Dr. Syam Kumar R

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Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2019 in Applied Catalysis B: Environment and Energy

Journal Article

Theoretical and experimental investigation of visible light responsive AgBiS2-TiO2 heterojunctions for enhanced photocatalytic applications

Laura Clarizia

16 January 2024 in Crystals

Journal Article

Lead-Free Double Perovskites: A Review of the Structural, Optoelectronic, Mechanical, and Thermoelectric Properties Derived from First-Principles Calculations, and Materials Design Applicable for Pedagogical Purposes

David Olubiyi Obada

Shittu Babatunde Akinpelu

Simeon Akindele Abolade

Emmanuel Okafor

Aniekan Magnus Ukpong

Akinlolu Akande

Metal halide perovskite materials have shown significant advancements in their application as light absorbers in perovskite solar cells, with power conversion efficiencies reaching 27%. However, lead-based perovskites pose a concern due to their toxicity and stability issues in moisture, UV radiation, and heat. This has led to a pressing need to explore substitute materials that do not contain lead but maintain the remarkable characteristics of lead-based perovskites. This review article focuses on halide double perovskites characterised by the A₂B’B”X₆ composition, highlighting their structural, optical, thermoelectric, and mechanical capabilities. Additionally, the review evaluates several materials databases to investigate materials suitable for high-throughput first-principles calculations integrated inside density functional theory. The review aims to identify novel perovskite materials, offer a thorough evaluation of the potential benefits and drawbacks associated with this class of materials, and, from the pedagogical standpoint, discover effective instructional frameworks.

20 December 2019 in ACS Omega

Journal Article

Ternary Metal Chalcogenide Heterostructure (AgInS₂–TiO₂) Nanocomposites for Visible Light Photocatalytic Applications

Laura Clarizia

Hybrid nanoarchitectures of AgInS₂ and TiO₂ photocatalysts were prepared by using a modified sol–gel method. The experimental results reveal that these nanocomposites display enhanced visible light absorption and effective charge carrier separation compared to their pristine parent samples (AgInS₂ or TiO₂). 0.5 wt % AgInS₂ loading was found to be the optimum concentration for photocatalytic applications. More than 95% of doxycycline degradation was achieved within 180 min of solar light illumination. Similarly, the dopant concentrations at lower values (<2 wt %) exhibited 300 times higher H₂ generation rate under visible light irradiation compared to AgInS₂ and TiO₂. The microbial strains (Escherichia coli and Staphylococcus aureus) exhibited a 99.999% reduction within half an hour of simulated solar light illumination. The computational investigation was employed to understand the structural, electronic, and the dielectric properties of AgInS₂ and TiO₂ composites. The improved photocatalytic results are explained as a result of the decreased rate of exciton recombination. The current investigation opens up new insights into the use of novel ternary heterostructure nanocomposites for improved visible light activity.

1 March 2021 in Applied Catalysis B: Environment and Energy

Journal Article

New insights into the efficient charge transfer of ternary chalcogenides composites of TiO2

Priyanka Ganguly

Syam Kumar R

Marica Muscetta

Nisha T. Padmanabhan

Laura Clarizia

Suresh Pillai

1 January 2022 in International Journal of Hydrogen Energy

Journal Article

The rise of Nb-, Ta-, and Bi-based oxides/chalcogenides for photocatalytic applications

3 December 2025 in Crystals

Journal Article

Machine Learning for Photocatalytic Materials Design and Discovery

David Olubiyi Obada

Shittu Babatunde Akinpelu

Simeon Akindele Abolade

Mkpe O. Kekung

Emmanuel Okafor

Akinlolu Akande

Traditionally, the development and optimisation of photocatalytic materials have relied on experimental approaches and density functional theory (DFT) calculations. Although these methods have driven significant scientific progress, they are increasingly constrained by high computational costs, lengthy development cycles, and limited scalability. In recent years, machine learning (ML) has emerged as a powerful and sustainable alternative, offering a data-driven framework that accelerates materials discovery through rapid and accurate property prediction. This review highlights the essential components of the ML workflow data collection, feature engineering, model selection, and validation while exploring its application in predicting photocatalytic properties. It further discusses recent advances in forecasting key characteristics such as band edge positions, charge carrier mobility, and surface reactivity using both supervised and unsupervised ML techniques. Persistent challenges, including data scarcity, model interpretability, and generalisability, are also addressed, alongside potential strategies to improve the robustness and reliability of ML-driven materials design. By combining high prediction accuracy with superior computational efficiency, ML holds the potential to revolutionise high-throughput screening and guide the systematic development of next-generation photocatalysts.

About Me

Dr. Syam Kumar R is a researcher in the field of Computational Materials Science. He currently serves as an Assistant Lecturer at the Department of Health and Nutritional Science in the School of Science at Atlantic Technological University (ATU) Sligo, Ireland. His academic journey has been marked by a profound commitment to advancing our understanding of materials for energy applications and environmental sustainability. Following his Integrated M.Sc. in Physics at the Central University of Tamil Nadu, India, in 2016, Dr. Kumar pursued a Ph.D. in Science by Research at the Institute of Technology Sligo, Ireland, from 2016 to 2020. His doctoral research, supervised by Dr. Akinlolu Akande, Dr. Fadwa El Mellouhi, and Prof. Stefano Sanvito, focused on "Bismuth Based Ternary Chalcogenides as Novel Photovoltaics and Photocatalysts: A First-Principles Study." His technical expertise encompasses a range of skills, from basic Fortran 95 and Python to advanced tools like Fritz-Haber Institute Ab Initio Molecular Simulations (FHI-AIMS) package and Vienna Ab-initio Simulation Package (VASP).Dr. Kumar's academic and research interests revolve around the computational modelling of materials for energy applications, carbon dioxide capture materials, and hydrogen energy storage. His contributions to the scientific community are underscored by a prolific record of publications and active participation in conferences, workshops, and training schools.

Dr. Syam Kumar R

Dr. Syam Kumar R

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Representative Publications

About Me