Philipp M. Pflüger

740 citations
11 papers · 619 indexed · h-index 9
Topics
Catalytic C–H Functionalization Methods (6 papers)Machine Learning in Materials Science (5 papers)Computational Drug Discovery Methods (4 papers)

In The Last Decade

Philipp M. Pflüger

11 papers receiving 607 citations

Peers

Philipp M. Pflüger
Comparison fields: 5 of 36
  • Organic Chemistry 523
  • Inorganic Chemistry 99
  • Pharmaceutical Science 88
  • Materials Chemistry 47
  • Molecular Biology 34
Replace Stavros K. Kariofillis with:
Stavros K. Kariofillis United States
Jasper L. Tyler United Kingdom
Jacob A. Kautzky United States
Kathleen J. Berger United States
Julia C. Reisenbauer Switzerland
Dimitri F. J. Caputo United Kingdom
Yury Minko Israel
Jacob Werth United States
Christopher J. Evoniuk United States
Sanjun Zhi China
Philipp M. Pflüger relative to Stavros K. Kariofillis United States Stavros K. Kariofillis's profile →
Citations per field
00.5×2.9×
Stavros K. Kariofillis · 1×
Citations per year

Countries citing papers authored by Philipp M. Pflüger

Since Specialization
Citations

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

Fields of papers citing papers by Philipp M. Pflüger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philipp M. Pflüger

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

All Works

11 of 11 papers shown
#WorkIndexed citations
1 3
2 10
3 30
4 20
5 21
6 3
7 56
8 201
9 192
10 67
11 16

About Philipp M. Pflüger

Philipp M. Pflüger is a scholar working on Pharmaceutical Science, Computational Theory and Mathematics and Organic Chemistry, having authored 11 papers that have together received 619 indexed citations. Recurring topics across this work include Catalytic C–H Functionalization Methods (6 papers), Machine Learning in Materials Science (5 papers) and Computational Drug Discovery Methods (4 papers). The work is most often cited by research in Organic Chemistry (523 citations), Pharmaceutical Science (88 citations) and Inorganic Chemistry (99 citations). Philipp M. Pflüger has collaborated with scholars based in Germany, China and United States. Frequent co-authors include Frank Glorius, Huan‐Ming Huang, J. Luca Schwarz, Peter Bellotti, Bastian Heidrich, Maximilian Koy, Eloísa Serrano, Chaohuang Chen, Pinhong Chen and Guosheng Liu. Their work appears in journals such as Journal of the American Chemical Society, Angewandte Chemie International Edition and ACS Catalysis.

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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