Additive manufacturing potential for medical devices and technology

Ali, Mohammad Azam; Rajabi, Mina; Sali, Sonali Sudhir

doi:10.1016/j.coche.2020.05.001

Cited by 26 publications

(9 citation statements)

References 39 publications

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“…Our goal was to develop force fields that exhibit the generalizability needed to model each component over the wide range of temperatures accessible to the liquid-phase epitaxial growth of gallium nitride during an additive manufacturing (AM) process . The AM approaches allow the fabrication of geometries unattainable through traditional manufacturing and lead to a faster and cheaper prototyping to create a product with targeted material properties. − Our collaborators, Gagnon et al., developed an AM method to synthesize the gallium nitride . This process allows epitaxial growth of crystalline GaN and provides a new route to printing semiconductor materials, including vertical device geometries that enable higher performance but are currently difficult to fabricate .…”

Section: Introductionmentioning

confidence: 99%

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

Chen,

Shao,

et al. 2023

J. Chem. Theory Comput.

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Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semiempirical force field on one hand and the impractically large computational burden of using ab initio molecular dynamics on the other hand. In this paper, we use an "on-the-fly" active learning technique to develop a nonparameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field capable of capturing the crystallization of gallium nitride (GaN) during a novel additive manufacturing process featuring the reaction of liquid Ga and gaseous nitrogen precursors to grow crystalline GaN thin films. We show that this machine learning model is capable of producing a single force field that can model solid, liquid, and gas phases involved in the process. We verified our computational predictions against a range of experimental measurements relevant to each phase and against ab initio calculations, showing that this nonparametric force field produces properties with excellent accuracy as well as exhibits computationally tractable efficiency. The force field is capable of allowing us to simulate the solid−liquid coexistence interface and the crystallization of GaN from the melt. The development of this transferable force field opens the opportunity to simulate the liquid-phase epitaxial growth more accurately than before to analyze reaction and diffusion processes and ultimately to establish a growth model of the additive manufacturing process to create the gallium nitride thin films.

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Section: Introductionmentioning

confidence: 99%

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

Chen,

Shao,

et al. 2023

J. Chem. Theory Comput.

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“…[9] AM approaches allow the fabrication of geometries unattainable through traditional manufacturing and lead to faster and cheaper prototyping to create a product with targeted material properties. [10][11][12] Our collaborators at the Applied Physics Laboratory, Gagnon et al, have recently developed an AM method to synthesize gallium nitride. [13] This process allows epitaxial growth of solid GaN and provides a new route to printing semiconductor materials as desired, including vertical device geometries that enable higher performance but are currently difficult to fabricate [14].…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: A transferable force field for gallium nitride crystal growth from the melt using on-the-fly active learning

Clancy

Chen

Shao

et al. 2022

Preprint

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Atomic-scale simulations of reactive processes have been stymied by two factors: the general lack of a suitable semi-empirical force field on the one hand, and the impractically large computational burden of using ab initio molecular dynamics on the other. In this paper, we use an “on-the-fly” active learning technique to develop a non-parameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field suitable to capture the crystallization of gallium nitride (GaN) using a novel additive manufacturing route and a combination of liquid Ga and ammonia gas precursors to grow GaN thin films. We show that this machine learning model is capable of producing a transferable force field that can model all three phases, solid, liquid and gas, involved in this additive manufacturing process. We verified our computational results against a range of experimental measurements and ab initio molecular dynamics simulation, showing that this non parametric force field shows excellent accuracy as well as a computationally tractable efficiency. The development of this transferable force field opens the opportunity to simulate liquid phase epitaxial growth more accurately than before, analyze reaction and diffusion processes, and ultimately establish a growth model of the additive manufacturing process to create gallium nitride thin films.

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“…Thanks to these appealing properties, both SCPs and SMPs are considered promising SRPs for the biomedical sector like drug delivery, cancer treatment, tissue engineering, cardiovascular, and other surgical applications. [134][135][136][137][138][139][140] Hydrogels are macromolecular structures, in which polymer molecules are bonded in a hydrophilic network. [141][142][143][144] These materials can absorb a large amount of water, due to void imperfections in their structures (such as hydroxyl and carboxyl groups).…”

Section: Stimuli-responsive Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recent Advances in the Additive Manufacturing of Stimuli‐Responsive Soft Polymers

Tariq,

Arif,

Khalid

et al. 2023

Adv Eng Mater

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Stimuli‐responsive polymers (SRPs) are special types of soft materials, which have been extensively used for developing flexible actuators, soft robots, wearable devices, sensors, self‐expanding structures, and biomedical devices, thanks to their ability to change their shapes and functional properties in response to external stimuli including light, humidity, heat, pH, electric field, solvent, and magnetic field or combinations of two or more of these stimuli. In recent years, additive manufacturing (AM) aka three‐dimensional (3D) printing technology of these SRPs, also known as four‐dimensional (4D) printing, has gained phenomenal attention in different engineering fields, thanks to its unique ability to develop complex, personalized, and innovative structures, which undergo twisting, elongating, swelling, rolling, shrinking, bending, spiraling, and other complex morphological transformations. Herein, an effort has been made to provide insightful information about the AM techniques, type of SRPs, and their applications including, but not limited to tissue engineering, soft robots, bionics, actuators, sensors, construction, and smart textiles. This article also incorporates the current challenges and prospects, hoping to provide an insightful basis for the utilization of this technology in different engineering fields. It is expected that the amalgamation of 3D printing with SRPs would provide unparalleled advantages in different engineering arenas.This article is protected by copyright. All rights reserved.

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Additive manufacturing potential for medical devices and technology

Cited by 26 publications

References 39 publications

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

WITHDRAWN: A transferable force field for gallium nitride crystal growth from the melt using on-the-fly active learning

Recent Advances in the Additive Manufacturing of Stimuli‐Responsive Soft Polymers

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