2021
DOI: 10.1103/physrevmaterials.5.096001
|View full text |Cite
|
Sign up to set email alerts
|

Hot-press sintering of aluminum nitride nanoceramics

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
2
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5

Relationship

1
4

Authors

Journals

citations
Cited by 5 publications
(5 citation statements)
references
References 94 publications
0
2
0
Order By: Relevance
“…For instance, Liang et al. [ 107 ] employed MD simulations using the interatomic potential from Vashishta et al. [ 108 ] to investigate the hot‐press sintering process of AlN nanoceramics.…”
Section: Integrating Machine Learning Assisted Simulations Toolboxmentioning
confidence: 99%
See 1 more Smart Citation
“…For instance, Liang et al. [ 107 ] employed MD simulations using the interatomic potential from Vashishta et al. [ 108 ] to investigate the hot‐press sintering process of AlN nanoceramics.…”
Section: Integrating Machine Learning Assisted Simulations Toolboxmentioning
confidence: 99%
“…MD serves as a more suitable tool for studying polycrystalline nanoceramics. For instance, Liang et al [107] employed MD simulations using the interatomic potential from Vashishta et al [108] to investigate the hot-press sintering process of AlN nanoceramics. They specifically considered samples with varying nanoparticle sizes and hydrostatic pressures to elucidate their effects on the sintering process.…”
Section: Atomistic Simulations For Nanoceramicsmentioning
confidence: 99%
“…37−44 While MD simulations provide remarkable accuracy at the nanoscale, their computational demands are often excessive. 45,46 On the other hand, the dissipative particle dynamics (DPD) method, originally proposed by Hoogerbrugge and Koelman,47 offers an alternative approach to address these challenges. Comparing with MD simulations and the Lattice Boltzmann method, the DPD method is able to bridge the gap between capturing nanoconfinement effects and maintaining computational feasibility for the larger systems and extended time scales.…”
Section: ■ Introductionmentioning
confidence: 99%
“…Many studies attempted to address this problem by using molecular dynamics (MD) simulations to investigate the behavior of nanoconfined water and hydrocarbons. While MD simulations provide remarkable accuracy at the nanoscale, their computational demands are often excessive. , On the other hand, the dissipative particle dynamics (DPD) method, originally proposed by Hoogerbrugge and Koelman, offers an alternative approach to address these challenges. Comparing with MD simulations and the Lattice Boltzmann method, the DPD method is able to bridge the gap between capturing nanoconfinement effects and maintaining computational feasibility for the larger systems and extended time scales. , In particular, DPD’s soft interaction potentials and coarse-grained nature facilitate simulations over larger spatiotemporal scales compared to MD, thus providing a balance between efficiency and detail that is particularly advantageous for exploring the interaction dynamics. , Moreover, DPD accurately describes colloid transport and retention processes across a wide range of conditions since fluid flow and colloid transport are fully coupled in the DPD method .…”
Section: Introductionmentioning
confidence: 99%
“…Phase change materials are gradually widely used in the field of heat dissipation due to their high thermal conductivity [3]. The conventional heat dissipation temperature range of the electronic device is 40-75 ℃ [4][5][6] low melting point liquid metal satisfies the phase change requirement of this temperature zone well and has high thermal conductivity [7,8]. It is an order of magnitude higher than traditional thermal interface materials [9][10][11] with very good application prospects [12].…”
Section: Introductionmentioning
confidence: 99%