Search Results (4,506)

Rapid urbanization in China has reshaped the coupling coordination between pollution and carbon emissions. However, existing studies largely rely on linear approaches and lack multidimensional and nonlinear assessments of urban growth patterns. Using panel data for 289 prefecture-level cities from 2010 to 2023, including built-up land, nighttime lights, CO₂ emissions, and PM_2.5 concentrations, this study develops three indicators: Urban Expansion Intensity (UEI), Urban Sprawl Index (USI), and Urban Compactness (UC). By integrating a coupling coordination model, K-means clustering, Geographically and Temporally Weighted Regression (GTWR), and interpretable XGBoost-SHAP analysis, four urban growth patterns are identified: High-Speed Low-Efficiency Expansion (HLE), Low-Speed Low-Efficiency Expansion (LLE), High-Speed High-Efficiency Compact (HHC), and Low-Speed High-Efficiency Compact (LHC). Results indicate that: (1) USI and UC exhibit significant nonlinear threshold effects on CCD; moderate expansion and higher compactness enhance synergy, whereas excessive dispersion or over-compactness weakens coordination. (2) UEI plays a relatively indirect and spatially heterogeneous role. (3) HHC and LHC cities achieve the highest CCD levels, while HLE cities perform the lowest. (4) Urban expansion shows an overall contraction trend, yet substantial regional disparities persist. These findings highlight nonlinear and spatially heterogeneous mechanisms linking urban growth patterns and pollution–carbon coupling coordination, providing implications for differentiated spatial governance. Full article

Quantum dot (QD)-based color conversion layers are key components in QD-OLED displays because they can provide high color purity and simplified pixel architectures by converting blue emission from OLEDs into red or green light. The performance of the color conversion layer strongly depends on the blue light absorption, blue leakage, and overall emission efficiency of the display. We fabricated the color conversion layers using a thermally curable polydimethylsiloxane (PDMS) matrix, and their color conversion characteristics were systematically compared with those of QD-only layers. In the QD-only layers, the intensity of the converted green emission increased with increasing QD concentration due to enhanced absorption of blue light emitted from the OLED. However, a large fraction of blue light was transmitted through the layer without being absorbed by the QDs, resulting in a significant blue leakage and a relatively low output/input efficiency below 10%. In contrast, PDMS-based QD color conversion layers exhibited substantially improved color conversion characteristics. By varying the QD concentration and controlling the layer thickness, blue leakage was significantly suppressed and the green emission intensity increased. The maximum color conversion efficiency of 30.0% was obtained at a QD concentration of 8.3 wt% with a layer thickness of 35.9 µm. Full article

The world’s growing need for energy, fueled by industrial expansion and a rising population, continues to be a challenge for the scientific community. The heavy reliance on fossil fuels that contribute to environmental degradation and public health concerns, is shifting toward sustainable alternatives, with hydrogen production via advanced catalysts as an energy source emerging as a promising solution. This transition addresses the challenges posed by harmful combustion emissions. In this study, we developed an innovative PANI@Cu-NA-MOF nanocomposite catalyst through a sol–gel synthesis approach that strategically integrates conducting polymers with metal–organic frameworks. The catalyst was characterized using different sets of techniques. Surface morphology and elemental composition were investigated using SEM-EDX, while structural analysis was carried out with FTIR that helped to identify the chemical bonds and functional groups, and UV-Vis spectroscopy provided information on its light absorption properties. In addition, TGA was used to evaluate thermal behavior, and XPS offered detailed surface chemical analysis. It was observed by morphology that PANI@Cu-NA-MOF is a noncapsular-like structure. It is thermally highly stable; a TGA study showed that up to 550 °C, almost 2.5% of weight was lost. The single peak in UV-Vis is the preparation of a successful composite. XPS and FTIR reveal the required peaks of functional groups and elements. The PANI@Cu-NA-MOF composite turned out to be quite effective for water electrolysis, requiring an overpotential of just 0.47 V to drive the reaction. When tested against the reversible hydrogen electrode, we observed onset potentials of 1.6 V/RHE for the oxygen evolution reaction and 0.2 V/RHE for the hydrogen evolution reaction. What makes this particularly interesting is that such performance significantly cuts down on the energy needed for electrolysis, which could make hydrogen production much more practical. Since hydrogen burns cleanly and offers a real alternative to fossil fuels, having an efficient catalyst like this brings us one step closer to sustainable energy. Full article

