Thermal conductivity of multilayer hexagonal boron nitride nanoscrolls
Annotation
The article presents a theoretical analysis of the anisotropic thermal conductivity of multilayer hexagonal boron nitride (h-BN) nanoscrolls as promising fillers for thermal interfaces in electronic devices. Traditional thermally conductive composite materials, while possessing high thermal conductivity, are prone to agglomeration within the polymer matrix; their chemical inertness hinders the formation of strong bonds with the polymer, and their high electrical conductivity significantly limits their application in electronics. The h-BN-based material combines high thermal conductivity, excellent electrical insulation properties, and high processability for integration into electronic components. An analytical model is proposed to predict the thermal conductivity values of multilayer h-BN nanoscrolls in both the longitudinal and transverse directions. The analytical model for the anisotropic thermal conductivity of multilayer nanoscrolls (scrolled 2D nanoplates) is developed based on the generalized conductivity theory. Key scientific enhancements to existing models include the capability to increase the number of calculable layers and the dimensions of the nanoscrolls. To more accurately describe size effects, an interlayer scattering parameter is introduced for the first time in such a multilayer structure to correct the effective phonon mean free path within the material. Mathematical dependences of the thermal conductivity of multilayer h-BN nanoscrolls on the number of layers were obtained for the directions longitudinal and transverse to the nanoscroll axis. It is shown that as the number of layers increases, the longitudinal thermal conductivity (along the nanoscroll axis) decreases. The transverse thermal conductivity (perpendicular to the nanoscroll axis) is significantly higher than that of their carbon-based counterparts. Due to the absence of quantitative data (both experimental and numerical) for multilayer boron nitride nanoscrolls in available scientific literature, validation of the simulation results was performed on a similar system reported in open sources — a three-layer carbon nanoscroll. The obtained predictive results allow for assessing the influence of the layer count on the thermal conductivity of h-BN nanoscrolls and for synthesizing multilayer nanoscroll structures with a predetermined thermal conductivity value. It is demonstrated that multilayer h-BN nanoscrolls represent a promising alternative to carbon nanotubes in electronics for applications where it is critically important to eliminate “thermal bottlenecks” and ensure high inter-component electrical insulation.
Keywords
Постоянный URL
Articles in current issue
- Fluorescence studies of natural photosensitizers in oncology and antimicrobial therapy
- Review of deep learning methods for imaging photoplethysmography data processing
- Effect of heat treatment on the growth and luminescence of quantum dots CsPbI3 in fluorophosphate glass
- Study of nanopipettes conductivity depending on their shape and size
- Integrated control algorithm for obstacle and singularity avoidance in a robotic manipulator
- Method of automatic generation of the informative space for identifying information security events in corporate computer networks
- Spectral-based multi-band recurrent neural networks for black-box modeling of dynamic range compressors (in English)
- Hierarchical multi-task learning for low-complexity models based on task synergy analysis
- Detection of network anomalies in the Internet of Things environment using modified statistical criteria and ensemble methods
- Automatic detection of software design patterns using a language model on transformer architecture (in English)
- Ego-net link prediction with GNN (in English)
- Multi-task human’s psychological profile analysis based on text data using semi-supervised learning
- Modeling and optimization of information flows in electronic document management systems under information security threats
- Series-parallel architecture for the FPGA implementation of neural networks trainable in real-time using the error backpropagation algorithm
- An approach to contextual example mining for DGA domain identification using large language models
- Analysis of the effectiveness of optimizing behavioral descriptions of hardware in logic synthesizers for FPGA
- Spheroidal models of ore deposits in the framework of gravity tomography
- Prediction of maximum stresses in the shaft–insert system using a neural network
- Estimation criterion and method for optimizing the redundancy of video images in surveillance systems
- Generating spatiotemporal network load series in multi-access edge computing tasks using open data
- Application of hybrid artificial intelligence methods to practical industrial tasks under conditions of scarce training data
- Implementation and investigation of a reservoir computer based on a hardware model of three-element spiking neuron
- Analysis of a centerless control scheme for profiles of large-sized shells in the process of their shaping
- Oblivious signature based on the theory of elliptic curve isogeny