zero near the industry benchmark (I ≈ 0.85). The framework provides actionable decision support for cement and other high-emission enterprises navigating the unified carbon market, and contributes a quantitative methodology to the emerging field of environmental management accounting. This study contributes to Sustainable Development Goal 13 (Climate Action), Goal 7 (Affordable and Clean Energy), and Goal 9 (Industry, Innovation, and Infrastructure) by providing operational tools for decarbonisation in carbon-intensive industries. Full article

The maritime sector accounts for approximately 3% of global greenhouse gas (GHG) emissions and faces binding decarbonization obligations under the International Maritime Organization’s (IMO) Net-Zero Framework and the FuelEU Maritime Regulation. Conventional marine fuels, including very low sulphur fuel oil (VLSFO) and liquefied natural gas (LNG), are insufficient to meet long-term regulatory intensity targets on a well-to-wake (WtW) lifecycle basis, creating an urgent need for credible fuel alternatives. This study investigates ethanol as a primary fuel for marine dual-fuel propulsion systems, assessed across four distinct production pathways, sugar beet, corn, sugarcane, and wheat straw, to determine its full decarbonization potential relative to VLSFO and LNG benchmarks. A simulation-based multi-dimensional evaluation framework is developed and applied, integrating dynamic operational simulation, energy analysis, environmental lifecycle modelling, and regulatory compliance assessment. The framework is calibrated against a high-resolution dataset from an active container ship, with scenario-specific engine data. While ethanol requires 39.1% more fuel mass than VLSFO due to its lower energy density, all four ethanol pathways deliver substantially superior WtW GHG reductions: from 50.2% (corn) to 76.9% (wheat straw), compared with 20.6% for LNG. All ethanol scenarios satisfy FuelEU compliance limits across the 2026–2045 horizon, with wheat straw ethanol achieving a GFI of 22.52 gCO₂e/MJ, compliant marginally with the 2040 IMO target. These findings demonstrate that bio-based ethanol, particularly from lignocellulosic feedstocks, is a technically viable and regulatorily superior alternative to LNG for maritime decarbonization, warranting accelerated research into production scale-up and bunkering infrastructure development. Full article

Rockfall from slope unstable rock masses is a typical geological hazard induced by brittle failure, with abrupt occurrence, limited macroscopic deformation before failure, and a short warning lead time. Conventional static analysis methods are useful for design-stage stability checks, but they cannot continuously capture structural-plane damage or update the stability state in real time. Dynamic evaluation based on structural dynamics links measurable parameters such as natural frequency, damping ratio, mode shape, vibration trajectory, wave velocity, and energy dissipation to the degradation of structural planes. This review synthesizes the dynamic behavior mechanism, parameter system, theoretical models, sensing technologies, and engineering applications for slope unstable rock masses. Different from previous reviews that mainly summarize rockfall monitoring or conventional slope stability analysis, this paper organizes the literature by failure mode, monitoring scale, model assumptions, field validation, uncertainty sources, and engineering applicability. The single-degree-of-freedom models for sliding-, toppling-, and falling-type rock masses, multi-block chain-collapse models, and data-physics dual-driven surrogate models are compared critically. Contact monitoring based on MEMS sensors, non-contact LDV monitoring, acoustic emission, microseismic monitoring, coda wave interferometry, and cloud-edge early-warning architectures are further reviewed. Key challenges include field-scale validation under heterogeneous and anisotropic geological conditions, environmental compensation, robust threshold calibration, and probabilistic linkage between dynamic indicators and failure probability. The review provides guidance for selecting dynamic evaluation models, designing field monitoring systems, and developing full-life-cycle digital-twin platforms for rockfall risk mitigation. Full article

Transportation emissions raise critical environmental justice concerns, yet most studies overlook the distinct inequity patterns between passenger and freight systems. This study aims to compare the spatial disparities and driving mechanisms of exposure injustice from passenger and freight emissions at the U.S. county level. Using 2020 county-level cross-sectional data, we construct an environmental injustice index (EII) and apply spatial autocorrelation analysis, a two-stage multi-task TabNet model, and SHAP interpretation to identify spatial divergence, key determinants, and heterogeneous effects of urban compactness. Results show that passenger EII features continuous regional clustering, while freight EII concentrates along corridors and nodes with limited spatial overlap. Passenger injustice is driven by population density, auto dependence, and public transit, whereas freight injustice is dominated by truck intensity, freight network location, and logistics employment. Urban compactness has dual impacts on passenger injustice but consistently exacerbates freight injustice. These findings highlight the necessity of differentiated governance and provide empirical support for equitable low-carbon transport policies. Full article

