Dean C. Ripple

1.2k total citations
67 papers, 846 citations indexed

About

Dean C. Ripple is a scholar working on Biomedical Engineering, Aerospace Engineering and Molecular Biology. According to data from OpenAlex, Dean C. Ripple has authored 67 papers receiving a total of 846 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Biomedical Engineering, 21 papers in Aerospace Engineering and 16 papers in Molecular Biology. Recurrent topics in Dean C. Ripple's work include Calibration and Measurement Techniques (21 papers), Advanced Sensor Technologies Research (16 papers) and Protein purification and stability (15 papers). Dean C. Ripple is often cited by papers focused on Calibration and Measurement Techniques (21 papers), Advanced Sensor Technologies Research (16 papers) and Protein purification and stability (15 papers). Dean C. Ripple collaborates with scholars based in United States, Egypt and China. Dean C. Ripple's co-authors include Mariana N. Dimitrova, George W. Burns, Michael R. Moldover, M. Battuello, A. Skumanich, Dana R. Defibaugh, Richard E. Cavicchi, Nabil M. Amer, G. F. Strouse and Jason King and has published in prestigious journals such as The Journal of Chemical Physics, Physical review. B, Condensed matter and Applied Physics Letters.

In The Last Decade

Dean C. Ripple

61 papers receiving 803 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dean C. Ripple United States 17 437 333 196 150 124 67 846
Young‐Ho Park South Korea 15 114 0.3× 251 0.8× 71 0.4× 13 0.1× 10 0.1× 87 928
A. Franks United Kingdom 12 227 0.5× 87 0.3× 15 0.1× 51 0.3× 23 0.2× 56 708
Huihui Wang China 20 156 0.4× 187 0.6× 173 0.9× 6 0.0× 15 0.1× 75 1.1k
Uwe Ewert Germany 15 291 0.7× 32 0.1× 31 0.2× 12 0.1× 245 2.0× 111 994
Santosh Kumar Paidi United States 19 371 0.8× 354 1.1× 34 0.2× 4 0.0× 46 0.4× 32 994
Thomas Kruse Germany 11 63 0.1× 73 0.2× 44 0.2× 10 0.1× 29 0.2× 37 483
Masaki Sato Japan 21 107 0.2× 90 0.3× 129 0.7× 16 0.1× 8 0.1× 91 1.4k
A. George France 10 121 0.3× 405 1.2× 117 0.6× 3 0.0× 65 0.5× 27 985
Chunqi Jiang United States 23 195 0.4× 95 0.3× 171 0.9× 9 0.1× 911 7.3× 94 1.5k
Harbans S. Dhadwal United States 14 174 0.4× 38 0.1× 29 0.1× 6 0.0× 65 0.5× 56 504

Countries citing papers authored by Dean C. Ripple

Since Specialization
Citations

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

Fields of papers citing papers by Dean C. Ripple

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dean C. Ripple. 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 Dean C. Ripple. The network helps show where Dean C. Ripple may publish in the future.

