David M. Ford

2.2k total citations
68 papers, 1.8k citations indexed

About

David M. Ford is a scholar working on Materials Chemistry, Mechanical Engineering and Biomedical Engineering. According to data from OpenAlex, David M. Ford has authored 68 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Materials Chemistry, 22 papers in Mechanical Engineering and 20 papers in Biomedical Engineering. Recurrent topics in David M. Ford's work include Material Dynamics and Properties (19 papers), Membrane Separation and Gas Transport (18 papers) and Phase Equilibria and Thermodynamics (15 papers). David M. Ford is often cited by papers focused on Material Dynamics and Properties (19 papers), Membrane Separation and Gas Transport (18 papers) and Phase Equilibria and Thermodynamics (15 papers). David M. Ford collaborates with scholars based in United States, United Kingdom and Russia. David M. Ford's co-authors include Eduardo D. Glandt, Grant S. Heffelfinger, Daniel F. Shantz, Eric E. Simanek, George W. Huber, Aidan P. Thompson, Paul C. Millett, Xianghong Qian, Raymond J. Gorte and H. Cordatos and has published in prestigious journals such as Journal of the American Chemical Society, The Journal of Chemical Physics and Applied Physics Letters.

In The Last Decade

David M. Ford

68 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David M. Ford United States 25 844 651 418 252 199 68 1.8k
You Qiang United States 28 1.4k 1.6× 605 0.9× 200 0.5× 161 0.6× 171 0.9× 86 2.6k
Aleksey Vishnyakov United States 15 958 1.1× 590 0.9× 445 1.1× 649 2.6× 76 0.4× 30 2.1k
Xinyu Liu China 25 1.1k 1.4× 526 0.8× 211 0.5× 213 0.8× 227 1.1× 151 2.7k
Li Xu China 23 1.1k 1.3× 309 0.5× 241 0.6× 134 0.5× 134 0.7× 115 2.2k
David J. Keffer United States 32 1.1k 1.3× 985 1.5× 351 0.8× 400 1.6× 76 0.4× 153 3.2k
Krzysztof Fitzner Poland 29 1.1k 1.3× 674 1.0× 793 1.9× 71 0.3× 128 0.6× 152 2.7k
Yang Gao China 26 1.8k 2.1× 656 1.0× 179 0.4× 253 1.0× 210 1.1× 124 3.2k
Neil A. Spenley United Kingdom 8 1.1k 1.3× 294 0.5× 228 0.5× 105 0.4× 91 0.5× 8 2.1k
Scott M. Auerbach United States 22 1.0k 1.2× 314 0.5× 197 0.5× 994 3.9× 48 0.2× 45 1.9k

Countries citing papers authored by David M. Ford

Since Specialization
Citations

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

Fields of papers citing papers by David M. Ford

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David M. Ford

This figure shows the co-authorship network connecting the top 25 collaborators of David M. Ford. A scholar is included among the top collaborators of David M. Ford 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 David M. Ford. David M. Ford 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.
Tang, Yuanhui, Yakai Lin, David M. Ford, et al.. (2021). A review on models and simulations of membrane formation via phase inversion processes. Journal of Membrane Science. 640. 119810–119810. 87 indexed citations
2.
Ford, David M., et al.. (2018). On the mechanical stability of the body-centered cubic phase and the emergence of a metastable cI16 phase in classical hard sphere solids. The Journal of Chemical Physics. 148(2). 24502–24502. 5 indexed citations
3.
Yang, Yuguang, Raghuram Thyagarajan, David M. Ford, & Michael A. Bevan. (2016). Dynamic colloidal assembly pathways via low dimensional models. The Journal of Chemical Physics. 144(20). 204904–204904. 16 indexed citations
4.
Verma, Anurag & David M. Ford. (2011). Universal features of the free-energy functional at the freezing transition for repulsive potentials. Physical Review E. 83(5). 51110–51110. 1 indexed citations
5.
Huber, George W., et al.. (2011). Separation of acetic acid from the aqueous fraction of fast pyrolysis bio-oils using nanofiltration and reverse osmosis membranes. Journal of Membrane Science. 378(1-2). 495–502. 90 indexed citations
6.
Verma, Anurag & David M. Ford. (2009). Closure-based perturbative density-functional theory of hard-sphere freezing: Properties of the bridge functional. Physical Review E. 80(3). 31109–31109. 3 indexed citations
7.
Ford, David M., et al.. (2008). Thermodynamic and mechanical properties of epoxy resin DGEBF crosslinked with DETDA by molecular dynamics. Journal of Molecular Graphics and Modelling. 26(8). 1269–1275. 83 indexed citations
8.
Ford, David M., et al.. (2008). Molecular simulation of permeation through alkyl-functionalized mesoporous ceramic membranes. Journal of Membrane Science. 314(1-2). 173–182. 9 indexed citations
9.
Bevan, Michael A., et al.. (2007). Closure-Based Density Functional Theory Applied to Interfacial Colloidal Fluids. Langmuir. 23(25). 12481–12488. 3 indexed citations
10.
Bevan, Michael A., et al.. (2007). Interfacial colloidal sedimentation equilibrium. II. Closure-based density functional theory. The Journal of Chemical Physics. 127(16). 13 indexed citations
11.
Ford, David M., Eric E. Simanek, & Daniel F. Shantz. (2005). Engineering nanospaces: ordered mesoporous silicas as model substrates for building complex hybrid materials. Nanotechnology. 16(7). S458–S475. 93 indexed citations
12.
Ford, David M.. (2005). Probing the Origins of Linear Free Energy Relationships with Molecular Theory and Simulation. Adsorption. 11(S1). 271–277. 4 indexed citations
13.
Ford, David M., et al.. (2004). Making the business case for process safety using value-at-risk concepts. Journal of Hazardous Materials. 115(1-3). 17–26. 11 indexed citations
14.
Ford, David M., et al.. (2000). Entropic and energetic selectivity in air separation with microporous materials. AIChE Journal. 46(1). 99–109. 14 indexed citations
15.
Thompson, Aidan P., David M. Ford, & Grant S. Heffelfinger. (1998). Direct molecular simulation of gradient-driven diffusion. The Journal of Chemical Physics. 109(15). 6406–6414. 69 indexed citations
16.
Faulon, Jean‐Loup, et al.. (1997). Massively parallel simulations of diffusion in dense polymeric structures. 1–11. 1 indexed citations
17.
Cordatos, H., David M. Ford, & Raymond J. Gorte. (1996). Simulated Annealing Study of the Structure and Reducibility in Ceria Clusters. The Journal of Physical Chemistry. 100(46). 18128–18132. 86 indexed citations
18.
Ford, David M. & Eduardo D. Glandt. (1995). Molecular Simulation Study of the Surface Barrier Effect. Dilute Gas Limit. The Journal of Physical Chemistry. 99(29). 11543–11549. 51 indexed citations
19.
Gelgor, Linda, David M. Ford, & Duncan Mitchell. (1988). Behavioural and thalamic nociceptive responses in rats following noxious ischaemia of the tail. Pain. 34(2). 205–211. 9 indexed citations
20.
Henderson, Robin, Eric Renshaw, & David M. Ford. (1984). A correlated random walk model for two-dimensional diffusion. Journal of Applied Probability. 21(2). 233–246. 12 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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