Kabil Dev Mahato | Spectroscopy | Editorial Board Member

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.

Ruby Ahmed | Chemistry | Best Researcher Award

Published-on by Indian Scientist

Dr. Ruby Ahmed | Chemistry | Best Researcher Award

Shibli National College, Azamgarh | India

Dr. Ruby Ahmed is an accomplished Associate Professor with a strong academic background in chemistry, holding advanced degrees culminating in a PhD focused on chemosensors. Her academic journey includes rigorous education in chemical sciences followed by post-doctoral and faculty roles in synthesis, structural analysis and sensor development. With over a dozen peer-reviewed publications in high-impact journals, her scholarly output includes experimental and modeling studies on fluorophore-based detection of nitroaromatic compounds, conductive polymer–based gas sensors, and crystal structure analyses toward environmental pollutant sensing. Her work has attracted several dozen citations and recognition among peers, with approximately thirty-seven citations recorded across eleven documents on research platforms, suggesting an estimated h-index in the range of 5 to 6. Her research interests center on chemosensors, nanocomposites, crystal engineering and environmental pollutant detection. In addition to research, she has served in key administrative and leadership roles—heading a chemistry department, convening university-level committees, organizing boards of studies and doctoral committees, and reviewing articles for Springer Nature. She has also acted as principal investigator on regional research funding projects. Her contributions have earned institutional recognition. Dr. Ahmed continues to advance interdisciplinary research in functional materials for sustainable sensing applications, mentoring students and shaping curricula. She remains dedicated to translating fundamental chemistry into practical environmental technologies.

Profiles: Scopus

Featured Publication

Ahmed, R., Ali, F., Alam, M. J., Tameem, M., Ali, A., Ahmad, M., & Khan, M. A. (2025). Fluorescence quenching aptitude of carbazole for the detection of nitro-aromatics: A comprehensive experimental analysis and computational studies validation. RSC Advances, 15.