Aerogel fibers are good thermal insulators, suitable for weaving, and show potential as the next generation of intelligent textiles that can effectively reduce heat consumption for personal thermal management. However, the production of continuous aerogel fibers from biomass with sufficient strength and radial elasticity remains a significant challenge. Herein, continuous gel fibers were produced via wet spinning using agarose (AG) as the matrix, 2,2,2,6,6-tetramethylpiperidine-1-oxyl radical-oxidized cellulose nanofibers (TOCNs) as the reinforcing agent, and no other chemical additives by utilizing the gelling properties of AG. Supercritical drying and chemical vapor deposition (CVD) were then used to produce hydrophobic AG-TOCN aerogel fibers (HATAFs). During CVD, the HATAF gel skeleton was covered with an isostructural silica coating. Consequently, the HATAFs can recover from radial compression under 60% strain. Moreover, the HATAFs have low densities (≤0.14 g cm-3), high porosities (≥91.8%), high specific surface areas (≥188 m2 g-1), moderate tensile strengths (≤1.75 MPa), excellent hydrophobicity (water contact angles of >130°), and good thermal insulating properties at different temperatures. Thus, HATAFs are expected to become a new generation of materials for efficient personal thermal management.
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.
Although being applied in various fields, white light emitting diodes (WLEDs) still have drawbacks that urgently need to be conquered: the luminescent intensity of commercial phosphors sharply decreases at working temperature. In this study, we calculated the forming energy of defects and confirmed that the VNa defect state can stably exist in ß-NaGdF4, by density functional theory (DFT) calculation. Furthermore, we predicted that the VNa vacancies would provide a zero thermal quenching (ZTQ) property for the ß-NaGdF4-based red-light phosphor. Then, a series of ß-NaGdF4:xEu3+ and ß-NaGdF4:0.25Eu3+,yYb3+ red-light phosphors were synthesized by the hydrothermal method. We found that ß-NaGdF4:0.25Eu3+ and ß-NaGdF4:0.25Eu3+,0.005Yb3+ phosphors possess ZTQ properties at a temperature range between 303-483 K and 303-523 K, respectively. The thermoluminescence (TL) spectra were employed to calculate the depth and density of the VNa vacancies in ß-NaGdF4:0.25Eu3+ and ß-NaGdF4:0.25Eu3+,0.005Yb3+. Combining the DFT calculation with characterization results of TL spectra, it is concluded that electrons stored in VNa vacancies are excited to the exited state of Eu3+ to compensate for the loss of Eu3+ luminescent intensity. This will lead to an increase of luminescent intensity at high temperatures and facilitate the samples to improve ZTQ properties. WLEDs were obtained with CRI = 83.0, 81.6 and CCT = 5393, 5149 K, respectively, when phosphors of ß-NaGdF4:0.25Eu3+ and ß-NaGdF4:0.25Eu3+,0.005Yb3+ were utilized as the red-light source. These results indicate that these two phosphors may become reliable red-light sources with high antithermal quenching properties for WLEDs.
We present ReGO (Reference-Guided Outpainting), a new method for the task of sketch-guided image outpainting. Despite the significant progress made in producing semantically coherent content, existing outpainting methods often fail to deliver visually appealing results due to blurry textures and generative artifacts. To address these issues, ReGO leverages neighboring reference images to synthesize texture-rich results by transferring pixels from them. Specifically, an Adaptive Content Selection (ACS) module is incorporated into ReGO to facilitate pixel transfer for texture compensating of the target image. Additionally, a style ranking loss is introduced to maintain consistency in terms of style while preventing the generated part from being influenced by the reference images. ReGO is a model-agnostic learning paradigm for outpainting tasks. In our experiments, we integrate ReGO with three state-of-the-art outpainting models to evaluate its effectiveness. The results obtained on three scenery benchmarks, i.e. NS6K, NS8K and SUN Attribute, demonstrate the superior performance of ReGO compared to prior art in terms of texture richness and authenticity. Our code is available at https://github.com/wangyxxjtu/ReGO-Pytorch.
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.