We present a reproducible pipeline that converts color images into quantitative fluorescence maps by combining spectral measurement with a linear mixture model. The method is designed specifically for quantitative comparisons of Glo Germ™ on images of hands taken under different experimental conditions with controlled illumination. The emission spectrum of Glo Germ is measured using a spectral photometer and normalized to obtain its spectral power density function. This spectrum is projected into CIE XYZ coordinates and incorporated into a linear mixture model in which each pixel contains contributions from white light, UV-illuminated skin reflectance, and fluorophore emission. Component magnitudes are estimated with non-negative least squares, yielding a grayscale image whose intensity is a monotonic proxy for local fluorophore density. Spatial integration provides an image-level summary proportional to total detected material. Compared with single-channel proxies, the observer suppresses background structure, improves contrast, and remains radiometrically interpretable. Because the method depends only on measurable spectra and linear transforms, it can be reproduced across cameras and extended to other fluorophores. Full article

Microplastics (MPs), particularly low-density polyethylene (LDPE), are widespread pollutants in aquatic environments and may affect cyanobacterial physiology. This study investigated the concentration-dependent effects of LDPE-MPs on the physiology and transcriptomic responses of Arthrospira platensis. Cultures were exposed to 10–5000 mg/L LDPE-MPs (nominal size ≤ 500 µm) for 16 days. Low to moderate concentrations (10–1000 mg/L) produced minimal effects on growth, biomass accumulation, or pigment contents. In contrast, higher concentrations (3000–5000 mg/L) were associated with reduced growth and biomass, accompanied by declines in chlorophyll a (Chl a) and phycobiliproteins over time. By day 16 at 5000 mg/L, biomass and Chl a decreased to 1.47 ± 0.03 g/L and 8.39 ± 0.24 µg/mL, respectively, compared with 1.64 ± 0.04 g/L and 10.81 ± 0.52 µg/mL in the control (p < 0.05). Accordingly, Chl a yield decreased by 13%. Field-emission scanning electron microscopy revealed adhesion of LDPE particles to filament surfaces and the formation of extracellular polymeric substance (EPS)-rich aggregates, which may influence light availability and nutrient exchange. Transcriptomic analysis indicated changes in several metabolic pathways, including nitrogen assimilation, photosynthetic electron transport, carbon metabolism, and metal homeostasis, together with differential expression of genes related to stress responses and EPS biosynthesis. Overall, these findings suggest that relatively high concentrations of LDPE microplastics may influence physiological and metabolic processes in A. platensis. Full article

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

Hydrophobic carbon quantum dots (hbCQDs) with tunable photoluminescence were synthesized via a solvothermal approach and further hybridized with Rhodamine B (RhB) to extend emission into the visible range. The hbCQDs exhibit quasi-spherical morphology with an average particle size of 8 nm and predominantly disordered graphitic structure, as confirmed by TEM and XRD analyses. FTIR and XPS characterizations reveal surface functional groups including C–N, C=O/C–O, and S–H, which govern the photoluminescence properties. Pure hbCQDs display blue emission at 453 nm under excitation, with a quantum yield (QY) of 6.2%. Incorporation of RhB leads to dual-emission behavior: the surface-state emission remains in the blue region, while molecular-state emission from RhB appears in the orange-red region. The 0.2 mL RhB–CQD composite exhibits optimal properties, including a QY of 13% and a production yield of 82%, emitting white light under 365 nm UV excitation. Increasing RhB loading to 0.4 mL results in a shift in emission peaks and a reduced QY (<9%), with weaker orange fluorescence. These findings demonstrate that controlled RhB hybridization effectively tunes the emission spectrum of hbCQDs, offering a simple and reproducible strategy to achieve dual-color and white-light emission. The optimized hbCQDs/RhB composites hold significant potential for applications in hydrophobic media-compatible organic optoelectronics, light-emitting devices, and bioimaging. Full article

The design of volume holographic gratings (VHGs) is traditionally based on monochromatic plane waves. However, practical applications often involve light sources with broad wavelength bandwidths and certain emission areas, such as LEDs and MiniLEDs, which cause significant Bragg mismatch and degrade diffraction efficiency. To address this fundamental challenge, this paper proposes a novel, to the best of our knowledge, genetic algorithm (GA)-based optimization method for VHG design. A ray-tracing analysis model that fully incorporates the spectral and spatial characteristics of extended broadband sources is established. The GA optimizes the grating fabrication angles by minimizing a fitness function defined as the residual energy after diffraction, thereby achieving optimal performance under non-ideal illumination conditions. The effectiveness of the proposed method is demonstrated through a case study: suppressing the high-intensity central beam in an ultra-thin MiniLED backlight module (BLM). Simulation and experimental results show that the GA-optimized VHG significantly reduces the peak irradiance from 5.01 W/cm² to 4.14 W/cm² at an optical distance (OD) of 0.5 mm. This work provides a robust and source-adaptive design methodology for VHGs, with potential applications extending beyond backlighting to areas such as augmented reality, holographic displays, and optical communications. Full article

