Saurabh Vinod Parmar

I am Saurabh Parmar; a Master in Inorganic Chemistry and perusing Ph. D. in computational catalysis.

Catalysis plays a vital role in many chemical processes, allowing efficient and selective transformation of raw materials into useful products. However, many traditional catalytic processes involve the use of toxic and non-renewable materials, leading to negative environmental impacts and unsustainable practices. Efficient capture, storage, and sustainable catalysis can play a significant role in the capture and conversion of CO2 into methanol, which is considered an important renewable fuel and chemical feedstock. The development of efficient and selective catalytic processes for CO₂ reduction not only offers a practical solution to reduce greenhouse gas emissions but also contributes to the development of a sustainable chemical industry (https://doi.org/10.3389/fchem.2021.778718). The development of novel COF materials and catalysts and optimization of reaction conditions can lead to higher efficiency and selectivity in CO₂ conversion, making this process more economically viable and environmentally friendly.

COFs represent a promising field for CO₂ capture owing to their unique properties such as high surface area, tuneable structure, and reversible adsorption capabilities. Ongoing research in this field has the potential to contribute significantly to the development of efficient and sustainable CO₂ capture technologies, helping to mitigate the impact of CO₂ emissions (https://doi.org/10.1002/cphc.202200808).

Through this research, the primary objective is to investigate and advance sustainable catalytic processes to address pressing energy and sustainability issues. This research mainly involves designing and modelling novel catalysts to study their catalytic activity toward CO₂ activation. This research also employs theoretical calculations, such as density functional theory (DFT) and molecular dynamics simulations, to gain insights into the underlying catalyst mechanisms. Further to employ machine learning methods to expedite the catalysis discovery processes.  By exploring these topics, our goal is to contribute to the development of efficient and sustainable energy conversion methods that can significantly reduce carbon emissions and mitigate climate change.