As urban energy demand increases and available roof space remains limited, balcony photovoltaic (PV) systems have emerged as a promising distributed renewable energy solution. This study aims to evaluate the multidimensional benefits of these systems in urban residential applications from a dual-carbon perspective. A combination of experimental tests and numerical simulations was used to investigate the effects of installation tilt angles and vertical self-shading in high-rise buildings. A comprehensive assessment model was constructed, integrating technical power generation gains, economic returns, and environmental carbon reduction benefits. The results demonstrate that when comprehensively balancing generation gains, economic viability, and structural safety, the practical optimal installation tilt angle for balcony PV systems is around 30°. The Levelized Cost of Electricity (LCOE) is calculated at 0.050–0.061 USD/kWh. Furthermore, a standard 800 W system operating under Beijing’s climate conditions can reduce carbon emissions by approximately 12.68 tons over its 25-year lifecycle. Therefore, balcony PV systems deliver significant technical, economic, and environmental benefits, serving as a highly feasible strategy to promote low-carbon and sustainable development in high-density cities. Full article

Background/Objectives: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is widely used for non-invasive assessment of coronary artery disease under stress and rest conditions. Although deep learning has shown promise for automated SPECT MPI interpretation, most studies focus on single-tracer datasets and do not explicitly account for tracer-dependent variability. This study developed and evaluated a multi-task deep learning framework with tracer-specific prediction heads for patient-level SPECT MPI classification. Methods: A convolutional neural network with a shared feature encoder and tracer-specific heads was implemented using polar map representations from technetium-99m (Tc-99m) and thallium-201 (Tl-201) studies. Transfer learning from ImageNet was applied. Stress-only, rest-only, and dual-input configurations were evaluated using repeated patient-stratified cross-validation and independent testing. Performance was assessed using ROC-AUC and balanced accuracy. Results: For Tc-99m normal versus abnormal perfusion classification, the stress-only model achieved the highest cross-validation AUC (0.88 ± 0.067) and test AUC of 0.88 [0.67–0.99]. For Tl-201 low-risk versus intermediate/high-risk classification, stress-based models achieved the highest cross-validation AUC (0.88 ± 0.051) and test AUC of 0.80 [0.71–0.89], comparable to dual-input models. In both tracer-specific tasks, stress-phase information showed favorable performance, but the endpoints differed and should be interpreted separately. Conclusions: Stress-phase polar maps provided strong discriminative information within this single-center cohort. These findings should be interpreted in a tracer- and task-specific manner supporting stress-phase imaging as an informative input for AI-based SPECT MPI classification while underscoring the need for external validation before broader clinical generalization. Full article

Single-molecule Förster resonance energy transfer (smFRET) enables direct measurement of nanoscale conformational dynamics and heterogeneity in biomolecules, but quantitative interpretation of smFRET data critically depends on well-controlled excitation geometry, low background fluorescence, robust calibration, and reproducible data-analysis workflows. Prism-based total internal reflection fluorescence (pTIRF) microscopy provides important advantages for such measurements by physically separating excitation and emission paths and generating a highly confined evanescent field, yet practical guidance for implementing reproducible, quantitative pTIRF systems remains fragmented. Here we present a comprehensive, standardized framework for the design, alignment, calibration, validation, and operation of a prism-based TIRF microscope optimized for single-molecule fluorescence measurements. We describe the complete optical architecture for dual-color excitation and detection, establish alignment invariants that ensure reproducible evanescent excitation and stable donor–acceptor channel registration, and detail surface preparation, flow control, and photostabilization strategies required for reliable long-term imaging. Quantitative benchmarking protocols are introduced to evaluate signal-to-noise ratio, photobleaching kinetics, and spectral crosstalk, providing objective criteria for defining optimal operating conditions and instrument performance limits. Finally, we integrate these experimental procedures with an end-to-end single-molecule data-analysis workflow encompassing channel registration, automated and manual trajectory selection, FRET calculation, and kinetic analysis using hidden Markov modeling. The utility of the platform is demonstrated through smFRET measurements of conformational dynamics in a model nucleic acid system. Together, this work provides a reproducible and accessible methodology for implementing prism-based TIRF microscopy as a robust quantitative platform for single-molecule fluorescence studies across a wide range of biomolecular systems. Full article

