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Efficient thermal management has become one of the most critical issues of electronics because of the high heat flux generated from highly integrated, miniaturized, and increased power. Here we report highly flexible composites with aligned and overlapping interconnected boron nitride nanosheets (BNNSs) assembled in wrinkle structures. Besides high in-plane thermal conductivity of more than 26.58 W m-1 K-1, such structure rendered enhanced through-plane conduction along with increasing pre-stain. As thermal interface materials (TIMs) of both rigid and flexible devices, the composites revealed an outstanding thermal cooling capability outperforming some commercial TIMs. During a record-long bending process of more than 3000 cycles, the maximum temperature fluctuation of the flexible device with 100%-prestrained composite was only within 0.9 degrees C, less than one-third of that with commercial thermal pad. Moreover, the composite revealed a superior impermeability for flexible seals. Our results illustrate that the composites could be an ideal candidate for the thermal management of emerging flexible electronics.

Materials with tunable thermal properties at room temperature have attracted great attention owing to their applications as solid-state thermal switches for thermal management. As a typical ferroelectric material, barium titanate (BaTiO3) is regarded as promising candidate for thermal switch because its ferroelectric polarization can be switched by external electric field. However, BaTiO3 presents a low-symmetry tetragonal phase at room temperature, calculating its thermal conductivity through first-principles calculations is rather time-consuming. By solving Boltzmann transport equation with the interatomic force constants from second-principles model, we develop a time-efficient strategy to calculate the thermal conductivity of tetragonal BaTiO3. The calculated thermal properties of BaTiO3 based on the strategy are consistent with those from first-principles calculations while the calculation time is greatly reduced. It is found that both in-plane and out-of-plane thermal conductivity can be adjusted by external electric field. A thermal switch with a switch ratio of 5.1 at room temperature is predicted based on the polarization switching under external electric field. This work not only provides an efficient approach to study thermal properties but also suggests a room-temperature thermal switch controlled by an electric field.

Flexible grippers require appropriate stiffness to ensure stability and efficiency in unpredictable environments. While combining materials with variable stiffness and elastomers holds significant promise in this regard, concurrently achieving actuation and stiffness modulation typically results in heightened structural complexity and responsiveness issues. In this study, a giant electrorheological dielectric elastomer (GERDE) with high dielectric and high electrorheological (ER) properties is obtained by introducing giant electrorheological particles into silicone elastomers, further substantiating the ability to functionally couple actuation and stiffness-tuning, as well as application of a flexible gripper in mimicking emotive calligraphy. Specifically, GERDE exhibits better actuating (area strain rate of 33%) and ER performance (storage modulus increment of 1.5 MP) than existing conventional dielectric elastomer (DE) actuators (such as PDMS and VHB4910) under a low electric field. Compared to reported flexible grippers, bionic flexible grippers based on GERDE actuator have enhanced actuation, stiffness adjustment capability (i.e., stiffness is increased by 2.4 times), fast response (approximate to 180 ms), and coupling effect, and can write Chinese calligraphy of different emotions by the same trajectory. This work provides ideas and methods for achieving the integration of actuator-variable stiffness and emotional transmission in robots. By incorporating giant electrorheological particles into silicone elastomers, a novel type of giant electrorheological dielectric elastomer with high dielectric permittivity and superior electrorheological performance is successfully developed, further substantiating the coupled capabilities of actuation and stiffness tuning, as well as the application of flexible gripper in mimicking emotional calligraphy. image

A mathematical formula of high physical interpretation, and accurate prediction and large generalization power is highly desirable for science, technology and engineering. In this study, we performed a domain knowledge-guided machine learning to discover high interpretive formula describing the high-temperature oxidation behavior of FeCrAlCoNi-based high entropy alloys (HEAs). The domain knowledge suggests that the exposure time dependent and thermally activated oxidation behavior can be described by the synergy formula of power law multiplying Arrhenius equation. The pre-factor, time exponent (m), and activation energy (Q) are dependent on the chemical compositions of eight elements in the FeCrAlCoNi-based HEAs. The Tree-Classifier for Linear Regression (TCLR) algorithm utilizes the two experimental features of exposure time (t) and temperature (T) to extract the spectrums of activation energy (Q) and time exponent (m) from the complex and high dimensional feature space, which automatically gives the spectrum of pre-factor. The three spectrums are assembled by using the element features, which leads to a general and interpretive formula with high prediction accuracy of the determination coefficient R2=0.971. The role of each chemical element in the high-temperature oxidation behavior is analytically illustrated in the three spectrums, thereby the discovered interpretative formula provides a guidance to the inverse design of HEAs against high-temperature oxidation. The present work demonstrates the significance of domain knowledge in the development of materials informatics. © 2023

The hot deformation behaviors of FGH98 nickel-based powder superalloy were experimentally investigated and theoretically analyzed by Arrhenius models and machine learning (ML). Hot compression tests were conducted with a Gleeble-3800 thermo-mechanical simulation machine on the FGH98 superalloy at strain rates of 0.001–1 s–1 and temperatures of 1025–1175 °C. The peak stresses under different deformation conditions were analyzed via the Sellars model and an ML-inspired Gaussian process regression (GPR) model. The prediction of the GPR model outperformed that from the Sellars model. In addition, the stress-strain responses were predicted by the GPR model and tested by experimentally measured stress-strain curves. The results indicate that the developed GPR model has great power with wide generalization capability in the prediction of hot deformation behaviors of FGH98 superalloy, as evidenced by the R2 value higher than 0.99 on the test dataset. © 2022

A data augmentation technique is employed in the current work on a training dataset of 610 bulk metallic glasses (BMGs), which are randomly selected from 762 collected data. An ensemble machine learning (ML) model is developed on augmented training dataset and tested by the rest 152 data. The result shows that ML model has the ability to predict the maximal diameter Dmax of BMGs more accurate than all reported ML models. In addition, the novel ML model gives the glass forming ability (GFA) rules: average atomic radius ranging from 140 pm to 165 pm, the value of [Formula presented] being higher than 2.5, the entropy of mixing being higher than 10 J/K/mol, and the enthalpy of mixing ranging from -32 kJ/mol to -26 kJ/mol. ML model is interpretative, thereby deepening the understanding of GFA.

Ether-based electrolytes have long been proven to be advantageous over ether-based electrolytes in sodium ion batteries (SIBs), but the understanding of their effect on the phase transition of electrode materials during electrochemical cycling is very limited. In this work, we study the phase transition and electrochemical performance of 2H-MoS2 in ether- and ester-based electrolytes. Interestingly, we find that the MoS2 anode develops MoS2/diglyme-Na+ superlattices induced by cointercalation of Na+ and an ether solvent in an ether-based electrolyte, rather than the conversion reaction observed in ester-based electrolytes. The superlattices consist of mixed semiconductive 2H and metallic 1T phases, and are structurally stable and kinetically favorable for the repeated sodiation, which greatly enhances the capacity, cycling, and rate performance of MoS2. Specifically, the three-dimensional MoS2 nanoflowers deliver an ultrahigh capacity of 446.5 mAh g(-1) at 5 A g(-1) with no capacity fading after more than 3700 cycles in an ether-based electrolyte.