Flight Altitude Research Articles

Using UAV-based multispectral and RGB imagery to monitor above-ground biomass of oat-based diversified cropping

Timely access to crop above-ground biomass (AGB) information is crucial for estimating crop yields and managing water and fertilizer efficiently. Unmanned aerial vehicle (UAV) imagery offers promising avenues for AGB estimation due to its high efficiency and flexibility. However, the accuracy of these estimations can be influenced by various factors, including crop growth stages, the spectral resolution of UAV sensors, and flight altitudes. These factors need thorough investigation, especially in diversified cropping systems where crop diversity and growth stages interplay complexly, challenging the accuracy of AGB estimation. This study aims to estimate AGB of oats planted under different agricultural regimes—monoculture, crop rotation, and intercropping—at various growth stages (jointing, flowering, and grain-filling) and across all stages combined, using multispectral and RGB UAV images collected at different flight altitudes (25 m, 50 m, and 100 m). Three feature selection methods—maximal information coefficient (MIC), least absolute shrinkage and selection operator (LAS), and recursive feature elimination (RFE)—were tested. Four machine learning models—ridge regression (RR), multilayer perceptron (MLP), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost)—were used for estimating AGB. Each feature selection method was combined with each machine learning model (e.g., MIC-RR, MIC-MLP, MIC-LGBM, MIC-XGBoost, LAS-RR) to evaluate their performance. Results revealed that the highest accuracy in AGB estimation was achieved with images acquired at a flight altitude of 25 m. The RFE-MLP model demonstrated superior results during the jointing stage (R² = 0.84, root mean squared error (RMSE) = 217.45 kg/ha, root mean squared logarithmic error (RMSLE) = 0.16, mean absolute percentage error (MAPE) = 4.15 %), the LAS-RR model excelled in the flowering stage (R² = 0.85, RMSE = 263.03 kg/ha, RMSLE = 0.05, MAPE = 14.44 %), and the RFE-XGBoost model was most effective during the grain-filling stage (R² = 0.68, RMSE = 865.03 kg/ha, RMSLE = 0.12, MAPE = 8.88 %). For cross-stage modelling, the RFE-MLP achieved remarkable results (R² = 0.93, RMSE = 680.44 kg/ha, RMSLE = 0.16, MAPE = 12.12 %). This study demonstrates the efficacy of combining feature selection methods with machine learning algorithms to enhance the accuracy of oat AGB estimations. The involvement of multiple cropping patterns enhances the generalizability of our findings, facilitating real-time and rapid monitoring of crop growth in future diversified cropping systems.

A contribution to mitigate NOx and H2O emissions for a hydrogen-powered hypersonic vehicle

AbstractHypersonic transport fueled with liquid hydrogen (LH2-HST) is currently considered as long-term future technology of civil aviation to fly with speeds greater than Mach 5 at stratospheric altitudes of 25–38 km. In this paper, we present a comprehensive methodology to assess the emission mitigation potential (via NOx and H2O) of future LH2-HST through operational measures, considering realistic constraints such as the sonic boom carpet as well as tolerable g-forces acting on the passengers while flying with hypersonic speeds. Both NOx- and H2O-optimal 4D-trajectories are identified by a brute-force algorithm that varies the initial cruise altitude from 30 to 36 km. As case study, the Mach 8 passenger aircraft STRATOFLY-MR3, which was conceptually developed in the framework of the H2020 STRATOFLY project, is operated on a single route from Brussels (BRU) to Sydney (MYA). The findings are highlighted as relative changes regarding MR3's design flight altitude set at 32 km, respectively, 105 000 ft. As scientific contribution, 3D emission inventories are calculated and made publicly available for a world fleet of MR3 aircraft operated along the BRU-MYA route on both NOx- and H2O-optimal mission profiles in the year 2075.

Entropy‐Modulated Short‐Chain Cathode for Low‐Temperature All‐Solid‐State Li−S Batteries

AbstractAll‐solid‐state Li−S batteries (ASSLSBs) due to high theoretical energy density and exceptional safety are highly desirable for electric aircraft. However, as the flight altitude rises, the low‐temperature performance is hampered by inadequate practical capacity. Here, we discover that low‐temperature sulfur utilization is constrained by the multi‐step endothermic conversion reaction. By introducing multi‐chalcogen to modulate the local entropy, a short‐chain molecule cathode is designed to shorten the reduction pathways and enhance low‐temperature discharge capacity. Furthermore, the mismatched lithiation lattice of the short‐chain cathode reduces the decomposition energy barriers, thus enhancing low‐temperature charge/discharge reversibility. The designed short‐chain cathode exhibits high cathode utilization (99.4 %) and cycling stability (400 cycles, 92.2 % retention) at room temperature, as well as delivers excellent discharge capacity (579.6 mAh g−1, −40 °C) and cycling performance (100 cycles, 98.4 % retention, 394.9 Wh kg−1electrode, −20 °C) at low temperature. This study presents new opportunities to stimulate the development of low‐temperature ASSLSBs.

