Sukriti Singh

1.4k total citations
39 papers, 1.2k citations indexed

About

Sukriti Singh is a scholar working on Materials Chemistry, Organic Chemistry and Biomedical Engineering. According to data from OpenAlex, Sukriti Singh has authored 39 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Materials Chemistry, 20 papers in Organic Chemistry and 11 papers in Biomedical Engineering. Recurrent topics in Sukriti Singh's work include Polyoxometalates: Synthesis and Applications (14 papers), Chemical Synthesis and Reactions (11 papers) and Machine Learning in Materials Science (7 papers). Sukriti Singh is often cited by papers focused on Polyoxometalates: Synthesis and Applications (14 papers), Chemical Synthesis and Reactions (11 papers) and Machine Learning in Materials Science (7 papers). Sukriti Singh collaborates with scholars based in India, United Kingdom and United Arab Emirates. Sukriti Singh's co-authors include Anjali Patel, Raghavan B. Sunoj, Nilesh Narkhede, Bangaru Bhaskararao, Soyeb Pathan, Pritha Verma, Sayan Banerjee, P. Balamurugan, Monika Pareek and Debabrata Maiti and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Sukriti Singh

37 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sukriti Singh India 17 607 564 296 285 199 39 1.2k
Yoshihiro Hayashi Japan 18 504 0.8× 303 0.5× 157 0.5× 168 0.6× 84 0.4× 64 1.1k
Daniel Kopetzki Germany 11 366 0.6× 467 0.8× 264 0.9× 332 1.2× 68 0.3× 12 1.1k
Koya Prabhakara Rao India 19 343 0.6× 504 0.9× 651 2.2× 68 0.2× 117 0.6× 58 1.2k
Romaric Gérardy Belgium 11 349 0.6× 158 0.3× 147 0.5× 679 2.4× 137 0.7× 14 931
Felix Schäfer Germany 13 586 1.0× 248 0.4× 228 0.8× 76 0.3× 68 0.3× 18 943
Poonam Rani India 16 251 0.4× 241 0.4× 223 0.8× 86 0.3× 62 0.3× 39 595
Sascha Ceylan Germany 11 938 1.5× 300 0.5× 249 0.8× 1.2k 4.4× 118 0.6× 14 1.8k
Serkan Dayan Türkiye 18 492 0.8× 193 0.3× 263 0.9× 74 0.3× 24 0.1× 52 836
Zhenghui Wen Netherlands 12 355 0.6× 234 0.4× 58 0.2× 652 2.3× 74 0.4× 13 978

Countries citing papers authored by Sukriti Singh

Since Specialization
Citations

This map shows the geographic impact of Sukriti Singh'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 Sukriti Singh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sukriti Singh more than expected).

Fields of papers citing papers by Sukriti Singh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sukriti Singh. 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 Sukriti Singh. The network helps show where Sukriti Singh may publish in the future.

Co-authorship network of co-authors of Sukriti Singh

This figure shows the co-authorship network connecting the top 25 collaborators of Sukriti Singh. A scholar is included among the top collaborators of Sukriti Singh 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 Sukriti Singh. Sukriti Singh 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.
Singh, Sukriti & José Miguel Hernández-Lobato. (2025). A meta-learning approach for selectivity prediction in asymmetric catalysis. Nature Communications. 16(1). 3599–3599. 2 indexed citations
2.
Singh, Sukriti, Jyotsna Agarwal, Anupam Das, et al.. (2025). Leveraging molecular dynamics, physicochemical, and structural analysis to explore OMP33-36 protein as a drug target in Acinetobacter baumannii: An approach against nosocomial infection. Journal of Molecular Graphics and Modelling. 136. 108956–108956. 1 indexed citations
3.
Singh, Sukriti, et al.. (2025). An Evolutionary Attributes of OMP33-36 in Acinetobacter baumannii: In silico Based Analysis. Current Proteomics. 22. 1 indexed citations
4.
Singh, Sukriti, Sushmita Singh, Mala Trivedi, & Manish Dwivedi. (2024). An insight into MDR Acinetobacter baumannii infection and its pathogenesis: Potential therapeutic targets and challenges. Microbial Pathogenesis. 192. 106674–106674. 14 indexed citations
5.
Singh, Sukriti & José Miguel Hernández-Lobato. (2024). Deep Kernel learning for reaction outcome prediction and optimization. Communications Chemistry. 7(1). 136–136. 6 indexed citations
6.
Singh, Sukriti & José Miguel Hernández-Lobato. (2024). Data-Driven Insights into the Transition-Metal-Catalyzed Asymmetric Hydrogenation of Olefins. The Journal of Organic Chemistry. 89(17). 12467–12478. 4 indexed citations
7.
Sanyal, Somali, et al.. (2024). Pathophysiological and Clinical Potential of Human Microbiome: Microbe-based Therapeutic Insights. Current Pharmaceutical Biotechnology. 26(13). 2082–2096. 1 indexed citations
8.
Singh, Sukriti & Raghavan B. Sunoj. (2023). Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges. Accounts of Chemical Research. 56(3). 402–412. 48 indexed citations
9.
Singh, Sukriti & Raghavan B. Sunoj. (2022). A transfer learning approach for reaction discovery in small data situations using generative model. iScience. 25(7). 104661–104661. 9 indexed citations
10.
Singh, Sukriti, et al.. (2018). IOT-based Auto-Payment of Toll Tax. International Journal of Computer Applications. 179(42). 49–53. 2 indexed citations
11.
Deb, Arghya, Sukriti Singh, Kapileswar Seth, et al.. (2017). Experimental and Computational Studies on Remote γ-C(sp3)–H Silylation and Germanylation of Aliphatic Carboxamides. ACS Catalysis. 7(12). 8171–8175. 98 indexed citations
12.
Patel, Anjali & Sukriti Singh. (2016). Novel dilacunary phosphotungstate supported onto zirconia: Synthesis, characterization and versatile catalytic activity. Journal of the Taiwan Institute of Chemical Engineers. 64. 306–313. 8 indexed citations
13.
Khera, Neeraj, et al.. (2015). Remote Condition Monitoring of Real-Time Light Intensity and Temperature Data. 3–6. 8 indexed citations
14.
Singh, Sukriti & Anjali Patel. (2014). 12-Tungstophosphoric acid supported on mesoporous molecular material: synthesis, characterization and performance in biodiesel production. Journal of Cleaner Production. 72. 46–56. 72 indexed citations
15.
Costa, Elı́sio, Emı́lia Sousa, Sukriti Singh, et al.. (2014). Structure Based Design, Synthesis, and Evaluation of Potential Inhibitors of Steroid Sulfatase. Current Topics in Medicinal Chemistry. 14(8). 1033–1044. 9 indexed citations
16.
17.
Singh, Sukriti & Anjali Patel. (2014). Selective Green Esterification and Oxidation of Glycerol over 12-Tungstophosphoric Acid Anchored to MCM-48. Industrial & Engineering Chemistry Research. 53(38). 14592–14600. 32 indexed citations
18.
Narkhede, Nilesh, Anjali Patel, & Sukriti Singh. (2013). Mono lacunary phosphomolybdate supported on MCM-41: synthesis, characterization and solvent free aerobic oxidation of alkenes and alcohols. Dalton Transactions. 43(6). 2512–2520. 31 indexed citations
19.
20.
Kumar, Amit, et al.. (2011). Synthesis and Biological Evaluation of a Range of Thiosemicarbazone - Based Compounds as Potential Inhibitors of Estrone Sulfatase (ES). Letters in Drug Design & Discovery. 8(3). 241–245. 3 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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