Effect of High Valency Cations on High Refractive-Index and Low Dispersion Characteristics of Borate Glasses

Abstract A borate glasses doped with rare earth Gd3+ ion in the system [6OB2O3 + 30 L12O + x Gd2O3 + (10-x) BiCl3] is prepared by the conventional melt quenching method and their optical properties have been studied. The oxide ion polarizability parameter is calculating by using refractive index of glass materials, which is obtained from UV-Vis spectra. The borate glasses are known to possess high oxide ion polarizability, high refractive index, high basicity and low interaction parameter values. In this present study, theoretical calculation of basicity and interaction parameter, using oxide ion polarization, of the glass network has been addressed. A good linear correlation between the interaction parameter and basicity is observed.

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Terahertz metamaterial with broadband and low-dispersion high refractive index

Optics Letters ◽

10.1364/ol.397230 ◽

2020 ◽

Vol 45 (17) ◽

pp. 4754

Author(s):

Xi Gao ◽

Fa Long Yu ◽

Cheng Lin Cai ◽

Chun Ying Guan ◽

Jin Hui Shi ◽

...

Keyword(s):

Refractive Index ◽

High Refractive Index ◽

Low Dispersion

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A New Mounting Medium of High Refractive Index

Scientific American ◽

10.1038/scientificamerican02131886-8429csupp ◽

1886 ◽

Vol 21 (528supp) ◽

pp. 8429-8430

Keyword(s):

Refractive Index ◽

High Refractive Index

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Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

10.26434/chemrxiv.7670903.v1 ◽

2019 ◽

Author(s):

Mohammad Atif Faiz Afzal ◽

Mojtaba Haghighatlari ◽

Sai Prasad Ganesh ◽

Chong Cheng ◽

Johannes Hachmann

Keyword(s):

Data Mining ◽

Refractive Index ◽

High Throughput ◽

First Principles ◽

High Throughput Screening ◽

Large Scale ◽

Computational Study ◽

High Refractive Index ◽

Structural Features ◽

Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

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