A Likelihood-Based Triangulation Method for Uncertainties in Through-Water Depth Mapping

Coastal environments, which are crucial for economic and strategic reasons, heavily rely on accurate bathymetry for safe navigation and resource monitoring. Recent advancements in through-water photogrammetry have shown promise in mapping shallow waters efficiently. However, robust uncertainty modeling methods for these techniques, especially in challenging coastal environments, are lacking. This study introduces a novel likelihood-based approach for through-water photogrammetry, focusing on uncertainties associated with camera pose—a key factor affecting depth mapping accuracy. Our methodology incorporates probabilistic modeling and stereo-photogrammetric triangulation to provide realistic estimates of uncertainty in Water Column Depth (WCD) and Water–Air Interface (WAI) height. Using simulated scenarios for both drone and airborne surveys, we demonstrate that viewing geometry and camera pose quality significantly influence resulting uncertainties, often overshadowing the impact of depth itself. Our results reveal the superior performance of the likelihood ratio statistic in scenarios involving high attitude noise, high flight altitude, and complex viewing geometries. Notably, drone-based applications show particular promise, achieving decimeter-level WCD precision and WAI height estimations comparable to high-quality GNSS measurements when using large samples. These findings highlight the potential of drone-based surveys in producing more accurate bathymetric charts for shallow coastal waters. This research contributes to the refinement of uncertainty quantification in bathymetric charting and sets a foundation for future advancements in through-water surveying methodologies.

Numerical study on flow and combustion properties of oblique detonation engine in a wide speed range

Ensuring safe flight is a fundamental prerequisite for developing hypersonic propulsion systems. A comprehensive investigation of the steady boundary associated with oblique detonation wave in a wide speed range was conducted, with the aim of exploring the feasibility of oblique detonation engine across a diverse array of flight conditions. In this study, the wedge angle applicable in a wide-speed range was acquired via the analysis of oblique detonation wave polar curve. The configuration of the internal injection oblique detonation engine was subsequently designed and established, considering the effect of fuel-air inhomogeneity and complex wave system interactions within a confined combustor. The compressible Euler equations coupled with a 9-species and 19-step chemical reaction mechanism are employed to simulate the oblique detonation process. Ultimately, the safe flight envelope of an air-breathing vehicle equipped with the internal injection oblique detonation engine is mapped across a broad range of Mach numbers, demonstrating the engine’s capability to operate within the Mach 8 to 12 range. Furthermore, the findings reveal that decreasing either the flight Mach number or altitude results in unsteady oblique detonation wave within the internal injection oblique detonation engine combustor, however, reducing the equivalence ratio can stabilize the oblique detonation wave once again. This study provides valuable guidance for the design and wide-speed-range operation of an internal injection oblique detonation engine.

Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network

Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R2), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm3, respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant’s canopy volumes, which is crucial for implementing site-specific management of strawberry crops.

High-resolution spatiotemporal forecasting of the European crane migration

In this paper we present three different models to forecast bird migration. They are species-specific individual-based models that operate on a high spatiotemporal resolution (kilometres, 15 min-hours), as an addition to radar-based migration forecast models that currently exist. The models vary in complexity, and use GPS-tracked location, flying direction and speed, and/or wind data to forecast migration speed and direction. Our aim is to quantitatively evaluate the forecasting performance and assess which metrics improve forecasts at different ranges. We test the models through cross-validation using GPS tracks of common cranes during spring and autumn migration. Our results show that recordings of flight speed and direction improve the accuracy of forecasts on the short range (<2 h). Adding wind data at flight altitude results in consistent improvements of the forecasts across the entire range, particularly in the predicted speed. Direction forecasts are less affected by adding wind data because cranes mostly compensate for wind drift during migration. Migration in spring is more difficult to forecast than in autumn, resulting in larger errors in flight speed and direction during spring. We further find that a combination of flight behaviours – thermal soaring, gliding, and flapping – complicates the forecasts by inducing variance in flight speed and direction. Fitting those behaviours into flight optimisation models proves to be challenging, and even results in significant biases in speed forecasts in spring. We conclude that flight speed is the most difficult parameter to forecast, whereas flight direction is the most critical for practical applications of these models. Such applications could e.g., be prevention of bird strikes in aviation or with wind turbines, and public engagement with bird migration.

