Recently, thermal interface materials (TIMs) are in great demands for modern electronics. For mechanically mixed polymer composite TIMs, the thermal conductivity and the mechanical properties are generally lower than expected values due to the sharply increased viscosity and poor filler dispersion. This work shows that addition of a small amount of polyester-based hyperbranched polymer (HBP) avoided the trade-off in mechanically mixed ABS/hexagonal boron nitride (h-BN) composites. After adding 0.5 wt% HBP, the maximum h-BN content in the composites increased from 50 to 60 wt%. The out-of-plane, in-plane thermal conductivity, and tensile strength of ABS/h-BN with 50 wt% h-BN were 0.408, 0.517 W/mK, and 18 MPa, respectively, and were increased to 0.729, 0.847 W/mK, and 32 MPa by adding 0.5 wt% HBP, while 0.972, 1.12 W/mK, and 29.5 MPa were achieved for ABS/h-BN/HBP with 60 wt% h-BN. The morphological and rheological results proved that these enhancements are due to the improved h-BN dispersion by decreasing viscosity of composites during mixing. Theoretical modeling based on the modified effective medium theory confirmed such results and showed that the interfacial thermal resistance also decreased slightly. Thus, this work demonstrates a facile and scalable method for simultaneously improving the thermal conductivity and mechanical properties of thermoplastic-based TIMs. 相似文献
Nano Research - Insufficient intratumoral penetration greatly hurdles the anticancer performance of nanomedicine. To realize highly efficient tumor penetration in a precisely and spatiotemporally... 相似文献
Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANNFCM-GRN. In MANNFCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANNFCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANNFCM-GRN and show that MANNFCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANNFCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.
World Wide Web - The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each... 相似文献
