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This study aims to design an efficient frost-free air source heat pump evaporator for use in cold regions. The challenges of low performance, slow heat transfer, and poor energy supply efficiency associated with air source heat pump evaporators are addressed. This study analyzed the benefits of combining solar energy and air source heat pumps for heat supply. A three-dimensional model of the evaporator was constructed in ANSYS software, and optimal operating parameters were determined through variable experiments. The experimental results showed that when other factors were controlled constant, the glass cover size increased from 1.7 m to 2.9 m, and the maximum value of heat absorption of the evaporator increased from 2155 W to 2418 W and then decreased to 2340 W. When the wind speed and wind pressure increased from 150Pa to 300Pa, the maximum value of heat absorption of the evaporator increased from 1620 W to 2110 W. In summary, the maximum value of heat absorption of the evaporator was increased from 1,620 W to 2,110 W when the size of the glass cover was 2.5 m*2.5 m*2.5 m. When the wind speed and pressure were 300Pa, the heat absorption efficiency of the evaporator was the highest. This finding not only provides a new idea for the performance optimization of air source heat pump evaporator, but also opens up new possibilities for energy efficiency improvement in cold regions, reflecting the innovative and application value of this study.

Light plays a crucial role in the growth of fruit trees, influencing not only nutrient absorption but also fruit appearance. Therefore, understanding fruit tree canopy light transmittance is essential for agricultural and forestry practices. However, traditional measurement methods, such as using a canopy analyzer, are time-consuming, labor-intensive, and susceptible to external influences, lacking convenience and automation. To address this issue, we propose a novel method based on point clouds to estimate light transmittance, with the Leaf Area Index (LAI) serving as the central link. Focusing on apple trees, we utilized handheld LiDAR for three-dimensional scanning of the canopy, acquiring point cloud data. Determining the optimal voxel size at 0.015 m via standardized point cloud mean spacing, we applied the Voxel-based Canopy Profile method (VCP) to estimate LAI. Subsequently, we established a function model between LAI and canopy light transmittance using a deep neural network (DNN), achieving an overall correlation coefficient R2 of 0.94. This model was then employed to estimate canopy light transmittance in dwarfed and densely planted apple trees. This approach not only provides an evaluation standard for pruning effects in apple trees but also represents a critical step towards visualizing and intelligentizing light transmittance.

The identification of branches and bud points is the key to intelligent pruning of dormant grapevine branches and precise positioning of the pruning point on the branch is an important prerequisite for robotic arm pruning. This study takes Cabernet Sauvignon wine grapes as the experimental object and proposes a depth image-based pruning point localization algorithm based on pruning rules. In order to solve the problem of bud recognition in complex backgrounds, this study adopts a detection method that combines semantic segmentation and target detection. Firstly, the semantic segmentation algorithm PSP-net is used to separate the branches and the main stem from the background and the separated image undergoes two kinds of processing: one is to skeletonize it using the Zhang–Suen thinning algorithm and the other is to identify the buds and obtain the center coordinates of the buds using the target-detection method YOLOv5; finally, combining with the depth information of the depth image, we use the coordinates of the buds to determine the location of the pruning point located on the skeleton image. The results show that PSP-net has better results in segmentation performance with mIoU reaching 83.73%. YOLOv5 performs better in target detection with mAP reaching 81.06% and F1 reaching 0.80. The accuracy of this method in determining the location of pruning points reaches 82.35%. It can provide a method for fruit tree pruning robots to determine the location of pruning points.