Numerical Modeling Reveals That Resistant Western Corn Rootworm Are Stronger Fliers than Their Susceptible Conspecifics

The hindwing geometry, aspect ratio, and numerical modeling of susceptible, Bt-Corn- and rotation-resistant western corn rootworm (WCR) wings was investigated. All variants had similar hindwing geometries and aspect ratio (AR: 6–7). These AR values correspond to wings suited to lower altitude flights of a shorter distance. These AR values are characteristic of wings that can carry heavier loads and are capable of precision flying. Numerical modeling using the finite element method (FEM) showed that the Bt-Corn-resistant and rotation-resistant WCR hindwings could potentially resist higher wind speeds with minimal deformations compared to conspecific susceptible WCR. Understanding the physiology and dispersal of resistant WCR enables a better understanding of how these variants spread their alleles across large scale agricultural landscapes. This may have important implications for integrated resistant management strategies for WCR.

Going digital: challenges in monitoring marine megafauna when comparing results from visual and digital aerial surveys

Since the first plans to develop offshore wind farms (OWFs), concerns have been raised about the impacts on marine megafauna. Today, it is required to assess these impacts over the whole lifecycle of the OWF. Before construction, initial assessments are often conducted by visual surveys, but subsequent monitoring over the lifecycle of the OWF has to be digital due to safety requirements, leading to challenges in data comparability. The aim of this study was to attempt to establish generalizable intercalibration factors for this transition between visual and digital monitoring methods. To this end, intercalibration surveys were conducted at five different sites and at different times of the year within a site, using both visual monitoring at low-altitude and digital monitoring at both low and high altitudes. We tested the potential for intercalibration of the results based on the ratio of abundance estimated from data collected by the different methods. We explored factors such as the species under study and site-specific conditions that may influence intercalibration. We computed more than 100 intercalibration factors and found that, on average, abundance estimates from digital methods were higher than those from visual methods and that flight altitude for digital monitoring did not significantly influence abundance estimates. Aside from divergent abundance estimates depending on monitoring method, the findings also revealed significant heterogeneity, only one-third of which was explained by contextual factors such as taxonomy or the sea conditions. This outcome presents a pessimistic outlook on the prospect for the intercalibration of results between an initial assessment carried out with visual observations and subsequent monitoring with digital methods after OWF construction and until decommissioning. The high heterogeneity prevents seamless transferability of intercalibration factors and highlights the importance of local context.

Contrail altitude estimation using GOES-16 ABI data and deep learning

Abstract. The climate impact of persistent aircraft contrails is currently estimated to be comparable to that due to aviation-emitted CO2. A potential near-term and low-cost mitigation option is contrail avoidance, which involves rerouting aircraft around ice-supersaturated regions, preventing the formation of persistent contrails. Current forecasting methods for these regions of ice supersaturation have been found to be inaccurate when compared to in situ measurements. Further assessment and improvements of the quality of these predictions can be realized by comparison with observations of persistent contrails, such as those found in satellite imagery. In order to further enable comparison between these observations and contrail predictions, we develop a deep learning algorithm to estimate contrail altitudes based on GOES-16 Advanced Baseline Imager (ABI) infrared imagery. This algorithm is trained using a dataset of 3267 contrails found within Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data and achieves a root mean square error (RMSE) of 570 m. The altitude estimation algorithm outputs probability distributions for the contrail top altitude in order to represent predictive uncertainty. The 95 % confidence intervals constructed using these distributions, which are shown to contain approximately 95 % of the contrail data points, are found to be 2.2 km thick on average. These intervals are found to be 34.1 % smaller than the 95 % confidence intervals constructed using flight altitude information alone, which are 3.3 km thick on average. Furthermore, we show that the contrail altitude estimates are consistent in time and, in combination with contrail detections, can be used to observe the persistence and three-dimensional (3D) evolution of contrail-forming regions from satellite images alone.