The rapid advancement of nanotechnology has opened up new avenues for applications in all stages of the food industry. Over the past decade, extensive research has emphasized that when nanoparticles (NPs) enter organisms, they spontaneously adsorbed biomolecules, leading to the formation of biocorona. This paper provided a detailed review of the process of biocorona formation in the food industry, including their classification and influencing factors. Additionally, various characterization methods to investigated the morphology and structure of biocoronas were introduced. As a real state of food industry nanoparticles in biological environments, the biocorona causes structural transformations of biomolecules bound to NPs, thus affecting their fate in the body. It can either promote or inhibit enzyme activity in the human environment, and may also positively or negatively affect the cellular uptake and toxicity of NPs. Since NPs present in the food industry will inevitably enter the human body, further investigations on biocoronas will offer valuable insights and perspectives on the safety of incorporating more NPs into the food industry.
Human Body , Nanoparticles , Humans , Nanoparticles/toxicity , Nanoparticles/chemistry , Food Industry
Lotus seedpod oligomeric procyanidins (LSOPC) are potent inhibitors of advanced glycation end products (AGEs), whose gastrointestinal susceptibility to degradation limits their use in vivo. In this study, carboxymethyl chitosan-lotus seedpod oligomeric procyanidin nanoparticles (CMC-LSOPC NPs) were constructed with a binding ratio of 1:6.51. CMC-LSOPC NPs significantly inhibited the release of AGEs from glycated casein (G-CS) during digestion, increasing the inhibition rate by 25.76% in the gastric phase and by 14.33% in the intestinal phase compared with LSOPC alone. To further investigate the inhibition mechanism, fluorescence microscopy, scanning electron microscopy and FTIR were used to find that CMC-LSOPC NPs could form cohesions to encapsulate G-CS in the gastric phase and hinder G-CS hydrolysis. In the intestinal phase, LSOPC was targeted for release and bound to trypsin through hydrophobic interactions and hydrogen bonding, resulting in protein peptide chain rearrangement, changes in secondary structure and significant reduction in trypsin activity. In addition, CMC-LSOPC NPs increased the antioxidant capacity of digestive fluid and could reduce the oxidative stress in the gastrointestinal tract caused by the release of AGEs. It's the first time that CMC-LSOPC NPs were constructed to enhance the stability of LSOPC during digestion and explain the mechanism by which CMC-LSOPC NPs inhibit the release of AGEs from G-CS in both stomach and intestine. This finding will present a novel approach for reducing AGEs during gastrointestinal digestion.
Chitosan , Lotus , Nanoparticles , Proanthocyanidins , Dietary Advanced Glycation End Products , Caseins/analysis , Proanthocyanidins/analysis , Lotus/chemistry , Chitosan/chemistry , Trypsin/analysis , Digestion , Nanoparticles/chemistry , Seeds/chemistry
Since recent facial landmark localization methods achieve satisfying accuracy, few of them enable fast inference speed, which, however, is critical in many real-world facial applications. Existing methods typically employ complicated network structure and predict all the key points through uniform computation, which is inefficient since individual facial part might take different computation to obtain the best performance. Taking both accuracy and efficiency into consideration, we propose the PicassoNet, a lightweight cascaded facial landmark detector with adaptive computation for individual facial part. Different from the conventional cascaded methods, PicassoNet integrates refinement submodules into a single network with group convolution, where each convolution group predicts landmarks from an individual facial part. Note that the groups' structures are flexible in the training process. Then, a novel grouping search algorithm is proposed to optimize the group division. With formulating the optimization as a network architecture search (NAS) problem, the grouping search adaptively allocates computation to each group and obtains an efficient structure. In addition, we propose a boundary-aware loss to optimize along tangent and normal of facial boundaries, instead of optimizing along horizontal and vertical as the conventional loss (L2, SmoothL1, WingLoss, and so on) do. The novel loss improves the joint locations of predicted keypoints. Experiments on three benchmark datasets AFLW, 300W, and WFLW show that the proposed method runs over 6× times faster than the state of the arts and meanwhile achieves comparable accuracy.