The electrification of urban and industrial transport is driving the need for propulsion architectures that combine energy efficiency, operational flexibility and regulatory compliance. However, current electric platforms often lack the adaptability required for customized body configurations and multistage manufacturing, and their approval is hindered by the complexity of meeting electrical safety and electromagnetic compatibility (EMC) requirements at vehicle level. This article presents the conceptual design of a modular electric propulsion module developed within the MODULe project, in which the traction motor, inverter, battery pack, Battery Management System (BMS) and cooling circuits are integrated into a standardized module conceived as an Independent Technical Unit (ITU). The propulsion module dimensioned using a modified WLTP cycle, and the results indicate that the selected components can meet the dynamic demands of light and medium-duty vehicles, achieving an estimated consumption of around 50 kWh/100 km and a driving range above 160 km. By concentrating the critical regulatory requirements within a single module, the proposed architecture facilitates multistage vehicle approval, reduces development effort and supports the scalable electrification of commercial fleets. This approach may contribute to accelerating the deployment of zero-emission vehicles in urban logistics and industrial applications, with potential benefits for both the sector and society. Full article

High-power-density LEDs can achieve many functions that are difficult for traditional light sources and conventional LEDs to realize, pushing the semiconductor lighting technology chain to a new level. Increasing the number of chips is an effective approach to improving the light output capability of LED devices. In this study, five high-power-density LED devices with different chip numbers (4, 9, 16, 25, and 64 chips) were fabricated using the same blue LED chips, and the effects of chip number on the light output capability, spatial light distribution characteristics, and spatially correlated color temperature distribution characteristics of high-power-density LED devices were systematically investigated. The temperature distribution characteristics of the internal chips were further analyzed in combination with infrared thermal imaging results. The results show that increasing the chip number significantly enhances the total light output capability of the device; however, due to the influence of thermal coupling among chips, the saturation current and saturated luminous intensity of devices with different chip numbers are not proportional to the chip number. Increasing the number of chips in the device does not alter the intrinsic spatial emission pattern. Under optical saturation conditions, the luminous intensity distribution curves of all five devices exhibit Lambertian spatial light distribution characteristics. In terms of correlated color temperature, the CCT of all devices increases with increasing current per chip, and devices with more chips exhibit higher CCT values and a faster increasing trend. The spatial CCT distribution results show that the correlated color temperature of the device reaches its maximum in the normal direction, decreases with increasing testing angle, and exhibits good spatial symmetry. The thermal imaging results show that devices with more chips exhibit higher average chip temperatures and a relatively more uniform temperature distribution, which improves the spatial CCT uniformity of the device to some extent. Full article

Bike sharing is an established component of urban mobility infrastructure, offering a low-emission alternative to motorized transport for short trips in cities worldwide. Accurate demand forecasting is essential for efficient system operation: it enables better bike redistribution, reduces user wait times, and lowers the operational costs associated with rebalancing. This study evaluated multiple ensemble strategies for hourly bike-sharing demand prediction, comparing bagging methods (Random Forest, Extra Trees), boosting methods (AdaBoost, Gradient Boosting Regressor, Histogram-based Gradient Boosting Regressor), and a Voting ensemble, while systematically investigating the impact of hyperparameter optimization. A repeated hold-out protocol was used, in which the dataset was randomly divided into 80% training and 20% test subsets across 10 random splits; 5-fold cross-validation was applied within each training fold exclusively for hyperparameter tuning, ensuring the test set remained unseen during model selection. Random Search and Bayesian Optimization were compared under identical budgets of 60 configurations per model. Results show that optimization substantially improves all models, with the most pronounced gains for AdaBoost (58% RMSE reduction) and Gradient Boosting Regressor (45% RMSE reduction). A Voting ensemble combining a Random Search-tuned Gradient Boosting Regressor and a Bayesian-optimized Histogram-based Gradient Boosting Regressor achieves the best overall performance (RMSE of 38.48, R² of 0.955) with the lowest variance among all repeated splits. Feature importance analysis confirms that hour of day and temperature are the dominant demand drivers, consistent with the operational patterns of urban bike-sharing systems. The performance difference between Random Search and Bayesian Optimization is negligible for most models, suggesting that well-designed search spaces allow simpler strategies to achieve competitive results. A controlled comparison conducted under identical experimental conditions shows that the Voting ensemble is statistically equivalent to XGBoost and nominally better than LightGBM, while CatBoost achieves a statistically significant advantage, highlighting it as a strong individual alternative. Full article