Aerogel fibers, characterized by their ultra-low density and ultra-low thermal conductivity, are an ideal candidate for personal thermal management as they hold the potential to effectively reduce the energy consumption of room heating and significantly contribute to energy conservation. However, most aerogel fibers have weak mechanical properties or require complex manufacturing processes. In this study, simple continuous silk fibroin–agarose composite aerogel fibers (SCAFs) were prepared by mixing agarose with silk fibroin through wet spinning and rapid gelation, followed by solvent replacement and supercritical carbon dioxide treatment. Among them, the rapid gelation of the SCAFs was achieved using agarose physical methods with heat-reversible gel properties, simplifying the preparation process. Hydrophobic silk fibroin–agarose composite aerogel fibers (HSCAFs) were prepared using a simple chemical vapor deposition (CVD) method. After CVD, the HSCAFs’ gel skeletons were uniformly coated with a silica layer containing methyl groups, endowing them with outstanding radial elasticity. Moreover, the HSCAFs exhibited low density (≤0.153 g/cm3), a large specific surface area (≥254.0 m2/g), high porosity (91.1–94.7%), and excellent hydrophobicity (a water contact angle of 136.8°). More importantly, they showed excellent thermal insulation performance in low-temperature (−60 °C) or high-temperature (140 °C) environments. The designed HSCAFs may provide a new approach for the preparation of high-performance aerogel fibers for personal thermal management.

Aiming at the problem that the color of tea buds is highly similar to the background in complex scenes and it is difficult to identify the buds, this study proposed an improved YOLOv7 algorithm by replacing the original convolution blocks with Depth Separable Convolution (DS Conv) blocks, and adding Convolutional Block Attention Modules (CBAM) and Coordinate Attention (CA) modules. The method improved mean Average Precision (mAP) by 1.28% and mean Recall (mR) rate by 2.92%, the final mAP and mR reached 96.70% and 93.88%, respectively, and 30.62 Frame Per Second (FPS) of the improved model meets the requirements of real-time detection. The results show that the detection accuracy of the improved YOLOv7 algorithm for tea buds was higher than that of other target detection algorithms, and the detecting performance is not significantly affected by the light conditions, and the recognition accuracy of tea buds at each growing period was excellent and balanced. This study provides experience for the realization of intelligent tea picking.

Abstract Highly efficient blue fluorescent materials have recently attracted great interest for organic light-emitting diode (OLED) application. Here, two new pyrene based organic molecules consisting of a highly rigid skeleton, namely SPy and DPy, are developed. These two blue light emitters exhibit excellent thermal stability. The experiment reveals that the full-width at half-maximum (FWHM) of the emission spectrum can be tuned by introducing different amounts of 9,9-diphenyl-N-phenyl-9H-fluoren-2-amine on pyrene units. The FWHM of the emission spectrum is only 37 nm in diluted toluene solution for DPy. Furthermore, highly efficient blue OLEDs are obtained by thermally activated delayed fluorescence (TADF) sensitization strategy. The blue fluorescent OLEDs utilizing DPy as emitters achieve a maximum external quantum efficiency (EQE) of 10.4% with the electroluminescence (EL) peak/FWHM of 480 nm/49 nm. Particularly, the EQE of DPy-based device is boosted from 2.6% in non-doped device to 10.4% in DMAc-DPS TADF sensitized fluorescence (TSF) device, which is a 400% enhancement. Therefore, this work demonstrates that the TSF strategy is promising for highly efficient fluorescent OLEDs application in wide-color-gamut display field. Graphical Abstract

A large quantity of coal fly ash is generated worldwide from thermal power plants, causing a serious environmental threat owing to disposal and storage problems. In this work, for the first time, coal fly ash is converted into advanced and novel aerogel fibers and high-purity α-Al2O3. Silica–bacterial cellulose composite aerogel fibers (CAFs) were synthesized using an in situ sol-gel process under ambient pressure drying. Due to the unique “nanoscale interpenetrating network” (IPN) structure, the CAFs showed wonderful mechanical properties with an optimum tensile strength of 5.0 MPa at an ultimate elongation of 5.8%. Furthermore, CAFs with a high porosity (91.8%) and high specific surface area (588.75 m2/g) can inherit advanced features, including excellent thermal insulation, stability over a wide temperature range, and hydrophobicity (contact angle of approximately 144°). Additionally, Al2O3 was simultaneously extracted from the coal fly ash to ensure that the coal fly ash was fully exploited. Overall, low-cost woven CAFs fabrics are suitable for wearable applications and offer a great approach to comprehensively use coal fly ash to address environmental threats.