Aquaculture development increasingly faces the dual requirement of increasing economic output and reducing environmental pressure under limited aquatic resources. Existing studies have examined aquaculture efficiency, environmental performance, and production optimization separately, but region-specific strategies that jointly address economic improvement and environmental-emission mitigation remain insufficiently developed. This study proposes a data-driven modeling and computational framework to identify regional green modes of fishery production, with dual properties of higher economic output and lower environmental-emission intensity. In this framework, data-analysis techniques, including missing-value imputation, regional aquaculture classification, nonlinear variable reconstruction, and Lasso regression, are integrated with scenario-based optimization models under alternative management priorities. By applying the proposed framework to provincial fishery data from China during 2017–2024, the results reveal clear heterogeneity in green fishery production modes across different aquatic-resource systems. In particular, under the economic-priority scenario with emission-reduction constraints, the optimized outputs increase by 11.19% and 6.54% in Zone 1 (an inland freshwater system) and Zone 2 (a coastal-intensive system), respectively. Under the environmental-priority scenario with required economic-growth condition, moderate emission-reduction potential is identified in Zone 1, whereas substantial emission reduction is observed in Zone 2. Furthermore, in view of the determined green fishery strategy by our framework, the nearest-optimum province is identified for each zone. By elasticity analysis, it is further found that technology-extension funding and fishery medicine expenditure are two synergistic production investments in Zones 1 and 2, whereas seedling and feed-related investments display properties of region-specific coordination. Summarily, the proposed computational framework in this paper provides an efficient tool of analyzing the regional green fishery production strategies and the regional heterogeneity in virtue of data-driven modeling and advanced optimization techniques. Full article

For agricultural films, spectral matching, UV protection, and environmental durability are essential for efficient crop production. A self-cleaning silane-crosslinked red/blue dual-emission carbon dot/polyvinyl alcohol composite film (KH/RB-CQDs/PVA) was fabricated via a covalent anchoring strategy. RB-CQDs were synthesized by a two-step hydrothermal method using o-phenylenediamine: initial blue-emitting carbon cores formed, then phosphoric acid-assisted secondary treatment covalently bridged residual precursor-derived red fluorophores onto cores through pyrophosphate bonds, as evidenced by TEM, XPS, ³¹P NMR, HPLC-MS and DFT. This rigid bridging suppressed excessive core growth and energy transfer while spatially separating dual emission, endowing excellent photostability (>95% fluorescence retention after 50 min UV and 30 d storage). Subsequently, KH-560 was employed to construct a robust covalent crosslinked network anchoring RB-CQDs in PVA and forming rough Si-O-Si surface structures, confirmed by SEM and XPS. The resulting film exhibited 16.16% quantum yield, 291% tensile strength enhancement, 95% UV shielding, and <1% contaminant residue. Chlorophyll fluorescence kinetics, gas-exchange analyses, and photosynthetic response curves demonstrated that KH/RB-CQDs/PVA increased the potato net photosynthetic rate by 55.46% and tuber yield by 76% through synergistic optimization of photosystem II electron transport and RuBisCO-mediated carbon assimilation. This work provides a molecular design principle for high-performance intelligent agricultural films. Full article

The global transition to electric mobility is crucial for reducing transportation-related emissions, although there is a scarcity of empirical research on customer adoption psychology in transition economies in Central Asia. This study investigates the economic and structural drivers of electric vehicle purchase intention in the Republic of Uzbekistan. Data collected from prospective customers across large city hubs were analyzed using a dual hierarchical multiple linear regression model, supported by an empirical bootstrapping procedure with 2000 resamples, based on the rational choice theory and bounded rationality. The structural model shows that baseline socio-demographics explain insignificant initial variance ($R^2$ = 0.105); however, the integration of primary theoretical constructs yields a significant incremental variance change ($\Delta R^2$ = 0.096), explaining 20.1% of the total variance. Inferential tracking confirms that government incentives are the only statistically significant driver of the purchase intention (p = 0.009). Conversely, purchase cost (p = 0.251) and charging infrastructure (p = 0.475) lack direct significance. However, partial collinearity and infrastructure expectation effects systematically change these localized contact points. The study concludes that consumer intent in this emerging marketplace is primarily anchored to macro-level institutional policy signaling rather than immediate vehicle-specific characteristics or current physical network constraints. Full article

