Исследование влияния технологических факторов на неопределенность результатов измерения теплопроводности методом лазерной вспышки

The estimation of the deviation in the measurements of thermal conductivity by the laser flash method for materials with different thermal conductivity coefficients, arising due to the presence of a graphite coating on the sample and the small thickness of the sample, is carried out. A computer model of the method was created in the Comsol Multiphysics software environment. For bulk samples with a graphite coating thickness of 20 μm, the deviation is 5.5 %. The thickness of bulk samples does not affect the measurement results. For materials with low thermal conductivity, a sharp increase in the deviation is observed, reaching 60%. For thermally conductive materials, the deviation is 16-18%. For thin samples less than 10 μm thick, the thickness of the graphite coating does not affect the measurement results. The decisive factor is the duration of the laser pulse.

A Novel Method to Prepare Transparent, Flexible and Thermally Conductive Polyethylene/Boron Nitride Films

The high thermal conductivity and good insulating properties of boron nitride (BN) make it a promising filler for high-performance polymer-based thermal management materials. An easy way to prepare BN-polymer composites is to directly mix BN particles with polymer matrix. However, a high concentration of fillers usually leads to a huge reduction of mechanical strength and optical transmission. Here, we propose a novel method to prepare polyethylene/boron nitride nanoplates (PE/BNNPs) composites through the combination of electrostatic self-assembly and hot pressing. Through this method, the thermal conductivity of the PE/BNNPs composites reach 0.47 W/mK, which gets a 14.6% improvement compared to pure polyethylene film. Thanks to the tight bonding of polyethylene with BNNPs, the tensile strength of the composite film reaches 1.82 MPa, an increase of 173.58% compared to that of pure polyethylene film (0.66 MPa). The fracture stress was also highly enhanced, with an increase of 148.44% compared to pure polyethylene film. Moreover, the addition of BNNPs in PE does not highly reduce its good transmittance, which is preferred for thermal management in devices like light-emitting diodes. This work gives an insight into the preparation strategy of transparent and flexible thermal management materials with high thermal conductivity.

Explore High Thermal Conductivity Amorphous Polymers using Reinforcement Learning

Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.4 W/m- K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all newly generated polymers with thermal conductivity > 0.400 W/m-K, we have evaluated their synthesizability by calculating the synthesis accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has a MD-calculated thermal conductivity of 0.693 W/m-K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other materials development tasks for different applications.

Explore High Thermal Conductivity Amorphous Polymers using Reinforcement Learning

Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.4 W/m- K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all newly generated polymers with thermal conductivity > 0.400 W/m-K, we have evaluated their synthesizability by calculating the synthesis accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has a MD-calculated thermal conductivity of 0.693 W/m-K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other materials development tasks for different applications.