Ranita Pal

Ms. Ranita Pal

Indian Institute of Technology Kharagpur, Kharagpur, India
orcid

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

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

Representative Publications

View All Publications
12 May 2022 in Journal of Computational Chemistry
Verified
Journal Article
Closed access
RPRanita Pal
Ranita Pal
PCPratim K. Chattaraj
Pratim K. Chattaraj

1 January 2021 in Journal of the Indian Chemical Society
Verified
Journal Article
Closed access
RPRanita Pal
Ranita Pal
PCPratim K. Chattaraj
Pratim K. Chattaraj

7 February 2022 in Catalysts
Verified
Journal Article
Open access
PDF file
RPRanita Pal
Ranita Pal
MGManas Ghara
Manas Ghara
PCPratim K. Chattaraj
Pratim K. Chattaraj
The chemistry of frustrated Lewis pair (FLP) is widely explored in the activation of small molecules, the hydrogenation of CO2, and unsaturated organic species. A survey of several experimental works on the activation of small molecules by FLPs and the related mechanistic insights into their reactivity from electronic structure theory calculation are provided in the present review, along with the catalytic hydrogenation of CO2. The mechanistic insight into H2 activation is thoroughly discussed, which may provide a guideline to design more efficient FLP for H2 activation. FLPs can activate other small molecules like, CO, NO, CO2, SO2, N2O, alkenes, alkynes, etc. by cooperative action of the Lewis centers of FLPs, as revealed by several computational analyses. The activation barrier of H2 and other small molecules by the FLP can be decreased by utilizing the aromaticity criterion in the FLP as demonstrated by the nucleus independent chemical shift (NICS) analysis. The term boron-ligand cooperation (BLC), which is analogous to the metal-ligand cooperation (MLC), is invoked to describe a distinct class of reactivity of some specific FLPs towards H2 activation.

16 August 2021 in Frontiers in Chemistry
Verified
Journal Article
Open access
RPRanita Pal
Ranita Pal
APArpita Poddar
Arpita Poddar
PCPratim K. Chattaraj
Pratim K. Chattaraj
Atomic clusters lie somewhere in between isolated atoms and extended solids with distinctly different reactivity patterns. They are known to be useful as catalysts facilitating several reactions of industrial importance. Various machine learning based techniques have been adopted in generating their global minimum energy structures. Bond-stretch isomerism, aromatic stabilization, Rener-Teller effect, improved superhalogen/superalkali properties, and electride characteristics are some of the hallmarks of these clusters. Different all-metal and nonmetal clusters exhibit a variety of aromatic characteristics. Some of these clusters are dynamically stable as exemplified through their fluxional behavior. Several of these cluster cavitands are found to be agents for effective confinement. The confined media cause drastic changes in bonding, reactivity, and other properties, for example, bonding between two noble gas atoms, and remarkable acceleration in the rate of a chemical reaction under confinement. They have potential to be good hydrogen storage materials and also to activate small molecules for various purposes. Many atomic clusters show exceptional opto-electronic, magnetic, and nonlinear optical properties. In this Review article, we intend to highlight all these aspects.

30 December 2018 in Chemical Biology & Drug Design
Verified
Journal Article
Open access
PDF file
RPRanita Pal
Ranita Pal
GJGourhari Jana
Gourhari Jana
SSShamik Sural
Shamik Sural
PCPratim K. Chattaraj
Pratim K. Chattaraj
QSAR/QSPR/QSTR modeling and chemical grouping approach are presented to provide information on the biological properties of various substituted benzene derivatives. A novel descriptor, viz., the square of electrophilicity index (ω2) is proposed to provide a compact correlation between the structure of the compounds and their biological properties which is marginally superior to electrophilicity index (ω) or ω3 in most of the cases, and more or less similar to that obtained from hydrophobicity (or lipophilicity). Besides the straightforward case study, neural networks (NN) are employed to ascertain the robustness of the QSAR model obtained by implementing multiple linear regression (MLR).

5 November 2019 in International Journal of Quantum Chemistry
Verified
Journal Article
Closed access
PDF file
GJGourhari Jana
Gourhari Jana
RPRanita Pal
Ranita Pal
SSShamik Sural
Shamik Sural
PCPratim K. Chattaraj
Pratim K. Chattaraj