Magneto-fluorescent carbon quantum dots (CQDs) promise compact, dual-readout nanomaterials; however, achieving pronounced photoluminescence alongside magnetic functionality in a simple, scalable formulation remains difficult, especially for emerging doped CQDs. Here, we report Fe-doped carbon quantum dots (Fe-CQDs) as an emerging quantum-dot platform that integrates fluorescence with magnetic-resonance (MR) relaxometry within a single ultrasmall, carbonaceous nanostructure. To enable this, Fe-CQDs are prepared through a straightforward two-step, low-temperature route that uses a magnetic deep eutectic solvent precursor followed by mild carbonization in air at atmospheric pressure. Under UV excitation, the Fe-CQDs display bright blue emission centered at 439 nm, and their optical behavior is characterized by UV-Vis absorption, photoluminescence spectroscopy, and fluorescence microscopy. Meanwhile, dynamic light scattering indicates a narrowly distributed nanoscale hydrodynamic diameter, and X-ray diffraction together with FT-IR supports a carbonaceous framework enriched with oxygenated surface functionalities, consistent with aqueous dispersibility and environmentally responsive photophysics in water, while XPS supports Fe incorporation in an Fe(III)-dominated chemical environment. Importantly, Fe incorporation enables intrinsic MR relaxometric readout, establishing an intrinsic fluorescence/MR dual modality. As a proof-of-concept, Fe-CQDs were tested with a representative per- and polyfluoroalkyl substance (PFAS), showing parallel fluorescence and MR response trends at ppm levels in natural water matrices from Millerton Lake with Stern–Volmer analysis and a NaCl-based ionic strength control. Overall, these results position Fe-CQDs as a versatile magneto-fluorescent nanomaterial for dual-readout screening workflows and motivate future surface engineering and dopant tuning to improve selectivity and expand toward multi-modal readouts. Full article

In order to reach net-zero by 2050, we need to have strong decarbonization policies, especially in hard-to-abate clean-ups like steel (8% of the global emissions), cement (7%), and power generation (30%), and negative emissions through direct air capture (DAC) and bioenergy with carbon capture and storage (BECCS). This review paper summarizes the progress in CO₂ capture, compression, transportation, and storage technologies between 2020 and 2025, including energy penalty (20–40%) and cost (15–30%) reductions, with innovations such as metal–organic frameworks (MOFs), bio-inspired catalysts, ionic liquids, and artificial intelligence (AI)-based optimization. This paper, as a new input into the carbon capture and storage (CCS) field, uses the Weighted Sum Model (WSM) as a multi-criteria decision-making tool to rank the best technologies in the capture, storage, monitoring, and transportation sectors. The weights of the criteria are calculated based on Shannon entropy, and the assessment is performed in three conditions, namely, optimistic, pessimistic, and expected. The weights are computed with sensitivity analysis to make the assessment robust. The viability of key projects, such as Northern Lights (Norway, 1.5 MtCO₂/year), Porthos (The Netherlands, 2.5 MtCO₂/year), Quest (Canada, 1 MtCO₂/year), and Petra Nova (USA, 1.6 MtCO₂/year), is evident, and it is projected that, globally, CCS will reach 49 MtCO₂/year across 43 plants in 2025. The review incorporates socio-economic and environmental justice, including barriers such as high costs ($30–600/MtCO₂), energy penalties (1–10 GJ/tCO₂), and opposition between people (20–40% in EU/US). In comparison with previous reviews, this article has a more comprehensive focus, provides quantitative synthesis through WSM, and discusses the implications for researchers, policymakers, and stakeholders towards achieving faster CCS implementation on the path to net-zero. Full article

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this situation has been widely researched, most UAV landing approaches rely on GNSS assistance or single-mode detection, which limits their robustness and scalability in real-world operations. This study proposes an actively heated thermal helicopter landing pad designed using electrically powered resistive heating elements and a high-emissivity surface coating. Furthermore, optical and thermal images collected during actual UAV flight experiments under daytime and night-time conditions were processed using image fusion techniques with AVGF, DWTF, GPF, LPF, MPF, and HWTF fusions, and their performance in deep learning models was compared. The obtained optical, thermal, and fused datasets are used to train and evaluate deep learning-based helicopter landing pad detection models based on the YOLOv8 architecture. Experimental results show that models trained with single-mode data exhibit limited cross-domain generalisation, while fusion-based learning significantly improves detection robustness in optical and thermal domains. Among the evaluated methods, LPF, MPF and HWTF provide the most consistent performance improvements. The findings indicate that electrically heated thermal helicopter landing pads, when combined with image fusion and deep learning-based detection, can increase the landing detectability of UAVs at night and in low-visibility conditions. This detection-focused approach contributes to UAV flight safety by enhancing the visibility of the landing area without relying on active infrared markers or additional navigation infrastructure. Full article

This work presents a mechanistic modeling approach for simulating methane emissions from triethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (<0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades. Full article