Radiometric calibration of UAV multispectral images under changing illumination conditions with a downwelling light sensor

AbstractUnmanned aerial vehicle (UAV)‐acquired multispectral images commonly suffer from radiometric inaccuracies due to changing illumination produced by intermittent cloud cover. This study addressed cloud shadow effects by integrating direct irradiance measurements from a downwelling light sensor into reflectance calculations. Two reflectance calibration methods were proposed as follows: (1) use of a single calibration reference panel for all images (Method 1) and (2) employment of a minimum distance classifier to assign color‐graded in‐field reflectance calibration targets to individual images based on their proximity in irradiance levels (Method 2). Images were acquired from 30 and 75 m flight altitudes with fixed‐exposure and auto‐exposure settings. Conventional photogrammetric calibration of UAV multispectral images inadequately addressed cloud shadows, resulting in orthomosaics with poor to moderate spatial uniformity (R2 = 0.70–0.92) and high mean absolute percentage error (MAPE = 13.52%–49.18%). The proposed calibration methods improved radiometric accuracy, yielding average R2 = 0.99 and 0.98 at 30 and 75 m images, respectively. Method 1 showed superior performance on the red‐edge and near infrared bands, and Method 2 worked best in the blue, green, and red bands. Post‐processing empirical line calibration of orthomosaics from Methods 1 and 2 reduced MAPE in reflectance estimation by at least 50% compared to conventional calibration. The clear sky reference histograms had lower Jensen–Shannon distance, higher Pearson's correlation, and improved intersection ratios with the histograms from the proposed methods, implying high similarity. Vegetation indices calculated with the proposed methods closely matched those from the reference orthomosaic, exhibiting significantly lower MAPE than conventionally calculated vegetation indices.

Impact of Variable Device Structural Changes on Particle Deposition Distribution in Multi-Rotor UAV

The aim of this study was to investigate the effect of structural changes in variable fertilizer application devices on the distribution of particle deposition in UAVs. With the rapid development of drone technology, particularly in particulate spreading, drones have demonstrated significant potential due to their efficiency and precision. This paper evaluates the impact of different variable adjustment modes of the device on particulate deposition distribution through drone spreading experiments and particulate deposition data analysis. In this study, device structure change is the main variable factor, and flight altitude, flight speed and ambient wind speed are single quantitative factors. Experiments were conducted by varying the structure of the device to test the detailed deposition distribution of the device under group a, b, and c structures. Experimental results indicate that by choosing different variable combinations, the spreading device can achieve various fertilizer deposition states to meet regional needs. Among all 27 variable groups, the fertilizer particle deposition data for group b1b2b3 is relatively uniform, with three-quarters of particulate deposition values being 3 g/m2 and the maximum value being 4 g/m2. However, even with a relatively uniform distribution of fertilizer particles, the coefficient of variation for group b1b2b3 remains high (36.5%), with a range of 4.5% to 41%. Under different group adjustments, the particle distribution shows the smallest variability range in group b1b2b3, with a range of 15.71–26.44% and a variability difference of 10.73%. The particle distribution shows the largest variability range in group a1a2b3, with a range of 0.78–35.06% and a variability difference of 34.28%. These research conclusions provide important guidance for the study and practice of drone spreading systems.

Accuracy of fish-eye camera device for aircraft positioning based on laboratory experiment combining computer graphic and 3D Projection

For the management of airborne mobility devices such as airplanes and UAVs, such as noise assessment, it is important to measure the three-dimensional position of the aircraft, and various technologies have been researched and developed. We have developed an Aircraft Positioning Camera (APC) based on an IoT device equipped with a fisheye camera that can mechanically measure aircraft position at a local area with low cost and with high mobility. The performance of this APC is still uncertain as to the limits of the flight altitude and speed of the aircraft that can be measured. Therefore, a detailed study of its performance is needed. Since, it is difficult to standardize the conditions such as weather and ground surface conditions, it is effective to conduct these studies in a laboratory where environmental changes can be controlled. The performance of this APC was examined at an experimental facility where images captured by a 360-degree camera or computer graphics can be projected in three dimensions onto a hemispherical dome.

