Optical Materials

Sudarsan, V.

doi:10.1016/b978-0-12-385142-0.00008-8

Cited by 20 publications

(7 citation statements)

References 63 publications

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“…The optical properties of a material define how the material interacts with electromagnetic radiation. 61 So, a detailed study of the optical properties is crucial in many industrial and scientific applications such as heat transfer, contactless temperature measurement, laser technology, optics industry for the productions of mirrors, lenses and optical windows, photovoltaic industry, the aerospace industry and so on. 62,63 Therefore, we have studied the optical properties of Na 2 SiO 3 at different compressive pressures by calculating the dielectric constant ( ε ), absorption coefficient ( α ) and refractive index ( η ) as a function of the photon energy (eV).…”

Section: Resultsmentioning

confidence: 99%

Electronic, mechanical, optical and piezoelectric properties of glass-like sodium silicate (Na₂SiO₃) under compressive pressure

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Electronic, mechanical, optical and piezoelectric properties of glass-like sodium silicate (Na₂SiO₃) under compressive pressure

et al. 2022

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“…The external electric and magnetic fields applied to the material determine these optical properties. Optical materials can be made out of a wide variety of organic or inorganic materials [ 1 ]. Tetra-(1,4-dithiin)porphyrazine is one of the phthalocyanine derivatives having two sulfur atoms at equivalent 1,4-positions of the phthalocyanine benzene units [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Structural and Optical Properties of Some [1,4]Dithiine-porphyrazine Dyes

et al. 2022

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1,4-Bis(p-tolylamino)-6,7-dichloroanthraquinone 1 when reacted with di(sodiothio)-maleonitrile 2 afforded heterocyclic thianone compound, 5,12-dioxo-5,12-dihydroanthro[2,3-b][1,4]dithiine-2,3-dicarbonitrile 3. Using lithium/pentanol and acetic acid, the dicarbonitrile product 3 was cyclotetramerized, yielding the matching tetra 5,12-dioxo-5,12-dihydroanthro[2,3-b][1,4]dithiine-porphyrazine dye compound (2H-Pz) 4a. The dicarbonitrile molecule was a ring-shaped metallic product utilizing metallic salt and quinoline, yielding the corresponding tetra 5,12-dioxo-5,12-dihydroanthro[2,3-b][1,4]dithiine-porphyrazinato-metal II dyes (M-Pz), M = Zn, Co, or Ni 4b–d. The produced compounds’ elemental analysis investigation, Infrared, and nuclear magnetic resonance spectrum information accord with the structures attributed to them. The cyclotetramerization and complexation reactions are ensured by the molecular weight and metal load of the produced products. The inclusion of electron-donating groups resulted in a lower optical band gap of the produced dye sensitizers, with “push–pull” promotion of about 1.55 eV. The prepared substituted porphyrazines reveal high absorption in the UV–VIS region, which could be of potential value as a building block for novel electronic and optical materials as well as a sensor for technology. This is considered for improving solar cell absorption. The absorption bands of the synthesized porphyrazine dyes extend beyond 800 nm, so these dyes could be useful in various optoelectronic applications.

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“…e elements used in optical applications are mostly made of glass, crystalline materials, polymers, or plastic materials. Properties such as degree of transparency, refractive index, and spectrum are the deciding factors for the choice of optical materials [1]. In recent years, polymer optics have become increasingly widely used.…”

Section: Introductionmentioning

confidence: 99%

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Nguyen

Truong

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the prediction of surface roughness in manufacturing polycarbonate (PC) by applying Bayesian optimization for machine learning models. The input variables of ultraprecision turning—namely, feed rate, depth of cut, spindle speed, and vibration of the X-, Y-, and Z-axis—are the main factors affecting surface quality. In this research, six machine learning- (ML-) based models—artificial neural network (ANN), Cat Boost Regression (CAT), Support Vector Machine (SVR), Gradient Boosting Regression (GBR), Decision Tree Regression (DTR), and Extreme Gradient Boosting Regression (XGB)—were applied to predict the surface roughness (Ra). The predictive performance of the baseline models was quantitatively assessed through error metrics: root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The overall results indicate that the XGB and CAT models predict Ra with the greatest accuracy. In improving baseline models such as XGB and CAT, the Bayesian optimization (BO) is next used to determine their best hyperparameters, and the results indicate that XGB is the best model according to the evaluation metrics. Results have shown that the performance of the models has been improved significantly with BO. For example, the values of RMSE and MAE of XGB have decreased from 0.0076 to 0.0047 and from 0.0063 to 0.0027, respectively, for the training dataset. Using the testing dataset, the values of RMSE and MAE of XGB have decreased from 0.4033 to 0.2512 and from 0.2845 to 0.2225, respectively. Moreover, the vibrations of the X, Y, and Z axes and feed rate are the most significant feature in predicting the results, which is in high accordance with the literature. We find that, in a specified value domain, the vibration of the axes has a greater influence on the surface quality than does the cutting condition.

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Optical Materials

Cited by 20 publications

References 63 publications

Electronic, mechanical, optical and piezoelectric properties of glass-like sodium silicate (Na₂SiO₃) under compressive pressure

Electronic, mechanical, optical and piezoelectric properties of glass-like sodium silicate (Na₂SiO₃) under compressive pressure

Investigation of Structural and Optical Properties of Some [1,4]Dithiine-porphyrazine Dyes

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

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Optical Materials

Cited by 20 publications

References 63 publications

Electronic, mechanical, optical and piezoelectric properties of glass-like sodium silicate (Na2SiO3) under compressive pressure

Electronic, mechanical, optical and piezoelectric properties of glass-like sodium silicate (Na2SiO3) under compressive pressure

Investigation of Structural and Optical Properties of Some [1,4]Dithiine-porphyrazine Dyes

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

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Electronic, mechanical, optical and piezoelectric properties of glass-like sodium silicate (Na₂SiO₃) under compressive pressure

Electronic, mechanical, optical and piezoelectric properties of glass-like sodium silicate (Na₂SiO₃) under compressive pressure