High thermal conductivity of high-quality monolayer boron nitride and its thermal expansion

Cai, Qiran; Scullion, Declan; Gan, Wei; Falin, Aleksey; Zhang, Shunying; Watanabe, Kenji; Taniguchi, Takashi; Chen, Ying; Santos, Elton J. G.; Li, Lu Hua

doi:10.1126/sciadv.aav0129

Cited by 390 publications

(325 citation statements)

References 51 publications

Supporting

Mentioning

281

Contrasting

Unclassified

Order By: Relevance

“…However, finding efficient passive fitting approaches require more elaborated studies. Such a methodology has a large versatility and could be employed to explore the optimization of thermal conductivities in two-dimensional materials based compounds such as MoS 2 [85] nanomembranes, boron-nitride multilayers [86] or carbon-based 2D nanostructures [87][88][89]. Worthy to remind that when using the DFT-BTE approach, by decreasing the symmetry in the atomic lattice and depending on the cutoff distance, the number of required single-point DFT calculations increase substantially [29].…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, the astonishing advances in machine-learning techniques have opened new possibilities to address critical challenges in various fields, also in materials science [9,[34][35][36][37][38]. For example, actively trained machine-learning interatomic potentials [39] have been successfully employed to predict novel materials [40,41] and examine lattice dynamics [42] and thermal conductivity [43] of bulk materials.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

et al. 2020

View full text Add to dashboard Cite

It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven for the case of graphene, while most of the available interatomic potentials estimate the structural and elastic constants with high accuracy, when employed to predict the lattice thermal conductivity they however lead to a variation of predictions by one order of magnitude. Here we present our results on using machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio molecular dynamics trajectories without any tuning or optimizing of hyperparameters. These first-attempt potentials could reproduce the phononic properties of different two-dimensional (2D) materials obtained using density functional theory (DFT) simulations. To illustrate the efficiency of the trained MLIPs, we consider polyaniline C 3 N nanosheets. C 3 N monolayer was selected because the classical MD and different first-principles results contradict each other, resulting in a scientific dilemma. It is shown that the predicted thermal conductivity of 418 ± 20 W mK −1 for C 3 N monolayer by the non-equilibrium MD simulations on the basis of a first-attempt MLIP evidences an improved accuracy when compared with the commonly employed MD models. Moreover, MLIP-based prediction can be considered as a solution to the debated reports in the literature. This study highlights that passively fitted MLIPs can be effectively employed as versatile and efficient tools to obtain accurate estimations of thermal conductivities of complex materials using classical MD simulations. In response to remarkable growth of 2D materials family, the devised modeling methodology could play a fundamental role to predict the thermal conductivity.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The thermal conductivity of hBN is very high [4][5][6], and it has stimulated modelling using semi-classical [4,[6][7][8] and quantum approaches [9,10]. It is now fully understood and hBN is widely used for thermal management issues [11] in miniaturized devices or for thermoelectric applications [12].…”

Section: The Early Daysmentioning

confidence: 99%

Boron nitride for excitonics, nano photonics, and quantum technologies

et al. 2020

View full text Add to dashboard Cite

AbstractWe review the recent progress regarding the physics and applications of boron nitride bulk crystals and its epitaxial layers in various fields. First, we highlight its importance from optoelectronics side, for simple devices operating in the deep ultraviolet, in view of sanitary applications. Emphasis will be directed towards the unusually strong efficiency of the exciton–phonon coupling in this indirect band gap semiconductor. Second, we shift towards nanophotonics, for the management of hyper-magnification and of medical imaging. Here, advantage is taken of the efficient coupling of the electromagnetic field with some of its phonons, those interacting with light at 12 and 6 µm in vacuum. Third, we present the different defects that are currently studied for their propensity to behave as single photon emitters, in the perspective to help them becoming challengers of the NV centres in diamond or of the double vacancy in silicon carbide in the field of modern and developing quantum technologies.

show abstract

“…Moreover, if the heat dissipation material is optically transparent, it can be incorporated even at the front side of displays, enabling more effective protection from thermal attack. In this light, BN can act as a practical material that can not only function as an effective heat spreading film owing to its high in‐plane thermal conductivity of 220– 420 W m −1 K −1 for bulk h‐BN [ 11 ] and up to 751 W m −1 K −1 for single‐layered crystalline BN [ 12 ] but also provide optical transparency resulting from its large bandgap energy (≈6 eV). [ 13 ]…”

Section: Figurementioning

confidence: 99%

Desolvation‐Triggered Versatile Transfer‐Printing of Pure BN Films with Thermal–Optical Dual Functionality

Han

Rah

et al. 2020

Advanced Materials

View full text Add to dashboard Cite

Although hexagonal boron nitride (BN) nanostructures have recently received significant attention due to their unique physical and chemical properties, their applications have been limited by a lack of processability and poor film quality. In this study, a versatile method to transfer‐print high‐quality BN films composed of densely stacked BN nanosheets based on a desolvation‐induced adhesion switching (DIAS) mechanism is developed. It is shown that edge functionalization of BN sheets and rational selection of membrane surface energy combined with systematic control of solvation and desolvation status enable extensive tunability of interfacial interactions at BN–BN, BN–membrane, and BN–substrate boundaries. Therefore, without incorporating any additives in the BN film and applying any surface treatment on target substrates, DIAS achieves a near 100% transfer yield of pure BN films on diverse substrates, including substrates containing significant surface irregularities. The printed BNs demonstrate high optical transparency (>90%) and excellent thermal conductivity (>167 W m−1 K−1) for few‐micrometer‐thick films due to their dense and well‐ordered microstructures. In addition to outstanding heat dissipation capability, substantial optical enhancement effects are confirmed for light‐emitting, photoluminescent, and photovoltaic devices, demonstrating their remarkable promise for next‐generation optoelectronic device platforms.

show abstract

High thermal conductivity of high-quality monolayer boron nitride and its thermal expansion

Cited by 390 publications

References 51 publications

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Boron nitride for excitonics, nano photonics, and quantum technologies

Desolvation‐Triggered Versatile Transfer‐Printing of Pure BN Films with Thermal–Optical Dual Functionality

Contact Info

Product

Resources

About