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Ceramic aerogels are attractive for thermal insulation but plagued by poor mechanical stability and degradation under thermal shock. In this study, we designed and synthesized hyperbolic architectured ceramic aerogels with nanolayered double-pane walls with a negative Poisson’s ratio (−0.25) and a negative linear thermal expansion coefficient (−1.8 × 10−6 per °C). Our aerogels display robust mechanical and thermal stability and feature ultralow densities down to ∼0.1 milligram per cubic centimeter, superelasticity up to 95%, and near-zero strength loss after sharp thermal shocks (275°C per second) or intense thermal stress at 1400°C, as well as ultralow thermal conductivity in vacuum [∼2.4 milliwatts per meter-kelvin (mW/m·K)] and in air (∼20 mW/m·K). This robust material system is ideal for thermal superinsulation under extreme conditions, such as those encountered by spacecraft.

The poor performance of lithium-ion batteries in extreme temperatures is hindering their wider adoption in the energy sector. A fundamental challenge in battery thermal management systems (BTMSs) is that hot and cold environments pose opposite requirements: thermal transmission at high temperature for battery cooling, and thermal isolation at low temperature to retain the batteries’ internally generated heat, leading to an inevitable compromise of either hot or cold performances. Here, we demonstrate a thermal regulator that adjusts its thermal conductance as a function of the temperature, just as desired for the BTMS. Without any external logic control, this thermal regulator increases battery capacity by a factor of 3 at an ambient temperature ( T ambient ) of −20 °C in comparison to a baseline BTMS that is always thermally conducting, while also limiting the battery temperature rise to 5 °C in a very hot environment ( T ambient  = 45 °C) to ensure safety. The result expands the usability of lithium-ion batteries in extreme environments and opens up new applications of thermally functional devices. Thermal fluctuations inside batteries limit their performance and pose various safety hazards. Here, the authors develop a shape memory alloy-based thermal regulator that stabilizes battery temperature in both hot and cold extreme environments.

This study investigates the use of cyanate ester (CE) as the matrix material and hexagonal boron nitride (hBN) as the functional filler. The hBN was surface-modified, blended with CE, and cured to form the composite material. A thermal conductivity model for the hBN/CE composites was developed, and the effects of hBN content, particle size, and surface functionalization on the formation mechanism of the thermal conductivity network were systematically investigated in conjunction with experimental results. The results indicate that when the hBN particle size ranges from 1 to 3 μm and its content is 16.70%, the thermal conductivity of the hBN/CE composite reaches 0.630 W/m·K, representing a 136% increase compared to the intrinsic thermal conductivity of the CE matrix (0.267 W/m·K). After modifying the hBN with 3 wt% silane coupling agent Z6020, the thermal conductivity of the 3.0% Z6020-hBN/CE composite further increases to 0.992 W/m·K, 3.71 times higher than that of the CE matrix. The experimental values are consistent with the simulation results. Moreover, the 3.0% Z6020-hBN/CE composite also shows a dielectric constant of 3.05 and a loss tangent of 9.30‰, with an improved bending strength of 102.7 MPa.

Topology optimization (TO) is an effective approach to designing novel and efficient heat transfer devices. However, the TO of conjugate heat transfer has been essentially limited to laminar flow conditions only. The present study proposes a framework for TO involving turbulent conjugate heat transfer based on the variable density method. Different from the commonly used and oversimplified Darcy model, this approach is based on the more accurate and widely accepted k-ε model to optimize turbulent flow channels. We add penalty terms to the Navier–Stokes equation, turbulent kinetic energy equation, and turbulent energy dissipation equation, and use interpolation models for the thermal properties of materials. A multi-objective optimization function, aiming to minimize the pressure drop and the average temperature, is set up to balance the thermal and hydraulic performance. A case study is conducted to compare various optimization methods in the turbulent regime, and the results show that the present method has substantially higher optimization effectiveness while remaining computationally inexpensive.