ABSTRACT: With the development of fuel cells, multi-stack fuel cell system (MFCS) for high power application has shown tremendous development potential owing to their obvious advantages including high efficiency, durability, reliability, and pollution-free. Accordingly, the state-of-the-art of MFCS is summarized and analyzed to advance its research. Firstly, the MFCS applications are presented in high-power scenarios, especially in transportation applications. Then, to further investigate the MFCS, MFCS including hydrogen and air subsystem, thermal and water subsystem, multi-stack architecture, and prognostics and health monitoring are reviewed. It is noted that prognostics and health monitoring are investigated rarely in MFCS compared with previous research. In addition, the efficiency and durability of MFCS are not only related to the application field and design principle but also the energy management strategy (EMS). The reason is that the EMS is crucial for lifespan, cost, and efficiency in the multi-stack fuel cell system. Finally, the challenge and development potential of MFCS is proposed to provide insights and guidelines for future research.

The tea industry is one of China’s most important industries. The picking of famous tea still relies on manual methods, with low efficiency, labor shortages and high labor costs, which restrict the development of the tea industry. These labor-intensive picking methods urgently need to be transformed into intelligent and automated picking. In response to difficulties in identification of tea buds and positioning of picking points, this study took the one bud with one leaf grade of the Fuyun 6 tea species under complex background as the research object, and proposed a method based on deep learning, combining object detection and semantic segmentation networks, to first detect the tea buds, then segment the picking area from the tea bud detection box, and then obtain the picking point from the picking area. An improved YOLOX-tiny model and an improved PSP-net model were used to detect tea buds and their picking areas, respectively; the two models were combined at the inference end, and the centroid of the picking area was taken as the picking point. The YOLOX-tiny model for tea bud detection was modified by replacing its activation function with the Mish function and using a content-aware reassembly of feature module to implement the upsampling operation. The detection effects of the YOLOX-tiny model were improved, and the mean average precision and recall rate of the improved model reached 97.42% and 95.09%, respectively. This study also proposed an improved PSP-net semantic segmentation model for segmenting the picking area inside a detection box. The PSP-net was modified by replacing its backbone network with the lightweight network MobileNetV2 and by replacing conventional convolution in its feature fusion part with Omni-Dimensional Dynamic Convolution. The model’s lightweight characteristics were significantly improved and its segmentation accuracy for the picking area was also improved. The mean intersection over union and mean pixel accuracy of the improved PSP-net model are 88.83% and 92.96%, respectively, while its computation and parameter amounts are reduced by 95.71% and 96.10%, respectively, compared to the original PSP-net. The method proposed in this study achieves a mean intersection over union and mean pixel accuracy of 83.27% and 86.51% for the overall picking area segmentation, respectively, and the detecting rate of picking point identification reaches 95.6%. Moreover, its detection speed satisfies the requirements of real-time detection, providing a theoretical basis for the automated picking of famous tea.

In order to improve the obstacle avoidance and endurance capability of quadrotor UAVs performing tasks such as forest inspection and rescue search, this paper proposes improvements to address the problems of too many traversed nodes, too many redundant corners, too-large turning angles and unsmooth generated paths in the traditional A* algorithm in path planning. The traditional A* algorithm is improved by using a segmented evaluation function with dynamic heuristic and weighting processing, a steering cost heuristic function based on heading angle deviation control, a redundant turning points removal strategy, and a quasi-uniform cubic b-spline. Through the test comparison of different complexity map scenarios, it is found that the improved A* algorithm reduces the number of traversed nodes by 64.87% on average, the total turning angle by 54.53% on average, the path search time by 49.64% on average, and the path length by 12.52% on average compared to the traditional A* algorithm, and there is no obvious turning point in the path. The real-world applicability of the improved A* algorithm is confirmed by comparing the effect of different algorithms on obstacle avoidance in a map of a real plantation forest environment.

