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With the integration and miniaturization of chips, there is an increasing demand for improved heat dissipation. However, the low thermal conductivity (TC) of polymers, which are commonly used in chip packaging, has seriously limited the development of chips. To address this limitation, researchers have recently shown considerable interest in incorporating high-TC fillers into polymers to fabricate thermally conductive composites. Hexagonal boron nitride (h-BN) has emerged as a promising filler candidate due to its high-TC and excellent electrical insulation. This review comprehensively outlines the design strategies for using h-BN as a high-TC filler and covers intrinsic TC and morphology effects, functionalization methods, and the construction of three-dimensional (3D) thermal conduction networks. Additionally, it introduces some experimental TC measurement techniques of composites and theoretical computational simulations for composite design. Finally, the review summarizes some effective strategies and possible challenges for the design of h-BN fillers. This review provides researchers in the field of thermally conductive polymeric composites with a comprehensive understanding of thermal conduction and constructive guidance on h-BN design.

Acute lung injury (ALI) is a severe clinical respiratory condition characterized by high rates of mortality and morbidity, for which effective treatments are currently lacking. In this study, lipopolysaccharide (LPS) was used to induce ALI mice, demonstrating the efficacy of tetramethylpyrazine (TMP) in ameliorating ALI. Subsequent we perfored high-throughput sequencing analysis and used Targetscan 8.0 and miRWalk 3.0 databases to predict the interaction between microRNAs and destrin (DSTN), ultimately identifying miR-369-3p as the focus of the investigation. The adenovirus carrying miR-369-3p was administered one week prior to LPS-induced in order to assess its potential efficacy in ameliorating ALI in mice. The findings indicated that the overexpression of miR-369-3p resulted in enhanced lung function, reduced pulmonary edema, inflammation, and permeability in LPS-induced ALI mice, while the suppression of miR-369-3p exacerbated the damage in these mice. Furthermore, the beneficial effects of TMP on LPS-induced ALI were negated by the downregulation of miR-369-3p. The results of our study demonstrate that TMP mitigates LPS-induced ALI through upregulation of miR-369-3p. Consequently, the findings of this study advocate for the clinical utilization of TMP in ALI treatment, with miR-369-3p emerging as a promising target for future ALI interventions.

Low-grade heat energy recycling is the key technology of waste-heat utilization, which needs to be improved. Here, we use a zinc-assisted solid-state pyrolysis route to prepare zinc-guided 3D graphene (ZnG), a 3D porous graphene with the interconnected structure. The obtained ZnG, with a high specific surface area of 1817 m2·g−1 and abundant micropores and mesopores, gives a specific capacitance of 139 F·g−1 in a neutral electrolyte when used as electrode material for supercapacitors. At a high current density of 8 A·g−1, the capacitance retention is 93% after 10,000 cycles. When ZnG is used for thermally chargeable supercapacitors, the thermoelectric conversion of the low-grade heat energy is successfully realized. This work thus provides a demonstration for low-grade heat energy conversion.

In this study, a practical adaptive control scheme is proposed for the trajectory tracking of an unmanned surface vehicle via the characteristic modelling approach. Therefore, accurate tracking control can be achieved in the presence of unknown time‐varying model parameters and environmental disturbances. The control scheme comprises a trajectory guidance module based on the virtual target approach and a tracking control module designed by characteristic modelling theory. Firstly, the ideal control commands of the yaw speed and surge speed are generated using the position errors between the vehicle and the virtual target. Then, a second‐order characteristic model for the heading and surge speed channel is developed. The parameters of the model are updated by a real‐time parameter identification algorithm. Based on this model, an integrated adaptive control law is designed which consists of golden‐section control, feed‐forward control and integral control. Finally, the development processes of the vehicle platform and the control algorithms are described, and the results of simulation and field experiments are presented and discussed.