Battery safety has attracted considerable attention worldwide due to the rapid development of wearable electronics and the steady increase in the production and use of electric vehicles. As battery failures are often associated with mechanical‐thermal coupled behaviors, protective shielding materials with excellent mechanical robustness and flame‐retardant properties are highly desired to mitigate thermal runaway. However, most of the thermal insulating materials are not strong enough to protect batteries from mechanical abuse, which is one of the most critical scenarios with catastrophic consequences. Here, inspired by wood, we have developed an effective approach to engineer a hierarchical nanocomposite via self‐assembly of calcium silicate hydrate and polyvinyl alcohol polymer chains (referred as CSH wood). The versatile protective material CSH wood demonstrates an unprecedented combination of light weight (0.018 g cm−3), high stiffness (204 MPa in the axial direction), negative Poisson's ratio (−0.15), remarkable toughness (6.67 × 105 J m−3), superior thermal insulation (0.0204 W m−1 K−1 in the radial direction), and excellent fire retardancy (UL94‐V0). When applied as a protective cover or a protective layer within battery packages, the tough CSH wood can resist high‐impact load and block heat diffusion to block or delay the spread of fire, therefore significantly reducing the risk of property damage or bodily injuries caused by battery explosions. This work provides new pathways for fabricating advanced thermal insulating materials with large scalability and demonstrates great potential for the protection of electronic devices. Inspired by wood, we have developed an effective approach to engineer a hierarchical nanocomposite via self‐assembly of calcium silicate hydrate and polyvinyl alcohol polymer chains. The versatile protective material demonstrates an unprecedented combination of light weight (0.018 g cm−3), high stiffness (204 MPa), negative Poisson's ratio (−0.15), remarkable energy dissipation (6.67 × 105 J m−3), superior thermal insulation (0.0204 W m−1 K−1), and excellent fire retardancy (UL94‐V0).

Vertically aligned carbon nanotubes arrays (VACNTs) are a promising candidate for the thermal interface material (TIM) of next-generation electronic devices due to their attractive thermal and mechanical properties. However, the environment required for synthesizing VACNTs is harsh and severely incompatible with standard device packaging processes. VACNTs’ extremely low in-plane thermal conductivity also limits its performance for cooling hot spots. Here, using a transfer-and-encapsulate strategy, a two-step soldering method is developed to cap both ends of the VACNTs with copper microfoils, forming a standalone Cu-VACNTs-Cu sandwich TIM and avoiding the need to directly grow VACNTs on chip die. This new TIM is fully compatible with standard packaging, with excellent flexibility and high thermal conductivities in both in-plane and through-plane directions. The mechanical compliance behavior and mechanism, which are critical for TIM applications, are investigated in depth using in situ nanoindentation. The thermal performance is further verified in an actual light emitting diode (LED) cooling experiment, demonstrating low thermal resistance, good reliability, and achieving a 17 °C temperature reduction compared with state-of-the-art commercial TIMs. This study provides a viable solution to VACNTs’ longstanding problem in device integration and free-end contact resistance, bringing it much closer to application and solving the critical thermal bottleneck in next-generation electronics.

Battery safety has attracted considerable attention worldwide due to the rapid development of wearable electronics and the steady increase in the production and use of electric vehicles. As battery failures are often associated with mechanical-thermal coupled behaviors, protective shielding materials with excellent mechanical robustness and flame-retardant properties are highly desired to mitigate thermal runaway. However, most of the thermal insulating materials are not strong enough to protect batteries from mechanical abuse, which is one of the most critical scenarios with catastrophic consequences. Here, inspired by wood, we have developed an effective approach to engineer a hierarchical nanocomposite via self-assembly of calcium silicate hydrate and polyvinyl alcohol polymer chains (referred as CSH wood). The versatile protective material CSH wood demonstrates an unprecedented combination of light weight (0.018 g cm−3), high stiffness (204 MPa in the axial direction), negative Poisson's ratio (−0.15), remarkable toughness (6.67 × 105 J m−3), superior thermal insulation (0.0204 W m−1 K−1 in the radial direction), and excellent fire retardancy (UL94-V0). When applied as a protective cover or a protective layer within battery packages, the tough CSH wood can resist high-impact load and block heat diffusion to block or delay the spread of fire, therefore significantly reducing the risk of property damage or bodily injuries caused by battery explosions. This work provides new pathways for fabricating advanced thermal insulating materials with large scalability and demonstrates great potential for the protection of electronic devices.