Co-authorship network of co-authors of Dean C. Ripple

This figure shows the co-authorship network connecting the top 25 collaborators of Dean C. Ripple. A scholar is included among the top collaborators of Dean C. Ripple 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 Dean C. Ripple. Dean C. Ripple 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, Xinyu, Fei Guo, Carlito B. Lebrilla, et al.. (2025). Analysis of TEM micrographs with deep learning reveals APOE genotype-specific associations between HDL particle diameter and Alzheimer’s dementia. Cell Reports Methods. 5(1). 100962–100962.
2.
Telikepalli, Srivalli, Michael J. Carrier, Dean C. Ripple, et al.. (2025). Root cause analysis investigation of visible particulates in therapeutic protein drug products using morphologically directed Raman spectroscopy. Scientific Reports. 15(1). 42026–42026.
3.
4.
Kim, Minkyung, Charudharshini Srinivasan, Thomas O’Connor, et al.. (2023). Morphologically-Directed Raman Spectroscopy as an Analytical Method for Subvisible Particle Characterization in Therapeutic Protein Product Quality. Scientific Reports. 13(1). 20473–20473. 3 indexed citations
5.
DeRose, Paul C., Kurt D. Benkstein, Elzafir Elsheikh, et al.. (2022). Number Concentration Measurements of Polystyrene Submicrometer Particles. Nanomaterials. 12(18). 3118–3118. 8 indexed citations
6.
Telikepalli, Srivalli, Michael J. Carrier, Dean C. Ripple, et al.. (2022). An Interlaboratory Study to Identify Potential Visible Protein-Like Particle Standards. AAPS PharmSciTech. 24(1). 18–18. 6 indexed citations
7.
Mathaes, Roman, Linda O. Narhi, Andrea Hawe, et al.. (2019). Phase-Appropriate Application of Analytical Methods to Monitor Subvisible Particles Across the Biotherapeutic Drug Product Life Cycle. The AAPS Journal. 22(1). 1–1. 23 indexed citations
8.
Cavicchi, Richard E. & Dean C. Ripple. (2019). Improving Diameter Accuracy for Dynamic Imaging Microscopy for Different Particle Types. Journal of Pharmaceutical Sciences. 109(1). 488–495. 4 indexed citations
9.
Telikepalli, Srivalli, et al.. (2019). Development of Protein-Like Reference Material for Semiquantitatively Monitoring Visible Proteinaceous Particles in Biopharmaceuticals. PDA Journal of Pharmaceutical Science and Technology. 73(5). 418–432. 10 indexed citations
10.
Cavicchi, Richard E., Jason King, & Dean C. Ripple. (2018). Measurement of Average Aggregate Density by Sedimentation and Brownian Motion Analysis. Journal of Pharmaceutical Sciences. 107(5). 1304–1312. 19 indexed citations
11.
Telikepalli, Srivalli, Jason King, N. Alan Heckert, et al.. (2018). Development of orthogonal NISTmAb size heterogeneity control methods. Analytical and Bioanalytical Chemistry. 410(8). 2095–2110. 27 indexed citations
12.
Defante, Adrian P., Wyatt N. Vreeland, Kurt D. Benkstein, & Dean C. Ripple. (2017). Using Image Attributes to Assure Accurate Particle Size and Count Using Nanoparticle Tracking Analysis. Journal of Pharmaceutical Sciences. 107(5). 1383–1391. 21 indexed citations
13.
Cao, Shawn, Andrea Hawe, Desmond Hunt, et al.. (2016). Analytical Gaps and Challenges for Particles in the Submicrometer Size Domain | NIST. 42(6). 3 indexed citations
14.
Vreeland, Wyatt N., et al.. (2015). Using light scattering to evaluate the separation of polydisperse nanoparticles. Analytica Chimica Acta. 886. 207–213. 9 indexed citations
15.
Ripple, Dean C., et al.. (2014). The Use of Index-Matched Beads in Optical Particle Counters. Journal of Research of the National Institute of Standards and Technology. 119. 644–644. 22 indexed citations
16.
Cavicchi, Richard E., et al.. (2014). Particle Shape Effects on Subvisible Particle Sizing Measurements. Journal of Pharmaceutical Sciences. 104(3). 971–987. 23 indexed citations
17.
Ripple, Dean C., et al.. (2013). Room temperature acoustic transducers for high-temperature thermometry. AIP conference proceedings. 44–49. 4 indexed citations
18.
Ripple, Dean C. & Mariana N. Dimitrova. (2012). Protein particles: What we know and what we do not know. Journal of Pharmaceutical Sciences. 101(10). 3568–3579. 84 indexed citations
19.
Ripple, Dean C.. (2005). Thermoelectric Properties of a Selected Lot of Gold versus Platinum Thermocouples | NIST. 2 indexed citations
20.
Mangum, B. W., George T. Furukawa, K.G. Kreider, et al.. (2001). The Kelvin and temperature measurements. Journal of Research of the National Institute of Standards and Technology. 106(1). 105–105. 10 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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