Daniel M. Kroll

1.3k total citations
21 papers, 1.1k citations indexed

About

Daniel M. Kroll is a scholar working on Molecular Biology, Materials Chemistry and Computational Mechanics. According to data from OpenAlex, Daniel M. Kroll has authored 21 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 7 papers in Materials Chemistry and 6 papers in Computational Mechanics. Recurrent topics in Daniel M. Kroll's work include Lattice Boltzmann Simulation Studies (5 papers), Heat and Mass Transfer in Porous Media (5 papers) and Lipid Membrane Structure and Behavior (5 papers). Daniel M. Kroll is often cited by papers focused on Lattice Boltzmann Simulation Studies (5 papers), Heat and Mass Transfer in Porous Media (5 papers) and Lipid Membrane Structure and Behavior (5 papers). Daniel M. Kroll collaborates with scholars based in United States, Germany and Slovenia. Daniel M. Kroll's co-authors include H. T. Davis, Robert S. Maier, Robert S. Bernard, Stacy E. Howington, John F. Peters, Mark R. Schure, Gerhard Gompper, Erkan Tüzel, Soumyendu Raha and Viera Lukáčová and has published in prestigious journals such as Physical Review Letters, The Journal of Chemical Physics and ACS Nano.

In The Last Decade

Daniel M. Kroll

21 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel M. Kroll United States 16 321 276 267 216 123 21 1.1k
Eugenia Corvera Poiré Mexico 15 189 0.6× 264 1.0× 231 0.9× 196 0.9× 10 0.1× 49 892
Laura Stingaciu United States 14 414 1.3× 121 0.4× 42 0.2× 161 0.7× 44 0.4× 41 983
Koichi Suzuki Japan 20 412 1.3× 99 0.4× 29 0.1× 440 2.0× 27 0.2× 64 1.3k
Akiyoshi Kuroda Japan 11 91 0.3× 89 0.3× 485 1.8× 114 0.5× 127 1.0× 44 1.1k
Victor Ovchinnikov United States 17 499 1.6× 39 0.1× 151 0.6× 155 0.7× 46 0.4× 33 829
Kang Kim Japan 21 333 1.0× 626 2.3× 123 0.5× 995 4.6× 14 0.1× 78 1.9k
David P. Hoogerheide United States 17 484 1.5× 632 2.3× 175 0.7× 117 0.5× 26 0.2× 46 1.1k
Pascal Théveneau France 16 538 1.7× 256 0.9× 12 0.0× 357 1.7× 237 1.9× 25 1.4k
Erik C. Yusko United States 11 628 2.0× 1.4k 5.1× 321 1.2× 167 0.8× 28 0.2× 16 1.7k
Eiji Yamamoto Japan 21 475 1.5× 349 1.3× 26 0.1× 266 1.2× 19 0.2× 93 1.3k

