Published-on by Indian Scientist

Mr. Kabil Dev Mahato | Spectroscopy | Editorial Board Member

National Institute of Technology Jamshedpur | India

Kapil Dev Mahato is a researcher in physics whose work spans laser spectroscopy, computational chemistry, and data-driven modeling of organic dyes. His scientific contributions focus on predicting photophysical properties such as absorption and emission wavelengths, quantum yields, Stokes shift values, and Förster distance parameters using advanced machine-learning approaches. He has authored multiple peer-reviewed journal articles, conference papers, review works, and a book chapter, reflecting strong academic engagement and interdisciplinary impact. His Google Scholar profile reports 153 citations, an h-index of 7, and an i10-index of 6, demonstrating growing recognition in spectroscopy and machine-learning research communities. His academic training includes strong foundations in physics at both undergraduate and postgraduate levels, further strengthened by competitive national research fellowships. He has collaborated with experts in spectroscopy and computer science, contributing to work on fluorescent dyes, sol-gel systems, optical materials, and predictive ML models for scientific and biomedical data. His research interests include organic dye photophysics, fluorescence mechanisms, nanomaterials, ensemble learning methods, and scientific data modeling. He has participated in multiple national and international conferences, presenting work on spectroscopy and machine-learning applications. His overall profile highlights consistent research productivity, interdisciplinary collaboration, and commitment to advancing spectroscopy and computational modeling.

Profile: Google Scholar | Research gate | Linked In

Featured Publications

Mahato, K. D., & Kumar, U. (2024). Optimized machine learning techniques enable prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 308, 123768.

Mahato, K. D., Das, S. S. K., Azad, C., & Kumar, U. (2024). Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields. APL Machine Learning, 2(1).

Bhowmick, A., Mahato, K. D., Azad, C., & Kumar, U. (2022). Heart disease prediction using different machine learning algorithms. In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC) (pp. 60–65).

Mahato, K. D., Das, S. S. G. K., Azad, C., & Kumar, U. (2024). Stokes shift prediction of fluorescent organic dyes using machine learning-based hybrid cascade models. Dyes and Pigments, 222, 111918.

Mahato, K. D., & Kumar, U. (2023). A review of organic dye-based nanoparticles: Preparation, properties, and engineering/technical applications. Mini-Reviews in Organic Chemistry, 20(7), 655–674.

Kabil Dev Mahato | Spectroscopy | Editorial Board Member

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