Bispyribac-sodium (BS) is an herbicide often used to control weeds in rice fields. Hydrogen-rich water (HRW) has recently been recommended for alleviating adverse effects, but whether and how HRW alleviates the injury to rice from exposure to BS is still largely unknown. In this study, a greenhouse hydroponic experiment showed that BS alone could substantially inhibit the plant height and fresh weight of both indica and japonica rice seedlings. For indica rice, its pretreatment with HRW at 75% saturation could markedly alleviate the impact on its size but not so with either 50% or 100% HRW. For japonica rice, all the concentrations of HRW used in this study (50%, 75%, and 100% HRW) were capable of reversing the plant size reductions. Further results revealed that the HRW supplement could increase the activity of antioxidative enzymes, including that of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD), which assist in the effective removal of ROS (reactive oxygen species). Subsequent results demonstrated a weaker inhibition of the acetolactate synthase (ALS) enzyme within five days by BS in rice seedlings pretreated with HRW than those receiving the BS treatment alone, and that the HRW pretreatment can hasten the rate at which BS is degraded in rice. Taken together, these findings strongly suggest that pretreatment with HRW may offer a promising and effective strategy to improve the ability of rice to tolerate BS.

Branch identification is key to the robotic pruning system for apple trees. High identification accuracy and the positioning of junction points between branch and trunk are important prerequisites for pruning with a robotic arm. Recently, with the development of deep learning, Transformer has been gradually applied to the field of computer vision and achieved good results. However, the effect of branch identification based on Transformer has not been verified so far. Taking Swin-T and Resnet50 as a backbone, this study detected and segmented the trunk, primary branch and support of apple trees on the basis of Mask R-CNN and Cascade Mask R-CNN. The results show that, when Intersection over Union (IoU) is 0.5, the bbox mAP and segm mAP of Cascade Mask R-CNN Swin-T are the highest, which are 0.943 and 0.940; as for the each category identification, Cascade Mask R-CNN Swin-T shows no significant difference with the other three algorithms in trunk and primary branch; when the identified object is a support, the bbox AP and segm AP of Cascade Mask R-CNN Swin-T is significantly higher than that of other algorithms, which are 0.879 and 0.893. Next, Cascade Mask R-CNN SW-T is combined with Zhang & Suen to obtain the junction point. Compared with the direct application of Zhang & Suen algorithm, the skeleton obtained by this method is advantaged by trunk diameter information, and its shape and junction points position are closer to the actual apple trees. This model and method can be applied to follow-up research and offer a new solution to the robotic pruning system for apple trees.

Convolutional neural networks have recently experienced successful development in the field of computer vision. In precision agriculture, apple picking robots use computer vision methods to detect apples in orchards. However, existing object detection algorithms often face problems such as leaf shading, complex illumination environments, and small, dense recognition targets, resulting in low apple detection rates and inaccurate localization. In view of these problems, we designed an apple detection model based on lightweight YOLOv4—called Improved YOLOv4—from the perspective of industrial application. First, to improve the detection accuracy while reducing the amount of computation, the GhostNet feature extraction network with a Coordinate Attention module is implemented in YOLOv4, and depth-wise separable convolution is introduced to reconstruct the neck and YOLO head structures. Then, a Coordinate Attention module is added to the feature pyramid network (FPN) structure in order to enhance the feature extraction ability for medium and small targets. In the last 15% of epochs in training, the mosaic data augmentation strategy is turned off in order to further improve the detection performance. Finally, a long-range target screening strategy is proposed for standardized dense planting apple orchards with dwarf rootstock, removing apples in non-target rows and improving detection performance and recognition speed. On the constructed apple data set, compared with YOLOv4, the mAP of Improved YOLOv4 was increased by 3.45% (to 95.72%). The weight size of Improved YOLOv4 is only 37.9 MB, 15.53% of that of YOLOv4, and the detection speed is improved by 5.7 FPS. Two detection methods of similar size—YOLOX-s and EfficientNetB0-YOLOv3—were compared with Improved YOLOv4. Improved YOLOv4 outperformed these two algorithms by 1.82% and 2.33% mAP, respectively, on the total test set and performed optimally under all illumination conditions. The presented results indicate that Improved YOLOv4 has excellent detection accuracy and good robustness, and the proposed long-range target screening strategy has an important reference value for solving the problem of accurate and rapid identification of various fruits in standard orchards.