Carbon nanomaterials, mainly including carbon nanotubes and graphene, have high potential for heat transfer applications at high temperatures because of their superb heat transport properties and good thermal stability. However, due to the small physical sizes of carbon nanomaterials, real-world applications often require an ensemble of them. The present study aims to characterizing the thermal properties of carbon nanomaterial ensembles and understanding the underlying mechanism with an emphasis on high temperature applications. A one-dimensional (1D) reference bar method is selected to perform thermal transport experiments on target materials. Despite its popularity for room temperature measurements, this method does not readily extend to the high-temperature regime, mainly due to oxidation and convective and radiative heat loss concerns. In this dissertation, a modified 1D reference bar test rig is presented that eliminates these problems. Oxidation and convection are avoided by vacuum. Radiation heat loss is accounted for by a data fitting algorithm. Monte Carlo simulation is used to quantify the uncertainty of this method. The system is also validated by testing a commercially available thermal interface material. One of the major drawbacks of the steady-state reference bar method is its slow test speed. Reaching thermal equilibrium takes a significant amount of time, from an hour up to days. Typical transient methods, which can perform tests much faster, require special instruments such as modulated heaters. In this dissertation, a new transient method is presented that can be used directly with existing 1D reference bar test rigs. Using the temperature response of the reference bars in time domain, thermal properties of the sample can be extracted. Uncertainty quantification shows that measurement accuracy is not lost compared to steady-state methods, but the fast test speed is shown to reduce the time needed to perform a test by as much as 40 times. Vertically oriented carbon nanotube (CNT) arrays hold high promise for thermal interface applications. However, such an ensemble of CNTs behaves much differently than a collection of isolated CNTs and suffers from various interface effects. After years of research, the thermal transport characteristics of CNT arrays are still not fully understood. Also, experimental data at elevated temperatures are lacking. Using the newly developed high temperature 1D reference bar test rig, thermal interface properties of CNT arrays are examined, and the results are presented in this dissertation. Thermal interface resistance of CNT arrays is found to consistently decrease at high temperatures for both thermomechanically matched and mismatched interfaces. The results also suggest that contact resistances between CNT tips and the opposing substrates are major contributions to the total interface resistances. A method of integrating CNT arrays to braze joints is also developed to improve CNT-based thermal interface materials. Braze alloys are found to infiltrate into CNT arrays and form strong chemical bonds. Thermal characterization results suggest very good thermal interface performance, which is further shown to be unaffected by thermomechanical stresses. Graphene aerogels are studied as another type of carbon nanomaterial ensemble. Their thermal conductivities are measured at varying volume fraction, temperature and compressive strain. Not surprisingly, increasing volume fraction and temperature are shown to increase the thermal conductivity. However, results imply that interfaces are critical to the material in terms of thermal transport. Thermal tests in compression and accompanying microscopy more vividly show the role of interfaces. The study demonstrates that with a combination of low density, defects and interface engineering, the thermal properties of graphene derivatives can be tuned across many orders of magnitude.

Ship detection has been playing a significant role in the field of remote sensing for a long time, but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection, and the redundancy of the detection region. In order to solve these problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ships in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving problems resulting from the narrow width of the ship. Compared with previous multiscale detectors such as Feature Pyramid Network (FPN), DFPN builds high-level semantic feature-maps for all scales by means of dense connections, through which feature propagation is enhanced and feature reuse is encouraged. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multiscale region of interest (ROI) Align for the purpose of maintaining the completeness of the semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has state-of-the-art performance.

Recently, methods based on Faster region-based convolutional neural network (R-CNN) have been popular in multi-class object detection in remote sensing images due to their outstanding detection performance. The methods generally propose candidate region of interests (ROIs) through a region propose network (RPN), and the regions with high enough intersection-over-union (IoU) values against ground truth are treated as positive samples for training. In this paper, we find that the detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially, detection performance of small objects is poor when choosing a normal higher threshold, while a lower threshold will result in poor location accuracy caused by a large quantity of false positives. To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework for multi-class object detection. Specially, by analyzing the different roles that IoU can play in different parts of the network, we propose an IoU-guided detection framework to reduce the loss of small object information during training. Besides, the IoU-based weighted loss is designed, which can learn the IoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspect ratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves the precision of the results. Extensive experiments validate the effectiveness of our approach and we achieve state-of-the-art detection performance on the DOTA dataset.