Search Results (1,027)

Effective in situ pH sensing holds exciting prospects in environmental and biomedical applications, but still faces a great challenge. Until now, pH sensors with small size, high sensitivity, good stability and repeatability, great biosafety, wide detection range, and flexible structure have rarely been reported. Herein, we propose a novel dual-emission ratiometric fluorescent pH sensor by decorating ethyl cellulose (EC)-encapsulated CdSe/ZnS quantum dots (QDs) and oxazine 170 perchlorate (O170 dye) on the surface of the spider silk. When a 473 nm excitation light is coupled into the pH sensor, the evanescent wave transmitting along the surface of the spider silk will excite the CdSe/ZnS QDs and then the O170 dye based on the fluorescence resonance energy transfer (FRET) effect from the QDs; thus, the pH sensing of the surrounding liquid environment can be achieved in real time by collecting the photoluminescence (PL) spectra of the pH sensor and measuring the emission intensity ratio of the two fluorescent materials. The sensor has also demonstrated a high sensing sensitivity (0.775/pH unit) within a wide pH range of 1.92–12.11, as well as excellent reusability and reversibility, structure and time stability, biocompatibility, and biosafety. The proposed pH sensor has a potential application in an in situ monitor of water microenvironments, cellular metabolism, tumor microenvironments, etc. Full article

In the context of promoting strategies to mitigate the global warming effect resulting from greenhouse gas emissions produced by human activities, ammonia stands out as an important player in the decarbonization of various sectors, including transportation, energy, and other industries. Ammonia is an effective carrier of hydrogen, having three times the volumetric energy density of hydrogen itself. In this study, the authors present findings obtained from a group of experiments and simulations conducted on a diesel engine operating at a constant speed and under different loads, using a dual-fuel method in which ammonia was injected into the intake manifold to partially replace the original diesel fuel. The results demonstrate that it is possible to reduce fuel consumption and CO₂ emissions. NO_x dropped by 40.8% and soot by 13.4% under heavy load, while under light load, they dropped by 50.5% and 23.3%, respectively. Full article

Understanding how transport conditions and the built environment shape urban CO₂ emissions is critical for low-carbon urban development. This study analyses CO₂ emission intensity across fifty major Chinese cities using integrated ODIAC emissions, VIIRS night-time lights, traffic performance indicators, built-environment morphology, population/POI structure, and socioeconomic controls. We develop a GeoAI workflow that couples XGBoost modelling with SHAP interpretation, congestion-based city grouping, and 1 km grid-level GNNWR to map intra-urban spatial non-stationarity. The global model identifies night-time light intensity as the strongest predictor, followed by population density and building density. SHAP results reveal pronounced nonlinearities, with high sensitivity at low–medium levels and diminishing marginal effects as activity and density increase. Although transport indicators are less influential in the aggregate model, their roles differ across congestion regimes: in low-congestion cities, emissions align more consistently with overall activity intensity, whereas in high-congestion cities they respond more strongly to population distribution, motorisation, and built-form intensity, with less stable relationships. Grid-level GNNWR further shows that key mechanisms are spatially uneven within cities, with local effects concentrating in specific cores and corridors or fragmenting across multiple subareas. These findings demonstrate that emission drivers are context-dependent across and within cities. Accordingly, uncongested cities may gain more from activity-related energy-efficiency measures, while highly congested cities may require congestion-sensitive land-use planning, spatial-structure optimisation, and motorisation control. Integrating explainable GeoAI with regime differentiation and spatial heterogeneity mapping provides actionable evidence for targeted low-carbon planning. Full article

The dynamic development of electromobility and tightening emissions regulations are making electric light commercial vehicles an increasingly important element of modern urban transport. The purpose of this article is to analyze and compare selected models of electric light commercial vehicles available on the market in terms of four key operational parameters: range, charging time, payload, and energy consumption. These parameters directly impact the efficiency of vehicle operation in real-world conditions, especially in last-mile transport. The study employed a multi-criteria decision method (MCDM), which evaluated 10 alternatives and objectively assigned criterion weights using the CRITIC method, which takes into account data variability and correlations between criteria. The article presents the interdependencies between these factors, emphasizing the need to find a compromise between maximum range and usable payload, as well as the impact of charging time on vehicle operational availability. The analysis aims to identify design and technological solutions that contribute most to improving the efficiency of electric light commercial vehicles in urban and suburban applications. Full article