Abstract Strain sensors for human health monitoring are of paramount importance in wearable medical diagnostics and personal health monitoring. Despite extensive studies, strain sensors with both high durability and stretchability are still desired, particularly with the stability for different environmental conditions. Here, we report a series of strain sensors possessing the graphene network with a high density of intermittent physical interconnections, which produces the relative resistance change by varying the overlap area between the neighboring graphene sheets under stretching and releasing, analogous to the slide rheostat working in electronics. Our in‐situ transmission electron microscope observation reveals the full recoverability of the structure from large deformation upon unloading for ensuring the exceptional cycle stability of our material on monitoring full‐range body movements. The stable response is also demonstrated over wide temperature range and frequency range, because the peculiar dynamic structure can be maintained through the self‐adjustment to the thermal expansion of the bulk material. Based on the working mechanism of graphene “slide rheostat,” the sensing properties of the strain sensor are tailored by tuning the graphene network structure with different mass densities using different concentrations of graphene oxide dispersion, while the stretchability and sensitivity can be separately optimized for different application requirements.

To investigate the unknown (uncertain) tire steering resistance moment and model uncertainties in a multi-axle vehicle electro-hydraulic power steering system (EHPSS), including the steering mechanisms, valve-controlled double hydraulic actuators, and heavy-duty tires, an adaptive robust control algorithm is developed to estimate the tire steering resistance moment online and compensate for the model uncertainties of EHPSS. The proposed control strategy consists of an adaptive control law and a robust controller which are based on a Lyapunov function. The adaptive control law is employed to update estimated values of the (unknown) tire steering resistance moment and parameter uncertainties in the EHPSS, whereas complicated nonlinearities are addressed by the robust controller. All adaptive laws of the EHPSS and controller, which can impede parametric uncertainties and stabilize the EHPSS, are synthesized by backstepping techniques. The comparative simulation results demonstrate that the proposed adaptive robust control scheme can achieve the desired performance for feasible tracking trajectories despite the existence of an uncertain tire steering resistance moment and external disturbance in the EHPSS.

Aerogels are three-dimensional nanoporous materials with outstanding properties, especially great thermal insulation. Nevertheless, their extremely high brittleness restricts their practical application. Recently, although the mechanical properties of silica aerogels have been improved by regulating the precursor or introducing a polymer reinforcer, these preparation processes are usually tedious and time-consuming. The purpose of this study was to simplify the preparation process of these composite aerogels. A silicic acid solution treated with cation exchange resin was mixed with agarose (AG) to gel in situ, and then composite aerogels (CAs) with an interpenetrating network (IPN) structure were obtained by aging and supercritical CO2 fluid (SCF) drying. Compared to previous works, the presented CAs preparation process is briefer and more environmentally friendly. Moreover, the CAs exhibit a high specific surface area (420.5 m2/g), low thermal conductivity (28.9 mW m−1 K−1), excellent thermal insulation properties, and thermal stability. These results show that these CAs can be better used in thermal insulation.

