Tim Würger

536 total citations
19 papers, 420 citations indexed

About

Tim Würger is a scholar working on Materials Chemistry, Biomaterials and Metals and Alloys. According to data from OpenAlex, Tim Würger has authored 19 papers receiving a total of 420 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Materials Chemistry, 6 papers in Biomaterials and 5 papers in Metals and Alloys. Recurrent topics in Tim Würger's work include Corrosion Behavior and Inhibition (13 papers), Machine Learning in Materials Science (7 papers) and Magnesium Alloys: Properties and Applications (6 papers). Tim Würger is often cited by papers focused on Corrosion Behavior and Inhibition (13 papers), Machine Learning in Materials Science (7 papers) and Magnesium Alloys: Properties and Applications (6 papers). Tim Würger collaborates with scholars based in Germany, Australia and China. Tim Würger's co-authors include Mikhail L. Zheludkevich, Robert H. Meißner, Christian Feiler, Sviatlana V. Lamaka, Gregor B. Vonbun‐Feldbauer, David A. Winkler, Bahram Vaghefinazari, Daniel Höche, J.M.C. Mol and Di Mei and has published in prestigious journals such as Advanced Energy Materials, Scientific Reports and The Journal of Physical Chemistry C.

In The Last Decade

Tim Würger

19 papers receiving 399 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tim Würger Germany 12 350 127 90 69 67 19 420
Christian Feiler Germany 14 452 1.3× 211 1.7× 117 1.3× 118 1.7× 71 1.1× 26 573
N.H. Helal Egypt 11 297 0.8× 40 0.3× 82 0.9× 132 1.9× 70 1.0× 20 403
M. Mokaddem France 10 288 0.8× 39 0.3× 87 1.0× 84 1.2× 92 1.4× 13 498
E. A. Matter Egypt 8 279 0.8× 28 0.2× 42 0.5× 104 1.5× 94 1.4× 15 331
X.Y. Wang China 9 349 1.0× 26 0.2× 43 0.5× 154 2.2× 23 0.3× 15 542
Tian Xiao China 11 284 0.8× 60 0.5× 16 0.2× 64 0.9× 6 0.1× 34 345
Song He China 11 188 0.5× 15 0.1× 32 0.4× 125 1.8× 15 0.2× 22 328
Keun-Woo Cho South Korea 4 362 1.0× 13 0.1× 22 0.2× 94 1.4× 26 0.4× 5 402
Lixin Meng China 13 243 0.7× 12 0.1× 36 0.4× 104 1.5× 13 0.2× 34 461

Countries citing papers authored by Tim Würger

Since Specialization
Citations

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

Fields of papers citing papers by Tim Würger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tim Würger

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

All Works

19 of 19 papers shown
1.
Wu, Yulong, Darya Snihirova, Tim Würger, et al.. (2025). Machine learning-guided discovery of high-efficiency electrolyte additives for aqueous magnesium-air batteries. Energy storage materials. 76. 104120–104120. 9 indexed citations
2.
Würger, Tim, Christian Feiler, Sviatlana V. Lamaka, et al.. (2025). Gaining scientific understanding with small data machine learning: explainable molecule representations and their consensus. npj Materials Degradation. 9(1). 1 indexed citations
3.
Würger, Tim, et al.. (2024). Influence of Simple Salts on Solvent Reduction Stability at Mg‐Alloy Anodes Interface: A Potential‐Dependent DFT Study. Advanced Energy Materials. 14(42). 1 indexed citations
4.
Winkler, David A., A.E. Hughés, J.M.C. Mol, et al.. (2024). Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors. Progress in Materials Science. 149. 101392–101392. 29 indexed citations
5.
Lu, Xiaopeng, et al.. (2024). New insights into the inhibition mechanism of carboxylate species on magnesium surface. Corrosion Science. 232. 112009–112009. 11 indexed citations
6.
Vaghefinazari, Bahram, et al.. (2023). Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques. npj Materials Degradation. 7(1). 10 indexed citations
7.
Würger, Tim, Bahram Vaghefinazari, Sviatlana V. Lamaka, et al.. (2023). Searching the chemical space for effective magnesium dissolution modulators: a deep learning approach using sparse features. npj Materials Degradation. 7(1). 5 indexed citations
8.
Li, Xiaojing, Tim Würger, Christian Feiler, et al.. (2022). Atomistic Insight into the Hydration States of Layered Double Hydroxides. ACS Omega. 7(14). 12412–12423. 15 indexed citations
9.
Würger, Tim, Linqian Wang, Darya Snihirova, et al.. (2022). Data-driven selection of electrolyte additives for aqueous magnesium batteries. Journal of Materials Chemistry A. 10(40). 21672–21682. 14 indexed citations
10.
Würger, Tim, Di Mei, Bahram Vaghefinazari, et al.. (2021). Exploring structure-property relationships in magnesium dissolution modulators. npj Materials Degradation. 5(1). 23 indexed citations
11.
Würger, Tim, Sviatlana V. Lamaka, Robert H. Meißner, et al.. (2021). Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models. npj Computational Materials. 7(1). 32 indexed citations
12.
Zeller‐Plumhoff, Berit, et al.. (2021). Exploring key ionic interactions for magnesium degradation in simulated body fluid – A data-driven approach. Corrosion Science. 182. 109272–109272. 29 indexed citations
13.
Feiler, Christian, et al.. (2021). Mechanical degradation estimation of thermosets by peak shift assessment: General approach using infrared spectroscopy. Polymer. 221. 123585–123585. 6 indexed citations
14.
Würger, Tim, Christian Feiler, Gregor B. Vonbun‐Feldbauer, Mikhail L. Zheludkevich, & Robert H. Meißner. (2020). A first-principles analysis of the charge transfer in magnesium corrosion. Scientific Reports. 10(1). 15006–15006. 56 indexed citations
15.
Fockaert, Laura-Lynn, Tim Würger, B. Boelen, et al.. (2020). ATR-FTIR in Kretschmann configuration integrated with electrochemical cell as in situ interfacial sensitive tool to study corrosion inhibitors for magnesium substrates. Electrochimica Acta. 345. 136166–136166. 48 indexed citations
16.
Feiler, Christian, Di Mei, Bahram Vaghefinazari, et al.. (2019). In silico screening of modulators of magnesium dissolution. Corrosion Science. 163. 108245–108245. 50 indexed citations
17.
Würger, Tim, Christian Feiler, Félix Musil, et al.. (2019). Data Science Based Mg Corrosion Engineering. Frontiers in Materials. 6. 38 indexed citations
18.
Würger, Tim, Stefan Müller, Andreas Stierle, et al.. (2018). Adsorption of Acetone on Rutile TiO2: A DFT and FTIRS Study. The Journal of Physical Chemistry C. 122(34). 19481–19490. 28 indexed citations
19.
Würger, Tim, et al.. (2017). Van der Waals Interaction Really Matters: Energetics of Benzoic Acid on TiO2 Rutile Surfaces. The Journal of Physical Chemistry C. 121(32). 17207–17214. 15 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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