The catalytic removal of refractory VOCs in gas–solid reactions usually suffers from the formation of toxic byproducts and catalyst deactivation. The advanced oxidation process (AOP) wet scrubber has recently attracted interest in VOCs purification due to its high efficiency and inhibited gaseous byproducts emission. MOFs/metal sulfides (termed M50C50) were designed to activate peroxymonosulfate (PMS) for toluene removal in a wet scrubber. The heterojunction interface synergistically couples MIL-100(Fe) and CoS for dual functions, the M50C50 enabled the rapid transfer the toluene from the gas phase to the aqueous phase, where they were subsequently mineralized by SO₄^•− and •OH radicals. The primary active sites responsible for PMS activation were identified as reducing sulfur species, along with low-valence cobalt and iron species. Over 90% of toluene were removed with a wide pH range, while •OH and SO₄^•− were involved in the mineralization of intermediates. The process showed high mineralization efficiency (75% CO₂ evolution) and effectively reduced the formation of toxic byproducts, underscoring its potential for minimizing secondary pollution risks. This work provides a novel route to designing composite catalysts for deep VOC oxidation via AOP wet scrubbers, greatly facilitating their use in environmental remediation. Full article

Background: Positron emission tomography (PET) is a molecular imaging technique that exploits the β⁺ decay of selected radionuclides to enable non-invasive in vivo investigation of biochemical and physiological processes, including early and subclinical disease alterations. Radiotracers are designed to bind specific molecular targets with high affinity and selectivity. Among the targets to which PET devotes increasing attention are G protein-coupled receptors (GPCRs)—the largest class of transmembrane receptors—which orchestrate a wide spectrum of biological outcomes and are widely implicated in human disease. Objectives: This review analyzes patents published between 2020 and 2025 focusing on GPCR-targeted PET radiotracers, highlighting design strategies, radionuclide selection, and translational perspectives across oncology, central nervous system (CNS) disorders, and inflammatory diseases. Results: Patent activity shows that most GPCR-targeted PET tracers are derived from validated ligands adapted for imaging while preserving affinity and selectivity. Oncology patents mainly favor peptide-based or modular metal–chelator platforms enabling radionuclide flexibility and theranostic extension, whereas CNS tracers rely on drug-like small molecules optimized under strict ADME and blood–brain barrier constraints. Increasing emphasis on non-orthosteric, function-sensitive, and dual-targeting approaches reflects a shift toward interrogating GPCR signaling states, while inflammatory indications remain comparatively underrepresented despite clear biological foundations. Conclusions: Current patent trends consolidate GPCR-targeted PET tracers as well-established diagnostic tools while progressively expanding their clinical utility, both as platforms supporting translational research—informing mechanistic insight and drug development—and as components of emerging theranostic strategies across multiple disease areas. Full article

Given the combined limitations of carbon peaking and carbon neutrality goals, the economic cost of industrial emission reduction in China has become increasingly prominent and regionally differentiated. This study evaluates the shadow prices of CO₂ within the Chinese sector and examines the spatiotemporal evolution of carbon abatement costs across provinces, as well as the underlying influencing mechanisms. To capture the evolution of marginal abatement costs (MAC), we use two non-parametric frameworks based on provincial panel data from 2010 to 2022: slack-based measure (SBM), and the directional distance function (DDF) that accounts for unwanted outcomes. In addition, a fixed effects model with regional and temporal effects was constructed to determine the key determinants of marginal carbon reduction costs. Empirical evidence suggests that: (1) From 2010 to 2022, China’s industrial carbon abatement marginal cost has clearly increased, indicating that emission reduction has gradually shifted from a low-cost stage driven by efficiency improvement to a high-cost stage relying on structural adjustment and advanced technologies. (2) Carbon abatement costs exhibit significant provincial heterogeneity by a small number of high-cost provinces (mainly in developed regions) and a majority of low-cost regions. (3) The industrial carbon emission reduction cost curves in some provinces of China have obvious similar evolution paths, and some areas also show a lagging phenomenon. (4) Carbon emission intensity is the dominant factor influencing abatement costs and presents a significant U-shaped relationship, while urbanization increases cost pressure and trade openness helps reduce abatement costs through structural optimization and technology spillovers. Full article

This paper investigates the optimal control of a stochastic delayed system with infinite delay of general functional type. By introducing a non-anticipative path derivative and its infinite-window dual operator, we formulate the infinitely anticipated backward stochastic evolution equation (IABSEE) as the adjoint equation and establish both necessary and sufficient maximum principles. As applications, we investigate two optimal control problems featuring infinite delay. For both the classical linear-quadratic (LQ) problem and the nonlinear emission control model, the optimal controls are derived explicitly. Full article