Advanced SiO2–Al2O3 aerogel materials have outstanding potential in the field of thermal insulation. Nevertheless, the creation of a mechanically robust and low-cost SiO2–Al2O3 aerogel material remains a considerable challenge. In this study, SiO2–Al2O3 aerogel based on coal gangue, which is a type of zero-cost inorganic waste, was constructed in porous agarose aerogel beads, followed by simple chemical vapor deposition of trimethylchlorosilane to fabricate SiO2–Al2O3/agarose composite aerogel beads (SCABs). The resulting SCABs exhibited a unique nanoscale interpenetrating network structure, which is lightweight and has high specific surface area (538.3 m2/g), hydrophobicity (approximately 128°), and excellent thermal stability and thermal insulation performance. Moreover, the compressive strength of the SCABs was dramatically increased by approximately a factor of ten compared to that of native SiO2–Al2O3 aerogel beads. The prepared SCABs not only pave the way for the design of a novel aerogel material for use in thermal insulation without requiring expensive raw materials, but also provide an effective way to comprehensively use coal gangue.

Abstract A hydrothermal method for synthesizing ultralong and thin copper nanowires (CuNWs) with average diameter of 35 nm and average length of 100 μm is demonstrated in this paper. The concerning raw materials include copric (II) chloride dihydrate (CuCl2·2H2O), octadecylamine (ODA), and ascorbic acid, which are all very cheap and nontoxic. The effect of different reaction time and different molar ratios to the reaction products were researched. The CuNWs prepared by the hydrothermal method were applied to fabricate CuNW transparent conductive electrode (TCE), which exhibited excellent conductivity-transmittance performance with low sheet resistance of 26.23 Ω/□ $\Omega /\square $ and high transparency at 550 nm of 89.06% (excluding Polyethylene terephthalate (PET) substrate). The electrode fabrication process was carried out at room temperature, and there was no need for post-treatment. In order to decrease roughness and protect CuNW TCEs against being oxidized, we fabricated CuNW/poly(methyl methacrylate) (PMMA) hybrid TCEs (HTCEs) using PMMA solution. The CuNW/PMMA HTCEs exhibited low surface roughness and chemical stability as compared with CuNW TCEs.

Forming fibers for fabric insulation is difficult using aerogels, which have excellent thermal insulation performance but poor mechanical properties. A previous study proposed a novel method that could effectively improve the mechanical properties of aerogels and make them into fibers for use in fabric insulation. In this study, composite aerogel fibers (CAFs) with excellent mechanical properties and thermal insulation performance were prepared using a streamlined method. The wet bacterial cellulose (BC) matrix without freeze-drying directly was immersed in an inorganic precursor (silicate) solution, followed by initiating in situ sol-gel reaction under the action of acidic catalyst after secondary shaping. Finally, after surface modification and ambient drying of the wet composite gel, CAFs were obtained. The CAFs prepared by the simplified method still had favorable mechanical properties (tensile strength of 4.5 MPa) and excellent thermal insulation properties under extreme conditions (220 °C and −60 °C). In particular, compared with previous work, the presented CAFs preparation process is simpler and more environmentally friendly. In addition, the experimental costs were reduced. Furthermore, the obtained CAFs had high specific surface area (671.3 m²/g), excellent hydrophobicity, and low density (≤0.154 g/cm3). This streamlined method was proposed to prepare aerogel fibers with excellent performance to meet the requirements of wearable applications.

Aerogels are nanoporous materials with excellent properties, especially super thermal insulation. However, owing to their serious high brittleness, the macroscopic forms of aerogels are not sufficiently rich for the application in some fields, such as thermal insulation clothing fabric. Recently, freeze spinning and wet spinning have been attempted for the synthesis of aerogel fibers. In this study, robust fibrous silica-bacterial cellulose (BC) composite aerogels with high performance were synthesized in a novel way. Silica sol was diffused into a fiber-like matrix, which was obtained by cutting the BC hydrogel and followed by secondary shaping to form a composite wet gel fiber with a nanoscale interpenetrating network structure. The tensile strength of the resulting aerogel fibers reached up to 5.4 MPa because the quantity of BC nanofibers in the unit volume of the matrix was improved significantly by the secondary shaping process. In addition, the composite aerogel fibers had a high specific area (up to 606.9 m2/g), low density (less than 0.164 g/cm3), and outstanding hydrophobicity. Most notably, they exhibited excellent thermal insulation performance in high-temperature (210 °C) or low-temperature (−72 °C) environments. Moreover, the thermal stability of CAFs (decomposition temperature was about 330 °C) was higher than that of natural polymer fiber. A novel method was proposed herein to prepare aerogel fibers with excellent performance to meet the requirements of wearable applications.

