Molecular dynamics simulation of structural changes in single crystalline silicon nitride nanomembrane

Shargh, Ali K.; Abdolrahim, Niaz

doi:10.1016/j.ceramint.2019.07.355

Cited by 8 publications

(2 citation statements)

References 59 publications

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“…In this paper, our objective is to design NPN membranes with improved or prescribed strength values. In comparison with our earlier designs [13][14][15] , our new designs will better resemble the microstructure of experimental NPN membranes. We should note that although one clear path to increase the strength of NPN membrane is to simply decrease their porosity, our focus is to increase their strength at a fixed porosity.…”

Section: Introductionmentioning

confidence: 94%

“…In practice, it is excessively easier to conduct such forward design approach through physics-based simulations including molecular dynamics (MD) or finite element (FE) simulations in comparison with the experiments [7][8][9][10][11][12] . In our earlier works [13][14][15] , we used MD simulations to investigate the mechanical behavior of NPN membranes with different microstructures. We revealed that the deformability of the NPN membranes can be significantly improved using hexagonal pore patterns with samesize circular pores.…”

Section: Introductionmentioning

confidence: 99%

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An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties

Shargh

Abdolrahim

2023

npj Comput Mater

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The high permeability and strong selectivity of nanoporous silicon nitride (NPN) membranes make them attractive in a broad range of applications. Despite their growing use, the strength of NPN membranes needs to be improved for further extending their biomedical applications. In this work, we implement a deep learning framework to design NPN membranes with improved or prescribed strength values. We examine the predictions of our framework using physics-based simulations. Our results confirm that the proposed framework is not only able to predict the strength of NPN membranes with a wide range of microstructures, but also can design NPN membranes with prescribed or improved strength. Our simulations further demonstrate that the microstructural heterogeneity that our framework suggests for the optimized design, lowers the stress concentration around the pores and leads to the strength improvement of NPN membranes as compared to conventional membranes with homogenous microstructures.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties

Shargh

Abdolrahim

2023

npj Comput Mater

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Carbon fiber cannot always reduce the wear of PEEK for orthopedic implants under DPPC lubrication

et al. 2022

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Excellent wear resistance is an important feature of orthopedic implants. However, although pure polyetheretherketone (PEEK) is outperformed by carbon fiber-reinforced PEEK (CF-PEEK) for stability and durability under laboratory conditions, it is not clear whether CF-PEEK should be preferred in all real-world applications. Results indicate that, under dipalmitoylphosphatidylcholine (DPPC) lubrication, the wear rates of PEEK are 35%—80% lower than the wear rates of CF-PEEK for different implant materials, speeds, loadings, and DPPC concentrations. Molecular dynamics calculations confirm that DPPC self-assembles on the PEEK surface to form an easily adsorbed continuous phospholipid lubricating film. In contrast, the carbon fibers on the CF-PEEK surface hinder the formation of the protective DPPC film and the CF-PEEK surface is thus subject to faster wear.

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Mechanical properties and deformation mechanisms of amorphous nanoporous silicon nitride membranes via combined atomistic simulations and experiments

et al. 2022

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Molecular dynamics simulation of structural changes in single crystalline silicon nitride nanomembrane

Cited by 8 publications

References 59 publications

An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties

An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties

Carbon fiber cannot always reduce the wear of PEEK for orthopedic implants under DPPC lubrication

Mechanical properties and deformation mechanisms of amorphous nanoporous silicon nitride membranes via combined atomistic simulations and experiments

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