Mood Mohan

1.8k total citations
45 papers, 1.4k citations indexed

About

Mood Mohan is a scholar working on Biomedical Engineering, Catalysis and Materials Chemistry. According to data from OpenAlex, Mood Mohan has authored 45 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Biomedical Engineering, 20 papers in Catalysis and 15 papers in Materials Chemistry. Recurrent topics in Mood Mohan's work include Ionic liquids properties and applications (20 papers), Biofuel production and bioconversion (17 papers) and Catalysis for Biomass Conversion (15 papers). Mood Mohan is often cited by papers focused on Ionic liquids properties and applications (20 papers), Biofuel production and bioconversion (17 papers) and Catalysis for Biomass Conversion (15 papers). Mood Mohan collaborates with scholars based in United States, India and China. Mood Mohan's co-authors include Vaibhav V. Goud, Tamal Banerjee, Blake A. Simmons, Seema Singh, John M. Gladden, Jeremy C. Smith, Michelle K. Kidder, Papu Kumar Naik, Kenneth L. Sale and Sandip Paul and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Chemical Physics and The Journal of Physical Chemistry B.

In The Last Decade

Mood Mohan

41 papers receiving 1.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mood Mohan United States 24 800 559 246 205 197 45 1.4k
Wei-Chien Tu United Kingdom 8 643 0.8× 486 0.9× 249 1.0× 154 0.8× 123 0.6× 8 1.9k
Michal Jablonský Slovakia 22 799 1.0× 546 1.0× 131 0.5× 341 1.7× 170 0.9× 97 1.9k
Dongbao Fu Canada 8 758 0.9× 274 0.5× 109 0.4× 225 1.1× 75 0.4× 8 1.2k
Rílvia Saraiva de Santiago-Aguiar Brazil 22 664 0.8× 386 0.7× 128 0.5× 51 0.2× 222 1.1× 47 1.3k
Xue‐Dan Hou China 14 1.1k 1.4× 490 0.9× 71 0.3× 367 1.8× 90 0.5× 24 1.6k
Victoria Rigual Spain 15 486 0.6× 333 0.6× 74 0.3× 155 0.8× 88 0.4× 26 879
André Pinkert New Zealand 8 1.1k 1.4× 588 1.1× 144 0.6× 929 4.5× 84 0.4× 10 2.0k
Filipa A. Vicente Slovenia 19 204 0.3× 321 0.6× 137 0.6× 181 0.9× 236 1.2× 36 1.0k
Andrea Škulcová Slovakia 15 351 0.4× 360 0.6× 74 0.3× 120 0.6× 99 0.5× 24 915
Rui Ferreira Portugal 14 295 0.4× 633 1.1× 81 0.3× 116 0.6× 206 1.0× 26 1.2k

Countries citing papers authored by Mood Mohan

Since Specialization
Citations

This map shows the geographic impact of Mood Mohan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mood Mohan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mood Mohan more than expected).

Fields of papers citing papers by Mood Mohan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mood Mohan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mood Mohan. The network helps show where Mood Mohan may publish in the future.

Co-authorship network of co-authors of Mood Mohan

This figure shows the co-authorship network connecting the top 25 collaborators of Mood Mohan. A scholar is included among the top collaborators of Mood Mohan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mood Mohan. Mood Mohan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mohan, Mood, et al.. (2025). Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation. ACS Sustainable Chemistry & Engineering. 13(48). 20737–20753.
2.
Mohan, Mood, et al.. (2025). Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids. Journal of Chemical Information and Modeling. 65(12). 5856–5867.
3.
Smith, Jeremy C., et al.. (2025). Molecular simulation and artificial intelligence for the circular economy of bioenergy and bioproducts. Biophysical Journal. 124(22). 3827–3852.
4.
Huang, Kaixuan, Kai Su, Mood Mohan, et al.. (2025). Research progress on organic acid pretreatment of lignocellulose. International Journal of Biological Macromolecules. 307(Pt 4). 142325–142325. 3 indexed citations
5.
Liu, Shih‐Hsien, Mood Mohan, Yan Yu, et al.. (2024). Molecular-level design of alternative media for energy-saving pilot-scale fibrillation of nanocellulose. Proceedings of the National Academy of Sciences. 121(37). e2405107121–e2405107121. 6 indexed citations
6.
Mohan, Mood, et al.. (2024). Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents. Journal of Chemical Theory and Computation. 20(9). 3911–3926. 35 indexed citations
7.
Yang, Shuang, Mood Mohan, Hiroyuki Yano, et al.. (2024). Multiscale investigation of the mechanism of biomass deconstruction in the dimethyl isosorbide/water Co-solvent pretreatment system. Green Chemistry. 26(8). 4758–4770. 6 indexed citations
8.
Mohan, Mood, Omar Demerdash, Blake A. Simmons, et al.. (2024). Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents. ACS Omega. 9(17). 19548–19559. 20 indexed citations
9.
Mohan, Mood, et al.. (2024). Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution. Green Chemical Engineering. 6(2). 275–287. 5 indexed citations
10.
Mohan, Mood, et al.. (2024). High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning. ACS Sustainable Chemistry & Engineering. 12(18). 7040–7054. 19 indexed citations
11.
Mohan, Mood, Micholas Dean Smith, Omar Demerdash, Michelle K. Kidder, & Jeremy C. Smith. (2023). Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning. The Journal of Chemical Physics. 158(21). 18 indexed citations
12.
Dou, Chang, Hemant Choudhary, Nawa Raj Baral, et al.. (2023). A hybrid chemical-biological approach can upcycle mixed plastic waste with reduced cost and carbon footprint. One Earth. 6(11). 1576–1590. 23 indexed citations
13.
Mohan, Mood, Micholas Dean Smith, Omar Demerdash, et al.. (2023). Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids. ACS Sustainable Chemistry & Engineering. 11(20). 7809–7821. 25 indexed citations
14.
Mohan, Mood, Omar Demerdash, Blake A. Simmons, et al.. (2023). Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network. Green Chemistry. 25(9). 3475–3492. 51 indexed citations
15.
Mohan, Mood, Blake A. Simmons, Kenneth L. Sale, & Seema Singh. (2023). Multiscale molecular simulations for the solvation of lignin in ionic liquids. Scientific Reports. 13(1). 271–271. 37 indexed citations
16.
Schieppati, Dalma, Mood Mohan, Bruno Blais, et al.. (2023). Characterization of the acoustic cavitation in ionic liquids in a horn-type ultrasound reactor. Ultrasonics Sonochemistry. 102. 106721–106721. 12 indexed citations
17.
18.
Mohan, Mood, Papu Kumar Naik, Tamal Banerjee, Vaibhav V. Goud, & Sandip Paul. (2017). Solubility of glucose in tetrabutylammonium bromide based deep eutectic solvents: Experimental and molecular dynamic simulations. Fluid Phase Equilibria. 448. 168–177. 72 indexed citations
19.
Mohan, Mood, Tamal Banerjee, & Vaibhav V. Goud. (2015). Hydrolysis of bamboo biomass by subcritical water treatment. Bioresource Technology. 191. 244–252. 85 indexed citations
20.
Mohan, Mood, et al.. (2010). Synthesis of low cost adhesives from pulp & paper industry waste. Journal of Scientific & Industrial Research. 69(5). 390–395. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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