The measurement accuracy of a single-point L-type total pressure probe in the inlet stage outlet flow path of a vortex engine compressor and the influence of the probe on the flow field were investigated via numerical simulation. The change pattern of probe measurement results with flow rate under three typical operating conditions of cruise, maximum continuous and takeoff were analyzed, as well as the influence of the installation position on the probe measurement results. The present results show that the measurement error of the probe gradually increases with the increasing of inlet flow rate. The relative measurement error of total probe pressure is the smallest and the measurement error is linear with the flow rate at a ratio( D / d ) of 6.5. The relative measurement error of the total probe pressure is greatest when the installation position is located near the leaf basin compared to the leaf back. As the probe is installed closer to the back of the leaf, the total pressure loss coefficient downstream of the probe decreases more slowly and the probe has a greater range of influence on the flow field in the flow direction. When the installation position is located near the leaf basin and leaf back, the velocity uniformity between 5 mm and 24 mm downstream of the probe is smaller. 采用数值模拟的方法, 研究了某涡轴发动机压气机进口级出口流道中单点L型总压探针的测量精度以及探针对流场的影响, 分析在巡航、最大连续、起飞3个典型工作状态下, 探针测量结果随流量的变化规律, 以及安装位置对探针测量结果的影响。研究结果表明: 随着进口流量增加, 探针的测量误差逐渐增加, 当 D / d 为6.5时, 探针总压相对测量误差最小, 且测量误差与流量呈线性关系; 相较于叶背, 当安装位置在叶盆附近时, 探针总压相对测量误差最大; 当探针安装位置越靠近叶背, 探针下游的总压损失系数下降速度越慢, 探针在流向的影响范围更大; 当安装位置在叶盆和叶背附近时, 探针下游5~24 mm之间的速度均匀度较小。

Nonlinear inversion algorithms are away ill-pose for the limited and special data measurement, such as mise-a-la-masse-based electric method and borehole to surface electromagnetic method. It is natural to develop new inversion algorithms without calculating the the Jacobian matrix and its inverse matrix, especially for three dimensional inversion. Basing on U-Net fully convolutional neural network, a three dimensional inversion of mise-a-la-masse-based electric data is proposed in this paper. The random rotated spheroids are used to synthesize diverse geological models. A typical U-Net learning network is constructed with incorporating the Dice coefficient and the input data feedback to the trained network. Synthetic datasets testing validates the effectiveness of this proposed algorithm for mise-a-la-masse-based electric data.

Engineered nanoparticle–support interaction is an effective strategy for tuning the structures and performance of engineered nanoparticles. Here, we show that tuning the dehydroxylation of kaolinite nanoclay as the support could induce zinc oxide–kaolinite interactions. We used free energy theory, electron microscopy, and X-ray photoemission spectroscopy to identify interaction strengths between metal oxides and the underlying nanoclay induced by dehydroxylation. Desirable exposure of nanoparticle sites and the geometrical and crystal structure were obtained by tuning the interface interactions between ZnO nanoparticles and nanoclay. The surface free energy of zinc oxide–nanoclay results in different interfacial interactions, and the properties of the surface free energy electron-donating (γ−) and electron-accepting (γ+) parameters have significant effects on the electron acceptor. This could, in turn, promote stronger interactions between zinc oxide and the kaolinite surface, which produce more active (0001) Zn-polar surfaces with promoting zinc oxide nanoparticles growing along the direction. Reactive oxygen species, leached zinc ions, and electron transfer can modulate the antibacterial activities of the samples as a function of interface free energy. This further demonstrates the interfacial interactions induced by dehydroxylation. This work has new application potential in biomedicine and materials science.

