Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications

Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications

Recent advancements in glass science have focused on optimizing optical and structural properties through the incorporation of rare oxide nanoparticles, such as lanthanum and gold oxide, to enhance transparency, refractive index, and band gap energy for photonic applications. Additionally, the integ...

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Bibliographic Details
Main Authors: S.N., Nazrin, Doroody, Camellia, Liyana Adilla, Burhanuddin, Jothi, Neesha, Noraini, Ibrahim, Halimah, Badioze Zaman, M.H.M., Tahir, Soudagar, Manzoore Elahi M., Ramesh, S., Shelare, Sagar, Sharma, Shubham
Format: Article
Language:English
Published: Elsevier 2025
Subjects:
QA75 Electronic computers. Computer science
QC Physics
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
TP Chemical technology
Online Access:http://umpir.ump.edu.my/id/eprint/44999/
http://umpir.ump.edu.my/id/eprint/44999/1/Machine%20learning-driven%20prediction%20of%20optical%20and%20physical%20properties.pdf

Internet

http://umpir.ump.edu.my/id/eprint/44999/
http://umpir.ump.edu.my/id/eprint/44999/1/Machine%20learning-driven%20prediction%20of%20optical%20and%20physical%20properties.pdf

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