Sundaram, RM, Sekiguchi, A., Sekiya, M., Yamada, T. & Hata, K. Copper/carbon nanotube composites: research trends and outlook. R. Soc. Open Sci. 5180814 (2018).
Krizhevsky, BA, Sutskever, I. & Hinton, GE ImageNet classification using deep convolutional neural networks. commune ACM 6084-90 (2012).
Mikolov, T., Deoras, A., Povey, D., Burget, L. & Černocký, J. Strategies for training language models for large-scale neural networks. in 2011 IEEE work. Auto Speech Recognition. Understanding, ASRU 2011, Proc. 196-201 (2011).
Kang, M. & Kwon, B. Deep learning of forced convection heat transfer. J. Heat transfer 1441-7 (2022).
Raissi, M., Yazdani, A. & Karniadakis, GE Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science 3671026-1030 (2020).
Edalatifar, M., Tavakoli, MB, Ghalambaz, M. & Setoudeh, F. Learning the physics of heat conduction using deep learning. J. Therm. Anal. calories. 1461435-1452 (2021).
Yang, L., Dai, W., Rao, Y. & Chyu, MK Optimization of hole distribution of an effusively cooled surface subjected to non-uniform inlet temperature using deep learning approaches. international J. Heat mass transfer. 145118749 (2019).
Kwon, B., Ejaz, F. & Hwang, LK Machine Learning for Heat Transfer Correlations. international commune heat mass transfer 116104694 (2020).
Wei, H., Zhao, S., Rong, Q. & Bao, H. Predicting the effective thermal conductivity of composites and porous media by machine learning methods. international J. Heat mass transfer. 127908-916 (2018).
Lee, KW, Son, HS, Cho, KS & Choi, HJ Effect of interfacial bridging atoms on the strength of Al/CNT composites: Machine learning-based prediction and experimental validation. J Mater. resolution technol. 171770-1776 (2022).
Matos, MAS, Pinho, ST & Tagarielli, VL Application of machine learning to predict the multiaxial strain detection response of CNT-polymer composites. Carbon NY 146265-275 (2019).
Le, TT Predicting the tensile strength of polymer-carbon nanotube composites using a practical machine learning method. J. Compos. mater 55787-811 (2021).
Ejaz, F. et al. A two-dimensional finite element model for Cu-CNT composites: the influence of interfacial resistances on electrical and thermal transports. material 24101505 (2022).
E Khaleghi, M Torikachvili, MA Meyers, and EA Olevsky. mater Latvian. 79256-258 (2012).
Choi, ES et al. Improving thermal and electrical properties of carbon-nanotube-polymer composites by magnetic field processing. J.Appl. physics 946034-6039 (2003).
Dai, J., Wang, Q., Li, W., Wei, Z. & Xu, G. Properties of well-aligned SWNT-modified poly(methyl methacrylate) nanocomposites. mater Latvian. 6127-29 (2007).
Wang, Q., Dai, J., Li, W., Wei, Z. & Jiang, J. The effects of CNT alignment on the electrical conductivity and mechanical properties of SWNT/epoxy nanocomposites. compos. Science. technol. 681644-1648 (2008).
Zho, B et al. Thermal conductivity of aligned CNT/polymer composites using mesoscopic simulation. compos. Part A Appl. Science. Manufacturer 90410-416 (2016).
Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region suggestion networks. IEEE Trans. pattern anal do intelligence 391137-1149 (2017).
Russakovsky, O. et al. ImageNet major visual recognition challenge. international J. Computer. Vis. 115211-252 (2015).
He K, Zhang X, Ren S & Sun J. Deep residual learning for image recognition. in Proc. IEEE calculation. society conf calculation. Vis. pattern recognition. 2016–dec770-778 (2016).
LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc.IEEE 862278-2323 (1998).
Günen, MA Performance comparison of deep learning and machine learning methods in the determination of wetland water bodies using the EuroSAT dataset. Vicinity. Science. Pollution. resolution 2921092-21106 (2022).
Subramaniam, C. et al. Hundredfold increase in current carrying capacity in a carbon-nanotube-copper composite. nat. commune 41-7 (2013).
Akbarpour, MR, Mousa Mirabad, H., Alipour, S. & Kim, HS Improved tensile properties and electrical conductivity of Cu-CNT nanocomposites processed by the combination of flake powder metallurgy and high-pressure torsion processes. mater Science. Closely. A 773138888 (2020).
Pan, Y et al. Fabrication, mechanical properties, and electrical conductivity of Al2O3-reinforced Cu/CNTs composites. J. Alloys Compd. 7821015-1023 (2019).
Daoush, WM, Lim, BK, Mo, CB, Nam, DH & Hong, SH Electrical and mechanical properties of carbon nanotube-reinforced copper nanocomposites prepared by electroless deposition. mater Science. Closely. A 513-514247-253 (2009).
Subramaniam, C. et al. Carbon nanotube copper with metal-like thermal conductivity and silicon-like thermal expansion for efficient cooling of electronics. nanoscale 62669-2674 (2014).
Bye, K. et al. Thermal properties of carbon nanotube-copper composites for thermal management applications. Nanosc. Resolution Latvian. 5868-874 (2010).
Kim, KT et al. Influence of embedded carbon nanotubes on the thermal properties of copper matrix nanocomposites processed by molecular-level mixing. Scr. mater 64181-184 (2011).
Never, JH et al. Fabrication and thermal conductivity of copper matrix composites reinforced by tungsten-coated carbon nanotubes. international J. Bergman. Metal. mater 19446-452 (2012).
