Dr. Biswarup Pathak
Professor
Department of Chemistry
PoD Building, Room Number: 1A 724
Phone (Office): 0731-660-3348; Lab:5157
Email: biswarup[at]iiti.ac.in and biswarup.pathak[at]gmail.com
Research Areas
Our group is actively working on pressing problems for the nation and the world at large using computational modelling/machine learning studies of nanomaterials (specially nanocluster) for energy, catalysis, and bio-applications. In a very short time, we have contributed remarkably in each of these following areas.
Nanoclusters/Catalysis: Our group has been trying to understand the activity of finite size (~1-2 nm) nanoclusters, enclosed by proper facets (111, 100, among others). We have established (Review Article: Catalysis Sc. and Technology, 9, 4835, 2019) that the catalytic activities of nanoclusters are not exactly dependent on the amounts of unsaturated sites contrary to the conventional concepts. In fact, such unsaturaion leads to poisoning the surface, which in turn affect the activity of the catalyst. Our group has also established that the cluster fluxionalities (Review article: WIRES computational molecular science, 1508, 1, 2021) are very important for small-sized nanocluster (less than 1nm). These studies have the potential to make a big impact in the scientific community as they provide a complete atomic insight towards the mechanisms and the reasons behind the excellent activity of nanocluster compared to their bulk structures.
Dual-ion Batteries: Our group also interested in computational designing of nanomaterials for energy related studies, specially in the field of batteries, dual ion batteries. For this, we have been working towards the designing of nanomaterials based electrodes for dual-ion batteries and for comparisons, we study their respective bulk systems too. In 2017, first time, our group has proposed the staging mechanisms (Phys. Chem. Chem. Phys., 19, 7980, 2017 | cited ~100 times) of dual-ion based batteries. The idea invited a lot of interests in the scientific community and as a result, Prof. Maksym V. Kovalenko’s group at ETH Zürich, Switzerland approached us to collaborate towards the designing of dual ion batteries (DIBs). This indeed leads to develop a dual ion battery (DIB) of 4.7 V (Nature Commun., 9, 4469, 2018 | cited >100 times). Since then, we have proposed stagging mechanisms for dual-ion based batteries, solid-electrolyte phase formation mechanisms, and nano-electrode materials for dual-ion based batteries (Journal of Physical Chemistry C, 124, 7634, 2020; Journal of Physical Chemistry C, 123, 23863, 2019).
DNA Sequencing: Solid nanopores based third generation DNA sequencing techniques have been proposed for ultrafast DNA sequencing. Since 2012, our group has been working in this direction. The recent COVID pandemic has shown the importance of ultrafast DNA sequencing. We have also used various computational techniques (transverse current and ionic current) for third generation DNA as well as protein sequencing. Our contributions (Journal of Physical Chemistry C, 123, 22377, 2019; and ACS Applied Materials and Interface, 11, 219, 2019; ACS Applied Bio materials, 4, 1403, 2021) in this area has invited a lot of interest in the scientific community.
Machine Learning: Our main objective is also to understand the activities of nanoclusters/nanomaterials important for all the above applications through Machine learning and Artificial Intelligence based studies. Nanoclusters/nanomaterials are already known for their excellent catalytic activity for various catalytic reactions. Using Machine learning, we have screened a large number of high entropy based catalysts for predicting the catalytic activity and product selectivity for CO2 reduction reactions. Here, the supervising learning-based machine learning techniques have been used, based on our expertise in this field, to screen the catalysts. We have shown that machine learning based techniques can not only be used to screen the CO2 reduction catalysts but can also be used to screen catalysts for product selectivity. In our recent study, we have shown that alloy-based catalysts (ternary/quaternary) are more efficient for product selectivity (methanol) compared to the high entropy-alloy based catalysts. The work has been just published in ACS Applied Materials and Interfaces (2021).
Nanoclusters/Catalysis: Our group has been trying to understand the activity of finite size (~1-2 nm) nanoclusters, enclosed by proper facets (111, 100, among others). We have established (Review Article: Catalysis Sc. and Technology, 9, 4835, 2019) that the catalytic activities of nanoclusters are not exactly dependent on the amounts of unsaturated sites contrary to the conventional concepts. In fact, such unsaturaion leads to poisoning the surface, which in turn affect the activity of the catalyst. Our group has also established that the cluster fluxionalities (Review article: WIRES computational molecular science, 1508, 1, 2021) are very important for small-sized nanocluster (less than 1nm). These studies have the potential to make a big impact in the scientific community as they provide a complete atomic insight towards the mechanisms and the reasons behind the excellent activity of nanocluster compared to their bulk structures.
Dual-ion Batteries: Our group also interested in computational designing of nanomaterials for energy related studies, specially in the field of batteries, dual ion batteries. For this, we have been working towards the designing of nanomaterials based electrodes for dual-ion batteries and for comparisons, we study their respective bulk systems too. In 2017, first time, our group has proposed the staging mechanisms (Phys. Chem. Chem. Phys., 19, 7980, 2017 | cited ~100 times) of dual-ion based batteries. The idea invited a lot of interests in the scientific community and as a result, Prof. Maksym V. Kovalenko’s group at ETH Zürich, Switzerland approached us to collaborate towards the designing of dual ion batteries (DIBs). This indeed leads to develop a dual ion battery (DIB) of 4.7 V (Nature Commun., 9, 4469, 2018 | cited >100 times). Since then, we have proposed stagging mechanisms for dual-ion based batteries, solid-electrolyte phase formation mechanisms, and nano-electrode materials for dual-ion based batteries (Journal of Physical Chemistry C, 124, 7634, 2020; Journal of Physical Chemistry C, 123, 23863, 2019).
DNA Sequencing: Solid nanopores based third generation DNA sequencing techniques have been proposed for ultrafast DNA sequencing. Since 2012, our group has been working in this direction. The recent COVID pandemic has shown the importance of ultrafast DNA sequencing. We have also used various computational techniques (transverse current and ionic current) for third generation DNA as well as protein sequencing. Our contributions (Journal of Physical Chemistry C, 123, 22377, 2019; and ACS Applied Materials and Interface, 11, 219, 2019; ACS Applied Bio materials, 4, 1403, 2021) in this area has invited a lot of interest in the scientific community.
Machine Learning: Our main objective is also to understand the activities of nanoclusters/nanomaterials important for all the above applications through Machine learning and Artificial Intelligence based studies. Nanoclusters/nanomaterials are already known for their excellent catalytic activity for various catalytic reactions. Using Machine learning, we have screened a large number of high entropy based catalysts for predicting the catalytic activity and product selectivity for CO2 reduction reactions. Here, the supervising learning-based machine learning techniques have been used, based on our expertise in this field, to screen the catalysts. We have shown that machine learning based techniques can not only be used to screen the CO2 reduction catalysts but can also be used to screen catalysts for product selectivity. In our recent study, we have shown that alloy-based catalysts (ternary/quaternary) are more efficient for product selectivity (methanol) compared to the high entropy-alloy based catalysts. The work has been just published in ACS Applied Materials and Interfaces (2021).