Ejecta from core-collapse supernovae interact with the circumstellar medium shed by the progenitor star, producing X-ray emission. Previous studies analyzed the X-ray spectrum of the Type IIb supernova SN 2011dh up to ∼500 days after explosion. Long-term monitoring of X-ray emission provides valuable constraints on supernova evolution and progenitor systems, yet such studies remain rare for Type IIb events due to limited data. Here we present the most comprehensive X-ray light curve of SN 2011dh to date, combining all available Chandra and XMM-Newton data with previously published and newly released Swift observations, extending coverage to ∼5100 days. We measure a luminosity decline consistent with $L_X\propto t^{-0.74\pm 0.04}$ and infer a mass-loss rate of $(1.0$–$2.2)\times {10}^{-6}{M}_{\odot }{\mathrm{yr}}^{-1}$ for $v_w=10\mathrm{km}{\mathrm{s}}^{-1}$, or $(2.0$–$4.4)\times {10}^{-6}{M}_{\odot }{\mathrm{yr}}^{-1}$ for $v_w=20\mathrm{km}{\mathrm{s}}^{-1}$. These estimates agree with earlier results, supporting the interpretation that the X-ray emission has been dominated by an adiabatic reverse shock. The consistency of our late-time results with previous studies demonstrates that SN 2011dh has evolved steadily for nearly 14 years. Full article

Currently, brake particle emissions from traffic are considered one of the dominant sources of particulate matter in the atmosphere. A recent question concerns the contribution to brake particles of Battery Electric Vehicles (BEVs). The present work assesses brake particle emissions by measurements of particle number (PN) and mass (PM) of three light-duty BEVs. One front disc brake of each vehicle has been enclosed in a customized casing with appropriate ventilation for forming the aerosol. All three BEVs have been measured on a two-axis chassis dynamometer. The BEV relying more on electric braking (some 68% of the braking energy was covered by electric braking) had the lowest brake PN emissions over the (emissions) WLTC at 6.4 × 10⁹ km⁻¹ per front brake. This was less than half with respect to the other BEV (where only 52% of the braking energy was electric). PM emissions of the two vehicles were similar at 0.93 mg/km for PM < 12 μm and 0.65 mg/km for PM < 2.5 μm, both for one front brake. However, one of the measured BEVs had extraordinarily high PN emissions, some 23 times higher than the lowest-emitting BEV. The difference in PM was not as high, but was some four times higher. Full article

The continuous tightening of emission regulations and the escalating costs of palladium (Pd) and rhodium (Rh) have renewed interest in platinum (Pt)-based three-way catalysts (TWCs) as cost-effective alternatives for gasoline aftertreatment. However, despite extensive studies on Pt/CeO₂ and Pt/Ba-based formulations, the cooperative roles of Ba and Ce and, in particular, the fundamental influence of the Ba/Ce ratio on oxygen mobility, NO_x storage behavior, and Pt–support interactions remain poorly understood. In this work, we address this gap by systematically tuning the Ba/Ce molar ratio in a series of Pt–Ba–Ce/Al₂O₃ catalysts prepared from Ba(CH₃COO)₂ and CeO₂ precursors, and evaluating their structure–function relationships in both fresh and hydrothermally aged states. Through comprehensive characterization (N₂ physisorption, XRD, XPS, H₂-TPR, NO_x-TPD, SEM, CO pulse adsorption, and dynamic light-off testing), we establish previously unrecognized correlations between Ba/Ce ratio–dependent structural evolution and TWC performance. The results reveal that the Ba/Ce ratio exerts a decisive control over catalyst textural properties, Pt dispersion, and interfacial Pt–CeO₂ oxygen species. Low Ba/Ce ratios uniquely promote Pt–Ce interfacial oxygen and O₂ spillover—providing a new mechanistic basis for enhanced low-temperature oxidation and reduction reactions—while higher Ba loading selectively drives BaCO₃ formation and boosts NO_x storage capacity. A clear volcano-type dependence of NO_x storage on the Ba/Ce ratio is demonstrated for the first time. Hydrothermal aging at 850 °C induces PtO_x decomposition, BaCO₃–Al₂O₃ solid-state reactions forming inactive BaAl₂O₄, and Pt sintering, collectively suppressing Pt–Ce interactions and reducing TWC activity. Importantly, an optimized Ba/Ce ratio is shown to mitigate these degradation pathways, offering a new design principle for thermally durable Pt-based TWCs. Overall, this study provides new mechanistic insight into Ba–Ce cooperative effects, establishes the Ba/Ce ratio as a critical and previously overlooked parameter governing Pt–support interactions and NO_x storage, and presents a rational strategy for designing cost-effective, hydrothermally robust Pt-based alternatives to Pd/Rh commercial TWCs. Full article