Currently, biomethane obtained from coal resources, such as lignite and peat, serves as a sustainable biofuel urgently needed by the energy economy. To improve biomethane yield from lignite, the effects of different concentrations of benzyl alcohol, a degraded product of humic acid, on a biomethanation fermentation system were analyzed. The total biomethane yield, daily biomethane yield, coenzyme F420, VFA (volatile fatty acids) concentration, alkalinity, and pH were used to determine the optimal benzyl alcohol concentration. The biomethanation fermentation system with 2000 mg/L benzyl alcohol produced up to 4.03 mL/g of biomethane, which was 1.15 times that produced from the control group. The coenzyme F420, VFA, alkalinity, and pH of the system after adding 2000 mg/L benzyl alcohol were more preferable after adding other concentrations during the lignite biomethanation process. In summary, 2000 mg/L benzyl alcohol had a significantly positive effect on the lignite biomethanation fermentation system. When benzyl alcohol is added to the fermentation system, it accelerates the tricarboxylic acid cycle, which in turn produces more biomethane. Additionally, the self-supply of lignite microbial transformation nutrients from the perspective of chemical composition was explored as a novel approach in solving the common problem of low biomethane yield from a single lignite raw material. This also laid a foundation for subsequent steps through the adjustment of pretreatment conditions to ensure that the lignite pretreatment liquid contained increased benzyl alcohol, and a greater yield of biomethane can be produced after activated sludge addition.
Visual Geometry Group (VGG)-style ConvNet is an neural-network process units (NPU)-friendly network; however, the accuracy of this architecture cannot keep up with other well-designed network structures. Although some reparameterization methods are proposed to remedy this weakness, their performance suffers from the homogenization issue of parallel branches, and the preset shape of convolution kernels also influences spatial perception. To address this problem, we propose a diversity-learning (DL) block to build the DLNet, which could adaptively learn various features to enrich the feature space. To balance floating point of operations (FLOPs) and accuracy, groupwise operation is introduced and finally, a lightweight DL ConvNet DLGNet is obtained. Extensive evaluations have been conducted on different computer vision tasks, e.g., image classification Canadian Institute For Advanced Research (CIFAR) and ImageNet, object detection PASCAL visual object classes (VOC) and Microsoft Common Objects in Context (MS COCO), and semantic segmentation (Cityscapes). The experimental results show that our proposed DLGNet can achieve comparable performance with the state-of-the-art networks while the speed is 183% faster than GhostNet and even over 600% faster than MobileNetV3 with similar accuracy when running on NPU.
For the purpose of obtaining red-light phosphors with excellent luminescence thermal stability, a series of Gd4Al2O9:Eu3+ (GAO:Eu3+) phosphors were synthesized by combining the sol-gel method with high-temperature calcination, and a detailed series of study and analysis of their room temperature and high temperature luminescence properties was carried out. In GAO, the emission peaks corresponding to the 5D0 â 7F j (j = 0, 1, 2, 3 and 4) transitions of Eu3+ were observed at 578, 590, 610, 654, and 707 nm, with the strongest emission peak at 610 nm, and the obtained samples were red-light phosphors. The sample GAO:Eu3+ synthesized by combining the sol-gel method with high-temperature calcination has a negative thermal quenching (NTQ) effect, and the best doped sample GAO:0.16Eu3+ has an optimal luminescence temperature of 120 °C, and the corresponding integrated PL intensity is 183.2% of the initial value at 30 °C. The presence of the NTQ effect makes GAO:0.16Eu3+ have good luminescence thermal stability, which manifests as thermal-optical energy conversion at the macroscopic level. A detailed study of the thermal quenching mechanism was carried out.
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.
The fan nozzle is widely used in the process of pest control in agriculture and forestry. The spray angle of the nozzle is an important characterization parameter in the atomization of liquids. The spray angle of the nozzle is an important characterizing parameter in the liquid atomization process. It affects the flow field at the exit of the nozzle, thereby affecting the size and velocity of the droplets, and further affecting the deposition effect of the droplets on the crop. Therefore, its research is of great significance for improving the deposition of liquid on plants and controlling pests and related diseases. Based on the classical theory of predecessors and considering the parameters of the flat fan nozzle, we further optimized the theory at the structural level by means of a simulation test and built a spray angle theoretical model taking into account the parameters of the inner chamber of the nozzle. We arrived at the following conclusions: (1) the average error of the spray angle measured by the simulation test and the actual test spray angle was 2.95%, the maximum spray angle deviation value was 2.81°, and the result proves that the simulation test parameter setting is accurate; and (2) the average error between the actual measured value and the theoretical model calculation value was 3.56%, the maximum spray angle deviation was 4°, through the actual test comparison, and the spray angle error of the theoretical model was within the allowable error range of industry production. It was proved that the model could effectively reflect the changing law of spray angle of the flat fan nozzle.