Summary Excessive nitrogen use and low nitrogen use efficiency (NUE) in current agroecosystems are disrupting the global nitrogen cycle. Chemical inhibitors offer only temporary relief, while plant‐derived biological nitrification inhibitors (BNIs) remain safer but underexplored. Identifying biological nitrification inhibition (BNI) traits in nitrogen‐demanding crops like wheat is key to improving sustainability. In this study, a combined GC‐ and LC–MS platform was used to determine the metabolome of the root exudates of 44 diverse wheat genotypes originating from India and Austria. With more than 6000 metabolic features, a pronounced genotype‐specific variation, a clear geographic pattern and an unexpected complexity of the root exudate metabolome were observed. A novel high‐throughput assay utilizing diverse ammonia‐oxidizing bacteria (AOB) and archaea (AOA) was developed for rapid BNI testing, highlighting distinct inhibition and even growth stimulation capacities between genotypes. Network analysis and advanced machine and deep learning analysis identified combinations of 32 metabolites linked to high BNI activity, including phenylpropanoids sinapinic acid, syringic acid and others, as well as glycosylated flavones isoschaftoside and others. This indicates that the concurrent presence of specific metabolites, rather than a single compound, drives nitrification inhibition in the rhizosphere. Variation in BNI activity among wheat genotypes, classified as either spring or winter types, suggests that root architecture modulates the dynamics of root exudation and the potential for nitrification inhibition. The unique combination of high‐throughput metabolomics analysis and the BNI fast‐track assay allows for screening of large germplasm collections as an essential requirement to introduce BNI and related NUE traits into modern breeding programmes.

Black talc is a natural silicate clay mineral with a typical 2:1 layered structure, low electrical conductivity, large specific surface area, and high thermal stability. The world’s largest black talc mine, with known reserves of one billion tons, is located in China’s Jiangxi province. Due to the restriction of its color, the application of black talc is only limited to ceramic raw materials, coating filler, waterproof materials, and other low-end application industries. Thermal treatment is a common method of clay mineral modification. It is vital to examine the structural and physical changes of black talc during calcination in order to prepare black-talc-based composites and to broaden their applications. This work discusses the evolution of black talc upon thermal treatment (30–1000 °C) and the corresponding structural changes. The thermal stability of minerals was analyzed via thermogravimetric (TG) analysis and thermogravimetry–mass spectrometry (TG-MS). The decomposition of minerals during calcination consists of four processes: dehydration, organic carbon decomposition, dihydroxylation, and phase transformation. In situ FTIR and in situ XRD were employed to track changes in black talc in real time during thermal treatment. At 800 °C, black talc was found to begin to go through dihydroxylation, and the crystallinity index decreased significantly. The XRD pattern of samples at 950 °C (T950) showed the reflection of the enstatite structure, and the relative crystallinity index was 27.3%, indicating that the mineral had undergone phase transformation. In addition, the Brunauer–Emmet–Teller (BET), laser particle size analyzer, Zeta potential, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) techniques were used to systematically characterize the physicochemical properties of minerals at different temperatures. The results show that black talc’s particle size and specific surface area increase with the calcination temperature. The surface charge changes, and more amorphous SiO2 and MgO appear, indicating that thermal treatment could induce structural changes and activate the surface of black talc.

Image processing networks have gained great success in many fields, and thus the issue of copyright protection for image processing networks has become a focus of attention. Model watermarking techniques are widely used in model copyright protection, but there are two challenges: (1) designing universal trigger sample watermarking for different network models is still a challenge; (2) existing methods of copyright protection based on trigger s watermarking are difficult to resist forgery attacks. In this work, we propose a dual model watermarking framework for copyright protection in image processing networks. The trigger sample watermark is embedded in the training process of the model, which can effectively verify the model copyright. And we design a common method for generating trigger sample watermarks based on generative adversarial networks, adaptively generating trigger sample watermarks according to different models. The spatial watermark is embedded into the model output. When an attacker steals model copyright using a forged trigger sample watermark, which can be correctly extracted to distinguish between the piratical and the protected model. The experiments show that the proposed framework has good performance in different image segmentation networks of UNET, UNET++, and FCN (fully convolutional network), and effectively resists forgery attacks.