Radha Somaiya | Computational Methods | Best Researcher Award

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Dr. Radha Somaiya | Computational Methods | Best Researcher Award

 PHD at S.V. National Institute of Technology, Surat, India

Dr. Radha N. Somaiya is an Institute Post-Doctoral Fellow at IIT Bombay, specializing in computational condensed matter physics. Her research focuses on the electronic, optical, and thermoelectric properties of 2D materials, particularly silicon-based compounds. She investigates their applications in photo(electro)catalysis for water splitting and reduction of CO2 and nitrogen. Dr. Somaiya holds a Ph.D. from S.V. National Institute of Technology, Surat, where she studied silicon-based 2D materials for energy and sensing applications. With over 11 journal publications and extensive experience in first-principles calculations, she contributes significantly to the fields of renewable energy and nanotechnology.

Professional Profiles

Education

M.Sc. in Applied Physics (2013 – 2015) The Maharaja Sayajirao University of Baroda, Gujarat, India B.Sc. in Physics (Main), Chemistry, and Mathematics (2010 – 2013) M. B. Patel Science College, Sardar Patel University, Anand, Gujarat, India Research Profile Post-Doctoral Fellow (June 2021 – December 2023) Area of Research: Photo(electro)catalytic water splitting, Carbon dioxide reduction, Nitrogen reduction Institution: Indian Institute of Technology Bombay (IIT-Bombay) Mentor: Prof. Aftab Alam Ph.D. in Computational Condensed Matter Physics (2017 – 2021) Thesis Title: Ab initio Study of Some Silicon-based 2D Materials for Energy and Sensing Applications Institution: S.V. National Institute of Technology Surat, Gujarat Mentor: Asso. Prof. Yogesh Sonvane

Experience

Physics Instructor at Rameshwar School of Science, Vadodara (Aug 2016 – July 2017) Physics Laboratory Assistant at Mahatma Gandhi High School, Vadodara (June 2014 – April 2016) Physics Laboratory Assistant at Navjeevan High School, Vadodara (June 2013 – April 2014). First-principles calculations for electronic structure, optical, elastic, strain, thermodynamic, and photo(electro)catalytic properties. Proficient in Python programming and beginner in Machine Learning. Experience in B. Tech first-year laboratory duties, semester exam duties, and PhD exam duties at IIT-Bombay. Mentored a PhD student at S.V. National Institute of Technology Surat.

Research Interests

Investigating the structural, electronic, and photo(electro)catalytic properties of low-dimensional systems. Carrier transport and carrier recombination rates. Optical properties of solar light harvesting materials. Molecular dynamics, density functional perturbation theory, and transition state nudge elastic band calculations. Water splitting (Hydrogen and Oxygen evolution reactions). Reduction of CO2 and N2 to useful hydrocarbons and NH3. Efficient HER catalyst development. Hydrogen storage and Li-ion or Na-ion batteries.

Honors/Fellowships

Shortlisted with provisional offer letter for the INSPIRE PhD Programme (2015) Qualified the PhD Entrance Test (PET) for the Faculty of Science at The Maharaja Sayajirao University of Baroda (2015) Awarded Prof. (Dr.) Padmini Agarwal Gold Medal for excellence in M.Sc. (Applied Physics) (2015) Silver Medal for highest CGPA in B.Sc. (Physics) at M.B. Patel Science College (2013) Silver Medal for second position in B.Sc. (Physics) at M.B. Patel Science College (2013) Selected for the Summer Programme-Advanced B.Sc. (Physics) at St. Xavier’s College, Ahmedabad (2012)

Research Focuse

Dr. Radha N. Somaiya’s research is centered on computational condensed matter physics, with a strong focus on the properties and applications of two-dimensional (2D) materials. Her work involves exploring the electronic, optical, and thermoelectric properties of various 2D materials such as silicon-based compounds and other low-dimensional systems. Key areas include photo(electro)catalytic water splitting, hydrogen evolution reaction (HER), CO2 reduction, and nitrogen reduction. Utilizing techniques like density functional theory (DFT), Dr. Somaiya investigates the structural and electronic properties of materials for applications in energy conversion, storage, and environmental remediation.

Publications

  1. Palladium-decorated SiX (X= N, P, As, Sb, Bi) catalysts for hydrogen evolution, Publication date: 2024.
  2. Strain modulated optical properties of MoSi2P4 monolayer–insights from DFT, Publication date: 2024.
  3. Biphenylene nanoribbon as a promising electrocatalyst for hydrogen evolution, Publication date: 2024.
  4. Efficient Electrochemical CO2 Reduction Reaction over Cu-decorated Biphenylene, Publication date: 2024.
  5. The activity of Pd supported Pd supported SiX (X= Group-V) Single-atom catalysts for hydrogen evolution reaction., Publication date: 2024.
  6. Biphenylene Nanoribbon as Promising Electrocatalyst for Hydrogen Evolution, Publication date: 2024.
  7. Biphenylene for efficient electrochemical renewable energy conversion., Publication date: 2023.
  8. Quasi-2D PdSi2–xGexN4 (x = 0, 1, 2): Promising Candidates for Spontaneous Overall Water Splitting, Publication date: 2023.
  9. Exploring the transport and optoelectronic properties of silicon diselenide monolayer, Publication date: 2021.
  10. Van der waals SiSe2 homo-bilayers for optoelectronics applications, Publication date: 2021.
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Data Analysis Techniques

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Introduction to Data Analysis Techniques:

Data analysis techniques are fundamental tools across various scientific disciplines, enabling researchers to extract meaningful insights and knowledge from large and complex datasets. Whether in the realms of physics, biology, finance, or social sciences, effective data analysis is crucial for making informed decisions, identifying trends, and drawing conclusions.

Statistical Analysis:

Statistical techniques involve the application of probability theory and mathematical statistics to analyze data, including hypothesis testing, regression analysis, and Bayesian inference, to uncover patterns and relationships.

Machine Learning and Predictive Modeling:

Explore the use of machine learning algorithms to build predictive models, classify data, and make data-driven predictions, with applications in fields such as image recognition, natural language processing, and recommendation systems.

Data Visualization:

Delve into data visualization techniques that enable researchers to represent data graphically, creating informative charts, graphs, and interactive visualizations to communicate findings effectively.

Big Data Analytics:

Focus on the challenges and methods for handling and analyzing large-scale datasets, including distributed computing, data preprocessing, and scalable machine learning algorithms.

Time Series Analysis:

Examine techniques for analyzing time-ordered data, such as financial data, environmental monitoring, and physiological signals, to identify trends, periodicities, and anomalies.

 

 

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