Countries citing papers authored by Daniel M. Kroll

Since Specialization
Citations

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

Fields of papers citing papers by Daniel M. Kroll

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel M. Kroll

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel M. Kroll. A scholar is included among the top collaborators of Daniel M. Kroll 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 Daniel M. Kroll. Daniel M. Kroll 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.
Miller, Joseph B., Naveen K. Dandu, Kirill A. Velizhanin, et al.. (2015). Enhanced Luminescent Stability through Particle Interactions in Silicon Nanocrystal Aggregates. ACS Nano. 9(10). 9772–9782. 34 indexed citations
2.
Fošnarič, Miha, Aleš Iglič, Daniel M. Kroll, & Sylvio May. (2013). Monte Carlo simulations of a polymer confined within a fluid vesicle. Soft Matter. 9(15). 3976–3976. 32 indexed citations
3.
Peltomäki, Matti, Gerhard Gompper, & Daniel M. Kroll. (2012). Scattering intensity of bicontinuous microemulsions and sponge phases. The Journal of Chemical Physics. 136(13). 134708–134708. 28 indexed citations
4.
Miller, Joseph B., et al.. (2012). Ensemble Brightening and Enhanced Quantum Yield in Size-Purified Silicon Nanocrystals. ACS Nano. 6(8). 7389–7396. 88 indexed citations
5.
Tüzel, Erkan, et al.. (2010). Dynamics of thermally driven capillary waves for two-dimensional droplets. The Journal of Chemical Physics. 132(17). 174701–174701. 11 indexed citations
6.
Fošnarič, Miha, Aleš Iglič, Daniel M. Kroll, & Sylvio May. (2009). Monte Carlo simulations of complex formation between a mixed fluid vesicle and a charged colloid. The Journal of Chemical Physics. 131(10). 43 indexed citations
7.
Tüzel, Erkan, et al.. (2009). Anterograde Microtubule Transport Drives Microtubule Bending in LLC-PK1 Epithelial Cells. Molecular Biology of the Cell. 20(12). 2943–2953. 77 indexed citations
8.
Maier, Robert S., et al.. (2008). Diameter‐dependent dispersion in cylindrical bead packs. AIChE Journal. 54(8). 2024–2028. 10 indexed citations
9.
Kroll, Daniel M., et al.. (2007). Three-Dimensional Modeling of the Brain's ECS by Minimum Configurational Energy Packing of Fluid Vesicles. Biophysical Journal. 92(10). 3368–3378. 15 indexed citations
10.
Tüzel, Erkan, et al.. (2007). Analysis of Microtubule Curvature. Methods in cell biology. 83. 237–268. 38 indexed citations
11.
Tüzel, Erkan, Thomas Ihle, & Daniel M. Kroll. (2006). Constructing thermodynamically consistent models with a non-ideal equation of state. Mathematics and Computers in Simulation. 72(2-6). 232–236. 9 indexed citations
12.
Khandelwal, Akash, et al.. (2005). A Combination of Docking, QM/MM Methods, and MD Simulation for Binding Affinity Estimation of Metalloprotein Ligands. Journal of Medicinal Chemistry. 48(17). 5437–5447. 127 indexed citations
13.
Lukáčová, Viera, et al.. (2005). A Comparison of the Binding Sites of Matrix Metalloproteinases and Tumor Necrosis Factor-α Converting Enzyme:  Implications for Selectivity. Journal of Medicinal Chemistry. 48(7). 2361–2370. 23 indexed citations
14.
Schure, Mark R., Robert S. Maier, Daniel M. Kroll, & H. T. Davis. (2004). Simulation of ordered packed beds in chromatography. Journal of Chromatography A. 1031(1-2). 79–86. 85 indexed citations
15.
Khandelwal, Akash, et al.. (2004). Simulation‐Based Predictions of Binding Affinities of Matrix Metalloproteinase Inhibitors. QSAR & Combinatorial Science. 23(9). 754–766. 7 indexed citations
16.
Döbereiner, Jürgen, et al.. (2003). Advanced Flicker Spectroscopy of Fluid Membranes. Physical Review Letters. 91(4). 48301–48301. 57 indexed citations
17.
Schure, Mark R., Robert S. Maier, Daniel M. Kroll, & H. T. Davis. (2002). Simulation of Packed-Bed Chromatography Utilizing High-Resolution Flow Fields:  Comparison with Models. Analytical Chemistry. 74(23). 6006–6016. 56 indexed citations
18.
Maier, Robert S., Daniel M. Kroll, Robert S. Bernard, et al.. (2000). Pore-scale simulation of dispersion. Physics of Fluids. 12(8). 2065–2079. 182 indexed citations
19.
Maier, Robert S., Daniel M. Kroll, H. T. Davis, & Robert S. Bernard. (1999). Simulation of Flow in Bidisperse Sphere Packings. Journal of Colloid and Interface Science. 217(2). 341–347. 46 indexed citations
20.
Gompper, Gerhard & Daniel M. Kroll. (1997). Fluctuations of polymerized, fluid and hexatic membranes: Continuum models and simulations. Current Opinion in Colloid & Interface Science. 2(4). 373–381. 18 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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