Remediated landfills require long-term monitoring due to ongoing processes such as settlement, water infiltration, leachate migration, and biogas emissions, which may lead to cover degradation and environmental risks. Traditional ground-based inspections are often time-consuming, costly, and limited in terms of spatial coverage. This study presents the application of Unmanned Aerial Vehicle (UAV)-based remote sensing methods for the structural assessment of a remediated landfill. A multi-sensor approach was employed, combining geometric data (Light Detection and Ranging (LiDAR) and photogrammetry), hydrological modeling (surface water accumulation and runoff), multispectral imaging, and thermal data. The results showed that subsidence-induced depressions modified surface drainage, leading to water accumulation, concentrated runoff, and vegetation stress. Multispectral imaging successfully identified zones of persistent instability, while UAV thermal imaging detected a distinct leachate-related anomaly that was not visible in red–green–blue (RGB) or multispectral data. By integrating geometric, hydrological, spectral, and thermal information, this paper demonstrates practical applications of remote sensing data in detecting cover degradation on remediated landfills. Compared to traditional methods, UAV-based monitoring is a low-cost and repeatable approach that can cover large areas with high spatial and temporal resolution. The proposed approach provides an effective tool for post-closure landfill management and can be applied to other engineered earth structures. Full article

This study presents the experimental and modelling investigation of the performance of an SI methanol marine engine operating under lean conditions. The effects of spark timing and excess air ratio on combustion characteristics, engine performance, and emissions are explored. Multiple machine learning models, including Support Vector Machines (SVM), Artificial Neural Network (ANN), LightGBM, and Random Forest (RF), are employed to predict the engine performance and emission characteristics. Experimental results show that as spark timing advances, the combustion phase advances, with the burn duration being extended. When the excess air ratio is less than 1.35, there exists an optimal spark timing, corresponding to a maximum brake thermal efficiency. The optimal spark timing exhibits an advancing tendency along with increasing excess air ratio. HC emission is primarily determined by the excess air ratio and shows no significant variation under the different spark timings. NO_x emission is initially increased and then decreased with advancing spark timing. Compared with ANN, LightGBM, and RF, SVM demonstrates a superior predictive accuracy, with R² values for engine performance exceeding 0.98 and R² values for emissions above 0.92. Full article

Ammonia can significantly reduce carbon emissions when used in internal combustion engines. However, pure ammonia is considered difficult to ignite and has a slow flame propagation speed, which makes its application challenging. Furthermore, previous research on pure ammonia engines has been based on bench tests, with no vehicle-level tests reported to date. In this study, an engine was tested using pure ammonia as a single fuel in a range-extended hybrid electric vehicle. First, a pure ammonia hybrid power system was implemented in a light-duty vehicle. By motoring the engine instantly to its optimal operating window, the hybrid mode ensures a rapid transition to stable combustion. The results show that, using pure ammonia, the engine can operate stably within a speed range of 1000–3175 rpm. The engine achieves an output power of 45 kW, with an indicated thermal efficiency exceeding 40% under 3175 rpm. Compared to gasoline, pure ammonia has a longer ignition delay but a similar combustion duration. Pure ammonia requires an earlier spark timing and higher intake temperature. The ammonia and NO remain high even after being treated by a three-way catalyst. This research verifies the feasibility of using pure ammonia as a single fuel in hybrid modes, offering broad application prospects in scenarios such as marine power and stationary power generation. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Small and medium Unmanned Aerial Vehicles (UAVs) are commonly equipped with diverse sensors for situational awareness, including cameras, Frequency-Modulated Continuous-Wave (FMCW) radars, Light Detection and Ranging (LiDAR) systems, and ultrasonic sensors. However, optical systems are constrained by adverse weather and darkness, while the limited detection range of compact FMCW radars-typically a few hundred meters-is often insufficient for higher-speed UAVs, particularly those operating Beyond Visual Line of Sight (BVLOS). This paper presents a Collision Avoidance System (CAS) based on a lightweight pulse radar, targeting medium UAV platforms (10–300 kg MTOM) where installing large, nose-mounted radars is impractical. The system is designed for obstacle detection at ranges of 1–3 km, directly addressing the standoff distance limitations of conventional sensors. Beyond its primary sensing function, the pulse architecture offers several operational advantages. Its lower time-averaged power also results in a reduced electromagnetic footprint, mitigating interference and supporting emission-control objectives. Furthermore, pulse radar offers greater robustness against interference in dense electromagnetic environments and lower power consumption, both of which directly enhance UAV operational endurance. Field tests demonstrated a one-to-one correspondence between visually identified objects and radar detections across 1–3 km, with ${\mathrm{P}}_{\mathrm{FA}}$ = 1.5%, confirming adequate standoff for tens of seconds of maneuvering time, with range resolution of 3.75 m and average system power below 80 W. Full article