Agriculture , Pest Control , Models, Theoretical , Particle Size , Physical Phenomena
Based on the theory of biological optimal particle size, the most easily attached droplets for different organisms have different particle sizes. To achieve the best average particle size, the droplet size in the atomization field must be more uniform and attain a high the adhesion rate. Therefore, during the application process, not only the average particle size of the droplets but also the influence of the uniformity of the droplets in the spray field must be considered. In this study, 20 small-angle fan nozzles ranging from 20° to 40° are used as the research objects. The droplet size information in the atomization field is obtained using a laser particle size analyzer, and the droplet uniformity under different parameters is calculated. The results showed that within the range of the parameters selected in the experiment, the droplet size increased with an increase in the flow rate, and decreased with an increase in the pressure. In addition, the angle had little effect on the droplet size. Increasing the spray height, spray angle, and pressure, while reducing the equivalent outlet diameter of the nozzle was beneficial to improve the uniformity of droplets. The order of the degree of influence of the four parameters on the uniformity of the droplets was height > equivalent outlet diameter (r) > pressure > spray angle, and the influence weights were 51.1%, 37.1%, 7.8%, 4.1%; 48.4%, 37.6%, 10%, and 4%. Under the condition of the parameter settings used in this experiment, the optimal atomization effect for the four intervals of 150-200 µm, 200-250 µm, 250-300 µm, and 300-400 µm was analyzed from the perspective of uniformity. The nozzle models with the best atomization effects in each interval were SS4003-0.3 MPa, SS4006-0.3 MPa, SS4008-0.3 MPa, and 633.512.30.CC-0.1 MPa.
Particle Size
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.
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.
A novel solar energy storage heating radiator (SESHR) prototype filled with low-temperature phase change material (PCM) has been developed to accommodate the urgent demand in thermal storage and the fluctuation in renewable energy utilization. This equipment integrated by several independent heat storage units (HSUs) and water and paraffin wax was used as a heat transfer fluid and an energy storage material, respectively. The experimental test platform for low-temperature SESHR was designed and established. The total storage/dissipation time, average storage/dissipation capacity, and the rate and overall thermal efficiency were investigated under different operating conditions. Experimental results showed that a higher temperature difference between the heat source and the melting point of the PCM could significantly improve the heat storage capacity and rate. The heat dissipation rate of the SESHR could be controlled by adjusting the opening ratio of the air convective channel. The average storage rate of the SESHR with 2#PCM reached 1106 W at a heat source temperature of 85 °C, and the average heat dissipation rate reached 80.7 W at 100% opening ratio when the SESHR was filled with 1#PCM.
The outpainting results produced by existing approaches are often too random to meet users' requirements. In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance. To this end, we propose an encoder-decoder based network to conduct sketch-guided outpainting, where two alignment modules are adopted to impose the generated content to be realistic and consistent with the provided sketches. First, we apply a holistic alignment module to make the synthesized part be similar to the real one from the global view. Second, we reversely produce the sketches from the synthesized part and encourage them be consistent with the ground-truth ones using a sketch alignment module. In this way, the learned generator will be imposed to pay more attention to fine details and be sensitive to the guiding sketches. To our knowledge, this work is the first attempt to explore the challenging yet meaningful conditional scenery image outpainting. We conduct extensive experiments on two collected benchmarks to qualitatively and quantitatively validate the effectiveness of our approach compared with the other state-of-the-art generative models.
Underwater robot designs inspired by the behavior and morphological characteristics of aquatic animals can provide reinforced mobility and energy efficiency. In the past two decades, the emerging materials science and integrated circuit technology have been combined and applied to various types of bionic soft underwater miniaturized robots by researchers around the world. Further, the potential applications of biomimetic soft micro-swimmers in the biological and medical fields have been explored. Here, this paper reviews the development of biomimetic soft tiny swimmers, which are designed based on a variety of intelligent materials and control strategies. This review focuses on the various actuation mechanisms of soft tiny swimmers reported in the past two decades and classifies these robots into four categories: fish-like, snake-like, jellyfish-like and microbial-inspired ones. Besides, this review considers the practical challenges faced by actuation mechanisms of each type of robot, and summarizes and prospects how these challenges affect the potential applications of robots in real environments.
Biomimetics , Robotics , Animals , Equipment Design
