Evan Shave

494 total citations
20 papers, 405 citations indexed

About

Evan Shave is a scholar working on Molecular Biology, Biomedical Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Evan Shave has authored 20 papers receiving a total of 405 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 11 papers in Biomedical Engineering and 7 papers in Electrical and Electronic Engineering. Recurrent topics in Evan Shave's work include Viral Infectious Diseases and Gene Expression in Insects (10 papers), Microfluidic and Capillary Electrophoresis Applications (9 papers) and Protein purification and stability (9 papers). Evan Shave is often cited by papers focused on Viral Infectious Diseases and Gene Expression in Insects (10 papers), Microfluidic and Capillary Electrophoresis Applications (9 papers) and Protein purification and stability (9 papers). Evan Shave collaborates with scholars based in United States, Australia and Denmark. Evan Shave's co-authors include Gyula Vigh, Linda H.L. Lua, Stephen M. Mahler, Trent P. Munro, Verónica S. Martínez, Esteban Marcellin, Lars K. Nielsen, M. A. MacDonald, Samuel G. Franklin and D.B. Rylatt and has published in prestigious journals such as Journal of Chromatography A, Biotechnology and Bioengineering and Electrophoresis.

In The Last Decade

Evan Shave

20 papers receiving 398 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Evan Shave United States 12 241 212 87 54 50 20 405
Elisabeth Wenisch Italy 15 372 1.5× 260 1.2× 114 1.3× 95 1.8× 166 3.3× 24 601
Hasin Feroz United States 11 175 0.7× 176 0.8× 35 0.4× 58 1.1× 15 0.3× 22 380
Pierre Girot France 11 247 1.0× 85 0.4× 137 1.6× 16 0.3× 97 1.9× 25 332
Kelly Wiltberger United States 11 379 1.6× 133 0.6× 49 0.6× 16 0.3× 13 0.3× 12 460
Duncan R. Purvis United Kingdom 9 286 1.2× 83 0.4× 58 0.7× 25 0.5× 41 0.8× 10 353
Shishir D. Gadam United States 7 273 1.1× 132 0.6× 88 1.0× 21 0.4× 155 3.1× 8 375
Keisuke Fukunaga Japan 12 293 1.2× 56 0.3× 54 0.6× 18 0.3× 26 0.5× 27 381
Inês F. Pinto Portugal 15 283 1.2× 345 1.6× 72 0.8× 46 0.9× 15 0.3× 27 507
Christof Finkler Switzerland 11 363 1.5× 149 0.7× 201 2.3× 5 0.1× 40 0.8× 16 451
Rachael A. Lewus United States 9 339 1.4× 122 0.6× 170 2.0× 16 0.3× 11 0.2× 9 401

Countries citing papers authored by Evan Shave

Since Specialization
Citations

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

Fields of papers citing papers by Evan Shave

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Evan Shave

This figure shows the co-authorship network connecting the top 25 collaborators of Evan Shave. A scholar is included among the top collaborators of Evan Shave 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 Evan Shave. Evan Shave 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.
Martínez, Verónica S., Tim McCubbin, Junjie Tong, et al.. (2024). Amino acid degradation pathway inhibitory by‐products trigger apoptosis in CHO cells. Biotechnology Journal. 19(2). e2300338–e2300338. 9 indexed citations
2.
MacDonald, M. A., Evan Shave, Stephen M. Mahler, et al.. (2023). Harnessing metabolic plasticity in CHO cells for enhanced perfusion cultivation. Biotechnology and Bioengineering. 121(4). 1370–1382. 2 indexed citations
3.
MacDonald, M. A., Verónica S. Martínez, Evan Shave, et al.. (2022). Engineering death resistance in CHO cells for improved perfusion culture. mAbs. 14(1). 2083465–2083465. 13 indexed citations
4.
Billakanti, Jagan, et al.. (2022). Design and optimization of membrane chromatography for monoclonal antibody charge variant separation. Biotechnology Progress. 38(6). e3288–e3288. 8 indexed citations
5.
MacDonald, M. A., Verónica S. Martínez, Peter P. Gray, et al.. (2022). Modeling apoptosis resistance in CHO cells with CRISPR‐mediated knockouts of Bak1, Bax, and Bok. Biotechnology and Bioengineering. 119(6). 1380–1391. 21 indexed citations
6.
MacDonald, M. A., Verónica S. Martínez, Benjamin L. Schulz, et al.. (2021). Perfusion culture of Chinese Hamster Ovary cells for bioprocessing applications. Critical Reviews in Biotechnology. 42(7). 1099–1115. 31 indexed citations
7.
Shooter, Gary K., et al.. (2020). Intensified Downstream Processing of Monoclonal Antibodies Using Membrane Technology. Biotechnology Journal. 16(3). e2000309–e2000309. 31 indexed citations
8.
Shave, Evan, et al.. (2019). Evaluation of process simulation as a decisional tool for biopharmaceutical contract development and manufacturing organizations. Biochemical Engineering Journal. 150. 107252–107252. 14 indexed citations
9.
Shave, Evan, et al.. (2018). Progression of continuous downstream processing of monoclonal antibodies: Current trends and challenges. Biotechnology and Bioengineering. 115(12). 2893–2907. 96 indexed citations
10.
Shave, Evan & Gyula Vigh. (2007). Use of a preparative-scale, recirculating isoelectric trapping device for the isolation and enrichment of acidic proteins in bovine serum. Journal of Chromatography A. 1155(2). 237–241. 2 indexed citations
11.
Shave, Evan & Gyula Vigh. (2007). The Biflow: An instrument for transfer‐loop mediated, continuous, preparative‐scale isoelectric trapping separations. Electrophoresis. 28(13). 2291–2299. 11 indexed citations
12.
Shave, Evan & Gyula Vigh. (2007). pH transients during salt removal in isoelectric trapping separations: A curse revisited. Electrophoresis. 28(4). 587–594. 6 indexed citations
13.
14.
Shave, Evan, et al.. (2004). Alkali‐stable high‐pI isoelectric membranes for isoelectric trapping separations. Electrophoresis. 25(14). 2128–2138. 22 indexed citations
15.
Shave, Evan, et al.. (2004). High‐buffering capacity, hydrolytically stable, low‐pI isoelectric membranes for isoelectric trapping separations. Electrophoresis. 25(20). 3323–3330. 19 indexed citations
16.
Shave, Evan & Gyula Vigh. (2004). Preparative‐scale, recirculating, pH‐biased binary isoelectric trapping separations. Electrophoresis. 25(2). 381–387. 27 indexed citations
17.
Shave, Evan & Gyula Vigh. (2003). Preparative-scale isoelectric trapping separations in methanol–water mixtures. Journal of Chromatography A. 1036(1). 3–6. 5 indexed citations
18.
Shave, Evan & Gyula Vigh. (2003). Preparative-scale isoelectric trapping enantiomer separations. Journal of Chromatography A. 989(1). 73–78. 19 indexed citations
19.
Rylatt, Dennis B., et al.. (2002). Preparative-scale isoelectric trapping separations using a modified Gradiflow unit. Journal of Chromatography A. 979(1-2). 155–161. 35 indexed citations
20.
Shave, Evan, et al.. (2002). Preparative electrophoresis: a general method for the purification of polyclonal antibodies. Journal of Chromatography A. 944(1-2). 161–168. 29 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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