Computational fluid dynamics analysis of aerodynamic characteristics in long-endurance unmanned aerial vehicles

Long-endurance unmanned aerial vehicles (UAVs) play an increasingly important role in various aspects of societal life. In response to the national emphasis on air force development, a study on the aerodynamic characteristics of long-endurance UAVs was conducted. This paper utilizes SolidWorks software to construct a geometric model based on the MQ-9 UAV, and a CFD method to establish a simulation model for UAV cruising flight. The aerodynamic coefficients, required thrust, and total mass during UAV cruise were calculated, and the variation of aerodynamic coefficients as airflow passes through the UAV was described. A comparative analysis was performed on the effects of different angles of attack, flight speeds, and flight altitudes on aerodynamic efficiency. The study results indicate that at an altitude of 10 km, with a 0° angle of attack, the UAV achieves a lift coefficient of 0.8888, a drag coefficient of 0.0679, and a lift-to-drag ratio of 13.0988. The required thrust is approximately 2236 N, and the total flight mass is approximately 3090 Kg. The angle of attack has the greatest impact on aerodynamic efficiency, followed by flight altitude, while flight speed has the least impact on aerodynamic performance. The lift-to-drag ratio initially increases sharply and then decreases rapidly with increasing angle of attack. It is recommended to control the angle of attack within the range of 0°–6°. The lift-to-drag ratio increases slowly with increasing flight speed, suggesting that the flight speed should be maintained near the maximum cruise speed. The lift-to-drag ratio generally decreases with increasing flight altitude, and it is recommended to maintain the flight altitude within the range of 9 km–11 km.

Characteristics Analysis and Modeling of Integrated Sensing and Communication Channel for Unmanned Aerial Vehicle Communications

As an important part of 6th generation (6G) communication, integrated sensing and communication (ISAC) for unmanned aerial vehicle (UAV) communication has attracted more and more attention. The UAV ISAC channel model considering the space-time evolution of joint and shared clusters is the basis of UAV ISAC system design and network evaluation. This paper introduces the UAV ISAC channel characteristics analysis and modeling method. In the UAV ISAC network, the channel consists of a communication channel and a sensing channel. A joint channel parameter is a combination of all (communication and sensing) multiple path component (MPC) parameter sets, while a shared path is the intersection of the communication path and sensing path that have some of the same MPC parameters. Based on the data collected from a ray-tracing (RT) UAV-to-ground scenario, the joint paths and shared paths of ISAC channels are clustered. Then, by introducing the occurrence and disappearance of clusters based on the birth–death (B–D) process, the space-time evolution of different clusters is described, and the influence of the addition of sensing clusters and the change in flight altitude on the B–D process is explored. Finally, the effects of the sensing cluster and flight altitude on the UAV ISAC channel characteristics, including the angle, time–varying characteristics, and sharing degree (SD), are analyzed. The related UAV ISAC channel characteristics analysis can provide reference for the future development of UAV ISAC systems.

Investigation of Aerodynamic Performance of Multi-element Airfoil in Free Surface Effect

Abstract This study examines the aerodynamic performance of the 30P30N multi-element airfoil flying on a free surface, a numerical computational model is established, the governing equations are addressed through the application of the finite volume method, while the volume of fluid method is utilized for the simulation of two-phase flow. Different mesh strategies and turbulence models are compared, with the numerical methods validated against experimental data. The aerodynamic properties of the 30P30N multi-element airfoil on the free surface at different wavelengths and wave heights are given by numerical computations, which show that the ground effect of the 30P30N multi-element airfoil differ from those on a clean airfoil, the aerodynamic coefficients of the 30P30N multi-element airfoil decrease as flight altitude lowers, with the pitching and drag moment of the slat is opposite to that of the main airfoil and the flap. Additionally, when the 30P30N multi-element airfoil flies over a regular wave surface, the aerodynamic coefficients of each component exhibit periodic behavior.

Experimental study of fuel distribution and combustion characteristics under subatmospheric pressure in an afterburner

The fuel distribution and ignition characteristics of an afterburner with typical components under subatmospheric inlet conditions are important references for improving the performance of the afterburner at high flight altitudes. In this work, lean ignition limits, outlet temperature distributions, fuel droplet size distributions and flame propagation processes in the afterburner were obtained experimentally. The results show that with increasing fuel injection angle, the fuel atomization characteristics improve, but the flame intensity fluctuation increases, and the ignition performance deteriorates; thus, a proper ratio of liquid-phase fuel to vapor-phase fuel is one of the key elements for successful ignition. On the one hand, the increased stabilizer blockage ratio results in a localized flow velocity increase near the trailing edge of the stabilizer, which effectively promotes fuel atomization in the shear layer, resulting in a fuel droplet size reduction of 25–30 μm. On the other hand, the extent of the recirculation zone increases, and a large low-pressure and low-turbulence recirculation zone is not conducive to fuel atomization in the flow direction. During the ignition process in an afterburner with all the typical components, the flame kernel first propagates along the flow direction for a certain distance and then propagates and anchors toward the shear layer. At subatmospheric pressures, the decrease in the intensity of the combustion reaction causes a reduction in the heat released by combustion, and the fuel droplet size increases by 10–25 μm, which requires more heat for evaporation. Therefore, the flame propagates an increased distance along the flow as the pressure decreases. Furthermore, the difficulty of ignition increases, and the ignition equivalence ratio increases by 0.1–0.15.

