The Effect of Operating Temperature on the Response Time of Optically Driven Liquid Crystal Displays

Chigrinov, Vladimir G.; Sun, Jiatong; Kuznetsov, M. M.; Belyaev, V. V.; Чаусов, Д. Н.

doi:10.3390/cryst10070626

“…Having the initial state n(z, t = 0) , a pair of elastic constants and the rotational viscosity γ 1 (e.g. 0.098 Pa s for 5CB at the room temperature 37,38 ), the dynamics n(z, t) is numericallly simulated (see "Methods"). The boundary conditions n(z = 0, t) and n(z = D, t) are determined by the selected anchoring type on each boundary.…”

Section: Figurementioning

confidence: 99%

Neural networks determination of material elastic constants and structures in nematic complex fluids

Zaplotnik

¹

,

Pišljar

²

,

Škarabot

³

et al. 2023

Sci Rep

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Supervised machine learning and artificial neural network approaches can allow for the determination of selected material parameters or structures from a measurable signal without knowing the exact mathematical relationship between them. Here, we demonstrate that material nematic elastic constants and the initial structural material configuration can be found using sequential neural networks applied to the transmmited time-dependent light intensity through the nematic liquid crystal (NLC) sample under crossed polarizers. Specifically, we simulate multiple times the relaxation of the NLC from a random (qeunched) initial state to the equilibirum for random values of elastic constants and, simultaneously, the transmittance of the sample for monochromatic polarized light. The obtained time-dependent light transmittances and the corresponding elastic constants form a training data set on which the neural network is trained, which allows for the determination of the elastic constants, as well as the initial state of the director. Finally, we demonstrate that the neural network trained on numerically generated examples can also be used to determine elastic constants from experimentally measured data, finding good agreement between experiments and neural network predictions.

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“…Having the initial state n(z,t = 0), a pair of elastic constants and the rotational viscosity γ 1 (e.g. 0.098 Pa s for 5CB at the room temperature 33,34 ), the dynamics n(z,t) is numericallly simulated (see Methods 4). The boundary conditions n(z = 0,t) and n(z = D,t) are determined by the selected anchoring type on each boundary.…”

Section: Determining K 11 and K 33mentioning

confidence: 99%

Neural networks determination of material elastic constants and structures in nematic complex fluids

Zaplotnik

¹

,

Pišljar

²

,

Škarabot

³

et al. 2023

Preprint

View full text Add to dashboard Cite

Supervised machine learning and artificial neural network approaches can allow for the determination of selected material parameters or structures from a measurable signal without knowing the exact mathematical relationship between them. Here, we demonstrate that material nematic elastic constants and the initial structural material configuration can be found using sequential neural networks applied to the transmmited time-dependent light intensity through the nematic liquid crystal (NLC) sample under crossed polarizers. Specifically, we simulate multiple times the relaxation of the NLC from a random (qeunched) initial state to the equilibirum for random values of elastic constants and, simultaneously, the transmittance of the sample for monochromatic polarized light. The obtained time-dependent light transmittances and the corresponding elastic constants form a training data set on which the neural network is trained, which allows for the determination of the elastic constants, as well as the initial state of the director. Finally, we demonstrate that the neural network trained on numerically generated examples can also be used to determine elastic constants from experimentally measured data, finding good agreement between experiments and neural network predictions.

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“…This approach involves application of amplitide modulator. However, azimuthal anchoring energy is also affected by several factors: azo dye layer thickness [9], temperature [18], LC cell fabrication process [19], etc. The uniformity of the irradiated light beam is also crucial for this approach.…”

Section: Geometry Of Orw E-paper and Properties Of Materialsmentioning

confidence: 99%

Optically Rewritable Liquid Crystal Displays: Characteristics and Performance

Chigrinov

¹

,

Kudreyko

²

,

Подгорнов

³

2021

Self Cite

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Recent achievements in the photoalignment technique for fabrication of optically rewritable electronic paper with high performance characteristics are surveyed with emphasis on temporal constraints on the exposure process. The possibility of creating electrode-free electronic paper has very important practical aspects. However, many existing studies do not include sufficient analysis on how to achieve acceptable reflective characteristics within short exposure time. In order to achieve this goal, we have applied the rotational diffusion model. We find that the parameters of the diffusion model can be adjusted to get acceptable light-reflecting characteristics within 10 s of exposure. In comparison with the long-time exposure, the reflectance coefficient reduces by 24%. The route to material improvements for optimized e-paper device is discussed.

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The Effect of Operating Temperature on the Response Time of Optically Driven Liquid Crystal Displays

Cited by 10 publications

References 24 publications

Neural networks determination of material elastic constants and structures in nematic complex fluids

Neural networks determination of material elastic constants and structures in nematic complex fluids

Neural networks determination of material elastic constants and structures in nematic complex fluids

Optically Rewritable Liquid Crystal Displays: Characteristics and Performance

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