Designing Amorphous Networks with Adjustable Poisson Ratio from a Simple Triangular Lattice

Jin, Zhipeng; Fang, Chenchao; Shen, Xiangying; Xu, Lei

doi:10.1103/physrevapplied.18.054052

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…Due to its amorphous structure, SiN x thin films deposited at low temperature may exhibit pinhole-defects on their surface, which could lead to water infiltration into the electrode and emission layer. In the report of Roberts' group, [39] the effects of defects need to be considered for thin films with water and oxygen barrier properties, as shown in the Figure 6a, where gas and water vapor are transported through interstices of the amorphous networks [40][41][42][43] structure, nano-defects and pinhole-defects. Due to the fact, that the samples in this study were deposited at low temperatures, and the surface morphology of the thin films characterized by SEM in Figure 4 did not show pinholes, this indicates that nano-defects or interstices of the amorphous networks structure dominate the diffusion path, leading to black spots in the luminescent region.…”

Section: Resultsmentioning

confidence: 99%

Hydrogen Role on the Properties of Silicon Nitride for Flexible Thin Film Encapsulation

Wu,

Yang,

Zhang

et al. 2024

Physica Status Solidi (a)

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High transmittance, high density, and good mechanical properties of silicon nitride (SiNx) make it a potential candidate for barrier layers of flexible organic light emission diodes (FOLEDs). However, the overall characteristics and water oxygen barrier properties of the films are easily affected by the dangling bonds in the films. After adding hydrogen flow rate to the deposition conditions of SiNx films, the hydrogen content interacts with the dangling bonds in amorphous SiNx, which could reduce the number of dangling bonds and improve the density of the hydrogenated amorphous silicon nitride (SiNxHy) films. The high‐density film layer could effectively prevent the invasion of water vapor due to its smaller pinhole density. Therefore, the water oxygen barrier performance of SiNxHy is significantly improved, the maximum decrease in water vapor transmittance rate (WVTR) value is approximately 22.4%. In addition, the increase of hydrogen content also changed the overall properties of the films, including transmittance, elastic modulus, hardness, residual stress, and fracture strain. Therefore, compared with SiNx films, SiNxHy films have more excellent mechanical and optical properties. The SiNxHy films featuring a simplified preparation process with high efficiency, low cost, and superior barrier performance is of great potential for the commercial applications.

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Section: Resultsmentioning

confidence: 99%

Hydrogen Role on the Properties of Silicon Nitride for Flexible Thin Film Encapsulation

Wu,

Yang,

Zhang

et al. 2024

Physica Status Solidi (a)

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Non-affinity: The emergence of networks from amorphous planar graphs

Shen

Zhu

2023

Sci. China Phys. Mech. Astron.

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A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks

Zhu,

Fang,

Jin

et al. 2024

Applied Physics Reviews

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“AI for science” is widely recognized as a future trend in the development of scientific research. Currently, although machine learning algorithms have played a crucial role in scientific research with numerous successful cases, relatively few instances exist where AI assists researchers in uncovering the underlying physical mechanisms behind a certain phenomenon and subsequently using that mechanism to improve machine learning algorithms' efficiency. This article uses the investigation into the relationship between extreme Poisson's ratio values and the structure of amorphous networks as a case study to illustrate how machine learning methods can assist in revealing underlying physical mechanisms. Upon recognizing that the Poisson's ratio relies on the low-frequency vibrational modes of the dynamical matrix, we can then employ a convolutional neural network, trained on the dynamical matrix instead of traditional image recognition, to predict the Poisson's ratio of amorphous networks with a much higher efficiency. Through this example, we aim to showcase the role that artificial intelligence can play in revealing fundamental physical mechanisms, which subsequently improves the machine learning algorithms significantly.

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Designing Amorphous Networks with Adjustable Poisson Ratio from a Simple Triangular Lattice

Cited by 5 publications

References 43 publications

Hydrogen Role on the Properties of Silicon Nitride for Flexible Thin Film Encapsulation

Hydrogen Role on the Properties of Silicon Nitride for Flexible Thin Film Encapsulation

Non-affinity: The emergence of networks from amorphous planar graphs

A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks

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