Microplastics, plastic particles smaller than 5 mm, are now ubiquitous and represent a form of pollution that threatens ecosystems and human health, infiltrating the environment, air, and food chain. The search for solutions to microplastics requires industrial policies that limit plastic production and technological innovations for removal and recycling. Specifically, this paper reports a sustainable and cost-effective method for the detection of high-density polyethylene (HDPE) and low-density polyethylene (LDPE) microplastics using nitrogen-doped carbon dots (N-CD) synthesized from onion peel and L-cysteine via hydrothermal carbonization. Two precursor ratios (1:1 and 1:0.30 w/w) were evaluated. The resulting N-CDs exhibited bright yellow-green fluorescence (470–500 nm) and excitation-dependent photoluminescence under 365 nm UV light. FTIR and UV-Vis spectroscopy confirmed the presence of nitrogen-containing functional groups and effective graphitization, particularly in the 1:0.30 ratio. Fluorescence imaging revealed stronger intensity and greater stain uniformity in thermally softened MPs treated with 1:0.30 N-CDs, with a peak emission of 10,230.02 a.u. at 2 h and PMT 11—surpassing the 1:1 ratio. Bandgap and absorbance analyses supported the superior optical behavior of the lower-concentration formulation. Image analysis further indicated increased luminescent area over time, and two-way ANOVA confirmed statistically significant effects of heating time and PMT settings (p < 0.05). Compared to traditional filtration staining, thermal-assisted application offered enhanced and stable fluorescence. These findings demonstrate the efficacy of green-synthesized N-CDs for MP detection, with potential scalability and environmental applicability. Future work should explore alternative biomass sources and assess N-CD performance under field conditions to optimize environmental sensing strategies. Full article

Travel planning is a process that allows users to obtain maximum benefit from their time, cost and energy. When planning a route from one place to another, it is an important option to present alternative travel areas on the route. This study proposes a travel route planning (TRP) architecture using a Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) model to improve both travel efficiency and environmental sustainability in route selection. This model incorporates carbon emissions directly into the route planning process by unifying user preferences, location recommendations, route optimization, and multimodal vehicle selection within a comprehensive framework. By merging environmental sustainability with user-focused travel planning, it generates personalized, practical, and low-carbon travel routes. The carbon emissions observed with TRP’s artificial intelligence (AI) recommendation route are presented comparatively with those of the user-determined route. XGBoost, Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), (Extra Trees Regressor) ETR, and Multi-Layer Perception (MLP) models are applied to the TRP model. LSTM is compared with Recurrent Neural Networks (RNNs) and Gated Recurrent Unit (GRU) models. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Normalized Root Mean Square Error (NRMSE) error measurements of these models are carried out, and the best result is obtained using XGBoost and LSTM. TRP enhances environmental responsibility awareness within travel planning by integrating sustainability-oriented parameters into the decision-making process. Unlike conventional reservation systems, this model encourages individuals and organizations to prioritize eco-friendly options by considering not only financial factors but also environmental and socio-cultural impacts. By promoting responsible travel behaviors and supporting the adoption of sustainable tourism practices, the proposed approach contributes significantly to the broader dissemination of environmentally conscious travel choices. Full article

A novel four-in-one (4-in-series) MicroLED-in-Package (MiP4) architecture is demonstrated for the first time, integrating four sub-85 µm blue micro-LED (µ-LED) dies on a transparent glass substrate through a redistribution-layer (RDL) interconnection process. The MiP4 device operates natively at 16 V, eliminating the need for step-down converters and simplifying high-voltage backlight driving circuits. The transparent glass carrier enables efficient light extraction, excellent thermal dissipation, and uniform emission. Electrical and optical characterization of dual- (B2), triple- (B3), and quad-chip (B4) devices shows ideal voltage scalability (8 V, 12 V, 16 V) and stable emission at 450 ± 2 nm with minimal FWHM broadening (22–29 nm). Compared with a commercial LED, the MiP4 delivers 1.8× higher optical power (~41.8 mW) despite its active area being only ~1/70 that of the reference device (20,000 µm² vs. 1,350,000 µm²), yielding a dramatically enhanced luminous flux density of 64 lm/mm² at 50 mA. Furthermore, pulse-driven measurements under 2%, 5%, and 10% duty cycles verify excellent thermal stability and minimal spectral shift (<1 nm), confirming the device’s robustness and energy efficiency. This first-of-its-kind 4-in-1 high-voltage glass-based µ-LED package provides a scalable and manufacturable route toward next-generation ultra-thin, high-brightness Mini-LED backlight and optical communication systems. Full article