A comparative study of conventional FRET and light harvesting properties of Rh-110/Rh-6G and Rh-19/Rh-B organic dye pairs impregnated in sol-gel glasses

Mahato, Kapil Dev; Kumar, Uday

doi:10.1088/2050-6120/accfe4

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article4

Preprint1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Stokes shift prediction of fluorescent organic dyes using machine learning based hybrid cascade models

Mahato,

Das,

Azad

et al. 2024

Dyes and Pigments

View full text Add to dashboard Cite

Stokes shift prediction of fluorescent organic dyes using machine learning based hybrid cascade models

Mahato,

Das,

Azad

et al. 2024

Dyes and Pigments

View full text Add to dashboard Cite

Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields

Mahato,

Kumar

2024

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

View full text Add to dashboard Cite

Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields

Mahato,

Kumar Das,

Azad

et al. 2024

APL Machine Learning

View full text Add to dashboard Cite

Fluorescent organic dyes are extensively used in the design and discovery of new materials, photovoltaic cells, light sensors, imaging applications, medicinal chemistry, drug design, energy harvesting technologies, dye and pigment industries, and pharmaceutical industries, among other things. However, designing and synthesizing new fluorescent organic dyes with desirable properties for specific applications requires knowledge of the chemical and physical properties of previously studied molecules. It is a difficult task for experimentalists to identify the photophysical properties of the required chemical molecule at negligible time and financial cost. For this purpose, machine learning-based models are a highly demanding technique for estimating photophysical properties and may be an alternative approach to density functional theory. In this study, we used 15 single models and proposed three different hybrid models to assess a dataset of 3066 organic materials for predicting photophysical properties. The performance of these models was evaluated using three evaluation parameters: mean absolute error, root mean squared error, and the coefficient of determination (R2) on the test-size data. All the proposed hybrid models achieved the highest accuracy (R2) of 97.28%, 95.19%, and 74.01% for predicting the absorption wavelengths, emission wavelengths, and quantum yields, respectively. These resultant outcomes of the proposed hybrid models are ∼1.9%, ∼2.7%, and ∼2.4% higher than the recently reported best models’ values in the same dataset for absorption wavelengths, emission wavelengths, and quantum yields, respectively. This research promotes the quick and accurate production of new fluorescent organic dyes with desirable photophysical properties for specific applications.

show abstract

A comparative study of conventional FRET and light harvesting properties of Rh-110/Rh-6G and Rh-19/Rh-B organic dye pairs impregnated in sol-gel glasses

Cited by 5 publications

References 86 publications

Stokes shift prediction of fluorescent organic dyes using machine learning based hybrid cascade models

Stokes shift prediction of fluorescent organic dyes using machine learning based hybrid cascade models

Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields

Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields

Contact Info

Product

Resources

About