The characteristic parameters of Citrus grandis var. Longanyou canopies are important when measuring yield and spraying pesticides. However, the feasibility of the canopy reconstruction method based on point clouds has not been confirmed with these canopies. Therefore, LiDAR point cloud data for C. grandis var. Longanyou were obtained to facilitate the management of groves of this species. Then, a cloth simulation filter and European clustering algorithm were used to realize individual canopy extraction. After calculating canopy height and width, canopy reconstruction and volume calculation were realized using six approaches: by a manual method and using five algorithms based on point clouds (convex hull, CH; convex hull by slices; voxel-based, VB; alpha-shape, AS; alpha-shape by slices, ASBS). ASBS is an innovative algorithm that combines AS with slices optimization, and can best approximate the actual canopy shape. Moreover, the CH algorithm had the shortest run time, and the R2 values of VCH, VVB, VAS, and VASBS algorithms were above 0.87. The volume with the highest accuracy was obtained from the ASBS algorithm, and the CH algorithm had the shortest computation time. In addition, a theoretical but preliminarily system suitable for the calculation of the canopy volume of C. grandis var. Longanyou was developed, which provides a theoretical reference for the efficient and accurate realization of future functional modules such as accurate plant protection, orchard obstacle avoidance, and biomass estimation.

Spraying is the most widely used means of pesticide application for pest control in agriculture and forestry. The atomization characteristics of the nozzles are directly related to the spray drift, rebound, and deposition. Previous research studies have mainly focused on the change pattern of atomization characteristics. Mathematical descriptions of the atomization characteristics of flat fan nozzles are rare, and pesticide application theories are also insufficient. Atomization characteristics mainly include droplet size and velocity. This study analyzes the influence of the spray parameters (spray angle, pressure, and equivalent orifice diameter of nozzles) and the spatial position in the flow field. To obtain the atomization characteristics of flat fan nozzles, the phase Doppler particle analyzer (PDPA) was selected for the accurate measurement of the droplet sizes and velocities at distances 0.30–0.60 m, using low spray pressures (0.15–0.35 MPa). The droplet size and velocity models were then established and validated. The results revealed that the average absolute error of the droplet size model was 23.74 µm and the average relative error was 8.23%. The average absolute and relative errors of the droplet velocity model were 0.37 m/s and 7.86%, respectively. At a constant spray pressure and angle, there was a positive correlation between the droplet size and the equivalent orifice diameter of the nozzles. The test also verified that the spray angle and distance had a negative correlation with the droplet velocity at a given pressure. The spray distance had no effect on the spray axial droplet size at constant spray pressure. In addition, the spray angle greatly affected the droplet velocity along the X-axis; similarly, the spray parameters, especially spray angle, greatly affected the droplet size.

