Giovanni M. Pavan

6.5k total citations · 1 hit paper
145 papers, 5.1k citations indexed

About

Giovanni M. Pavan is a scholar working on Organic Chemistry, Molecular Biology and Biomaterials. According to data from OpenAlex, Giovanni M. Pavan has authored 145 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Organic Chemistry, 62 papers in Molecular Biology and 50 papers in Biomaterials. Recurrent topics in Giovanni M. Pavan's work include Supramolecular Self-Assembly in Materials (50 papers), Dendrimers and Hyperbranched Polymers (45 papers) and RNA Interference and Gene Delivery (35 papers). Giovanni M. Pavan is often cited by papers focused on Supramolecular Self-Assembly in Materials (50 papers), Dendrimers and Hyperbranched Polymers (45 papers) and RNA Interference and Gene Delivery (35 papers). Giovanni M. Pavan collaborates with scholars based in Switzerland, Italy and United States. Giovanni M. Pavan's co-authors include Davide Bochicchio, Andrea Danani, Claudio Perego, Luca Pesce, Lorenzo Albertazzi, Matteo Garzoni, Eric E. Simanek, Sabrina Pricl, E. W. Meijer and Riccardo Capelli and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Giovanni M. Pavan

139 papers receiving 5.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Electrostatic co-assembly of nanoparticles with oppositely charged small molecules into static and dynamic superstructures

2021 206 citations Claudio Perego, Giovanni M. Pavan et al. profile →

Peers

Giovanni M. Pavan

BIOMA

Dietmar Appelhans Germany

Robert L. Harniman United Kingdom

Eric E. Simanek United States

Kazushi Kinbara Japan

Jeffery G. Saven United States

Bohdana M. Discher United States

Pol Besenius Germany

Paul S. Russo United States

Job Boekhoven Germany

Gustavo Fernández Germany

Dietmar Appelhans Germany

Giovanni M. Pavan

1.7k ×0.8

2.6k ×1.4

1.4k ×0.8

BIOMA

985 ×0.5

1.9k ×1.5

Citations per year, relative to Giovanni M. Pavan Giovanni M. Pavan (= 1×) peers Dietmar Appelhans

Countries citing papers authored by Giovanni M. Pavan

Since Specialization

Citations

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

Fields of papers citing papers by Giovanni M. Pavan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Giovanni M. Pavan. 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 Giovanni M. Pavan. The network helps show where Giovanni M. Pavan may publish in the future.

Co-authorship network of co-authors of Giovanni M. Pavan

This figure shows the co-authorship network connecting the top 25 collaborators of Giovanni M. Pavan. A scholar is included among the top collaborators of Giovanni M. Pavan 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 Giovanni M. Pavan. Giovanni M. Pavan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Perego, Claudio, et al.. (2025). Non-trivial stimuli-responsive collective behaviours emerging from microscopic dynamic complexity in supramolecular polymer systems. Nature Communications. 16(1). 5030–5030. 1 indexed citations

Cardellini, Annalisa, Cristina Caruso, Laura Rijns, et al.. (2025). Monomer exchange dynamics in ureido–pyrimidinone supramolecular polymers via molecular simulations. Journal of Materials Chemistry B. 13(44). 14326–14337.

Cardellini, Annalisa, et al.. (2025). Controlling the Assembly of Ureido‐Pyrimidinone Supramolecular Monomers by Tuning the Hydrophilic‐Hydrophobic Balance. Journal of Polymer Science. 64(3). 682–690.

Pavan, Giovanni M., et al.. (2025). A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information. Machine Learning Science and Technology. 6(3). 35039–35039. 1 indexed citations

Pavan, Giovanni M., et al.. (2024). pH-dependent conformational transitions in ferulic acid and Nrf2-ferulic acid complex analyzed through spectroscopy and molecular dynamics simulations. Journal of Molecular Liquids. 418. 126703–126703. 6 indexed citations

Pavan, Giovanni M., et al.. (2024). Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems. Proceedings of the National Academy of Sciences. 121(33). e2403771121–e2403771121. 12 indexed citations

Piane, Massimo Delle, et al.. (2024). Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning. Advanced Science. 11(25). e2307261–e2307261. 10 indexed citations

Fadaei‐Tirani, Farzaneh, et al.. (2024). Nano onions based on an amphiphilic Au ₃ (pyrazolate) ₃ complex. Nanoscale. 17(2). 1007–1012. 1 indexed citations

Parella, Teodor, et al.. (2023). Synthesis of C₆₀/[10]CPP‐Catenanes by Regioselective, Nanocapsule‐Templated Bingel Bis‐Addition. Angewandte Chemie International Edition. 62(42). e202309393–e202309393. 24 indexed citations

10.

Insua, Ignacio, Annalisa Cardellini, Julián Bergueiro, et al.. (2023). Self-assembly of cyclic peptide monolayers by hydrophobic supramolecular hinges. Chemical Science. 14(48). 14074–14081. 15 indexed citations

11.

Polino, Daniela, et al.. (2023). Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments. The Journal of Chemical Physics. 158(12). 124701–124701. 20 indexed citations

12.

Piane, Massimo Delle, et al.. (2023). Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles. Communications Chemistry. 6(1). 143–143. 15 indexed citations

13.

Uchida, Noriyuki, Kou Okuro, Annalisa Cardellini, et al.. (2022). Reconstitution of microtubule into GTP-responsive nanocapsules. Nature Communications. 13(1). 5424–5424. 9 indexed citations

14.

Pesce, Luca, Nobutaka Shimizu, Hideaki Takagi, et al.. (2021). Non‐uniform Photoinduced Unfolding of Supramolecular Polymers Leading to Topological Block Nanofibers. Angewandte Chemie International Edition. 60(52). 26986–26993. 21 indexed citations

15.

Empereur‐mot, Charly, Luca Pesce, Davide Bochicchio, et al.. (2020). Swarm-CG : Automatic Parametrization of Bonded Terms in MARTINI-Based Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization. ACS Omega. 5(50). 32823–32843. 52 indexed citations

16.

Varela‐Aramburu, Silvia, Giulia Morgese, Lu Su, et al.. (2020). Exploring the Potential of Benzene-1,3,5-tricarboxamide Supramolecular Polymers as Biomaterials. Biomacromolecules. 21(10). 4105–4115. 29 indexed citations

17.

Datta, Sougata, Keisuke Aratsu, Atsushi Isobe, et al.. (2020). Self-assembled poly-catenanes from supramolecular toroidal building blocks. Nature. 583(7816). 400–405. 248 indexed citations

18.

Beyeh, Ngong Kodiah, Nonappa Nonappa, Ville Liljeström, et al.. (2018). Crystalline Cyclophane–Protein Cage Frameworks. ACS Nano. 12(8). 8029–8036. 38 indexed citations

19.

Pavan, Giovanni M., et al.. (2015). Development and validation of RP-HPLC method for quantitativedetermination of imatinib mesylate in bulk drug and pharmaceutical dosageform. Der pharmacia lettre. 7(7). 102–112. 5 indexed citations

20.

Woodman, Scott E., Jonathan C. Trent, Katherine Stemke‐Hale, et al.. (2009). Activity of dasatinib against L576P KIT mutant melanoma: Molecular, cellular, and clinical correlates. Molecular Cancer Therapeutics. 8(8). 2079–2085. 141 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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