Three-dimensional numerical simulation and experimental validation of airborne ground-penetrating radar based on CNCS-FDTD method under undulating terrain conditions

Traditional Finite Difference Time Domain (FDTD) approaches face challenges with increased computational demands and errors as terrain complexity and flight altitude rising. This study introduces the Crank-Nicolson-Cycle-Sweep-FDTD (CNCS-FDTD) method, enhancing airborne ground penetrating radar (GPR) simulations over undulating terrains. CNCS-FDTD, as an unconditionally stable implicit algorithm, overcomes these by allowing larger time steps without the constraints of the Courant-Friedrichs-Lewy (CFL) condition. Our research aims to assess how CNCS-FDTD can improve computational efficiency and accuracy in modeling airborne GPR responses across varied terrains. Initial simulations using a three-dimensional graben model indicate that CNCS-FDTD can maintain calculation stability with significantly larger time steps, reducing computational time by 42%. To further verify the reliability of the numerical simulation results, the paper also presents experimental tests of the undulating terrain model. By comparing the numerical and physical simulation results of models under different flight altitudes and terrain conditions, the accuracy of the simulation results is validated.

Entropy-Modulated Short-Chain Cathode for Low-Temperature All-Solid-State Li-S Batteries.

All-solid-state Li-S batteries (ASSLSBs) due to high theoretical energy density and exceptional safety are highly desirable for electric aircraft. However, as the flight altitude rises, the low-temperature performance is hampered by inadequate practical capacity. Here, we discover that low-temperature sulfur utilization is constrained by the multi-step endothermic conversion reaction. By introducing multi-chalcogen to modulate the local entropy, a short-chain molecule cathode is designed to shorten the reduction pathways and enhance low-temperature discharge capacity. Furthermore, the mismatched lithiation lattice of the short-chain cathode reduces the decomposition energy barriers, thus enhancing low-temperature charge/discharge reversibility. The designed short-chain cathode exhibits high cathode utilization (99.4%) and cycling stability (400 cycles, 92.2% retention) at room temperature, as well as delivers excellent discharge capacity (579.6 mAh g-1, -40 °C) and cycling performance (100 cycles, 98.4% retention, 394.9 Wh kg-1electrode, -20 °C) at low temperature. This study presents new opportunities to stimulate the development of low-temperature ASSLSBs.

A comprehensive analysis of new particle formation across the northwest Atlantic: Analysis of ACTIVATE airborne data

New particle formation (NPF) is a critical source of particles and cloud condensation nuclei, yet there are scarce vertically-resolved measurements addressing NPF across different seasons in marine regions. This study leverages a multi-season set of airborne data from the NASA ACTIVATE mission between 2020 and 2022 to examine NPF characteristics over the northwest Atlantic ranging from the polluted U.S. East Coast to as far downwind (>1000 km) as Bermuda. Using the number concentration ratio above 3 and 10 nm (N3:N10) as a NPF indicator, we observe the highest ratios in the coldest months and comparable ratios over Bermuda relative to the U.S. East Coast. Within seasons, the highest and lowest ratios are found immediately above cloud tops and at the lowest possible flight altitudes (∼150 m above sea level), respectively. The ratio of (N3-N10)/N3 ranges from 0.16 to 0.29 depending on altitude, proximity to clouds, and season. The N3:N10 and (N3-N10)/N3 ratios increase with altitude up to as high as 9 km, with a case study showing favorable conditions around relatively thicker and precipitating cloud systems presumably due to high actinic fluxes and reduced aerosol surface area. Regression modeling reveals that increased N3:N10 is influenced most by reductions in temperature, relative humidity, and aerosol surface area. This work emphasizes the importance of both NPF in remote marine regions like Bermuda and vertical heterogeneity that exists in its contribution to aerosol and cloud condensation nuclei number budgets.