The applicability of organic Rankine cycle (ORC) technology to waste heat recovery (WHR) is currently experiencing growing interest and accelerated technological development. The utilization of low-to-medium grade thermal energy sources, especially in the presence of heat source intermittency in applications where the thermal source is characterized by highly variable thermodynamic conditions, requires a control strategy for off-design operation to achieve optimal ORC power-unit performance. This paper presents a validated comprehensive model for off-design analysis of an ORC power-unit, with R236fa as the working fluid, a gear pump, and a 1.5 kW sliding vane rotary expander (SVRE) for WHR from the exhaust gases of a light-duty internal combustion engine. Model validation is performed using data from an extensive experimental campaign on both the rotary equipment (pump, expander) and the remainder components of the plant, namely the heat recovery vapor generator (HRVH), condenser, reservoirs, and piping. Based on the validated computational platform, the benefits on the ORC plant net power output and efficiency of either a variable permeability expander or of sliding vane rotary pump optimization are assessed. The novelty introduced by this optimization strategy is that the evaluations are conducted by a numerical model, which reproduces the real features of the ORC plant. This approach ensures an analysis of the whole system both from a plant and cycle point of view, catching some real aspects that are otherwise undetectable. These optimization strategies are considered as a baseline ORC plant that suffers low expander efficiency (30%) and a large parasitic pumping power, with a backwork ratio (BWR) of up to 60%. It is found that the benefits on the expander power arising from a lower permeability combined with a lower energy demand by the pump (20% of BWR) for circulation of the working fluid allows a better recovery performance for the ORC plant with respect to the baseline case. Adopting the optimization strategies, the average efficiency and maximum generated power increase from 1.5% to 3.5% and from 400 to 1100 W, respectively. These performances are in accordance with the plant efficiencies found in the experimental works in the literature, which vary between 1.6% and 6.5% for similar applications. Nonetheless, there is still room for improvement regarding a proper design of rotary machines, which can be redesigned considering the indications resulting from the developed optimization analysis.

Silica aerogels can be strengthened by forming a nanoscale interpenetrating network (IPN) comprising a silica gel skeleton and a cellulose nanofiber network. Previous studies have demonstrated the effectiveness of this method for improving the mechanical properties and drying of aerogels. However, the preparation process is generally tedious and time-consuming. This study aims to streamline the preparation process of these composite aerogels. Silica alcosols were directly diffused into cellulose wet gels with loose, web-like microstructures, and an IPN structure was gradually formed by regulating the gelation rate. Supercritical CO2 drying followed to obtain composite aerogels. The mechanical properties were further enhanced by a simple secondary regulation process that increased the quantity of bacterial cellulose (BC) nanofibers per unit volume of the matrix. This led to the production of aerogels with excellent bendability and a high tensile strength. A maximum breaking stress and tensile modulus of 3.06 MPa and 46.07 MPa, respectively, were achieved. This method can be implemented to produce robust and bendable silica-based composite aerogels (CAs).

In-wheel motored powertrain on electric vehicles has more potential in maneuverability and active safety control. This paper investigates the longitudinal and lateral integrated control through the active front steering and yaw moment control systems considering the saturation characteristics of tire forces. To obtain the vehicle sideslip angle of mass center, the virtual lateral tire force sensors are designed based on the unscented Kalman filtering (UKF). And the sideslip angle is estimated by using the dynamics-based approaches. Moreover, based on the estimated vehicle state information, an upper level control system by using robust control theory is proposed to specify a desired yaw moment and correction front steering angle to work on the electric vehicles. The robustness of proposed algorithm is also analyzed. The wheel torques are distributed optimally by the wheel torque distribution control algorithm. Numerical simulation is carried out in Matlab/Simulink-Carsim cosimulation environment to demonstrate the effectiveness of the designed robust control algorithm for lateral stability control of in-wheel motored vehicle.

Xylan/poly(methacrylic acid) semi-interpenetrating network hydrogels were synthesized, and these hydrogels were fabricated using various ratios of xylan and methacrylic acid and cross-linked by N,N′-methylenebisacrylamide. The chemical structure and morphology of the prepared hydrogels were characterized by FT-IR spectroscopy and SEM analysis. The porous structure of the prepared hydrogels was found, and the interconnected porous channels increased when the content of xylan was increased in the hydrogels. The hydrogels presented excellent pH sensitivity and swelling reversibility. The dynamic swelling kinetics were also studied, and all obtained results indicated that the prepared xylan/poly(methacrylic acid) semi-IPN hydrogel could be used in biomedical fields, especially for drug release.