Stephen A. Sarles

2.1k total citations
83 papers, 1.6k citations indexed

About

Stephen A. Sarles is a scholar working on Electrical and Electronic Engineering, Molecular Biology and Biomedical Engineering. According to data from OpenAlex, Stephen A. Sarles has authored 83 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Electrical and Electronic Engineering, 34 papers in Molecular Biology and 31 papers in Biomedical Engineering. Recurrent topics in Stephen A. Sarles's work include Lipid Membrane Structure and Behavior (31 papers), Advanced Memory and Neural Computing (20 papers) and Photoreceptor and optogenetics research (17 papers). Stephen A. Sarles is often cited by papers focused on Lipid Membrane Structure and Behavior (31 papers), Advanced Memory and Neural Computing (20 papers) and Photoreceptor and optogenetics research (17 papers). Stephen A. Sarles collaborates with scholars based in United States, Lebanon and Switzerland. Stephen A. Sarles's co-authors include Donald J. Leo, C. Patrick Collier, Graham J. Taylor, Joseph S. Najem, Md Sakib Hasan, Ryan Weiss, Garrett S. Rose, Jonathan B. Boreyko, Barbar J. Akle and Panos G. Datskos and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Stephen A. Sarles

78 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stephen A. Sarles United States 23 727 574 552 277 152 83 1.6k
Anand Bala Subramaniam United States 20 803 1.1× 577 1.0× 320 0.6× 152 0.5× 213 1.4× 46 2.1k
Michele Dipalo Italy 25 1.0k 1.4× 479 0.8× 265 0.5× 684 2.5× 56 0.4× 63 1.9k
Shin‐ichiro M. Nomura Japan 24 695 1.0× 174 0.3× 1.4k 2.6× 494 1.8× 63 0.4× 111 2.5k
Kazuaki Sawada Japan 27 1.3k 1.8× 2.1k 3.7× 302 0.5× 343 1.2× 66 0.4× 388 3.3k
Mladen Barbic United States 14 1.2k 1.6× 534 0.9× 258 0.5× 308 1.1× 65 0.4× 35 2.6k
Tatsuo Yoshinobu Japan 36 876 1.2× 2.1k 3.7× 240 0.4× 244 0.9× 52 0.3× 201 3.6k
Hongyan Gao China 23 839 1.2× 1.2k 2.2× 92 0.2× 240 0.9× 68 0.4× 55 2.3k
Gili Bisker Israel 30 1.2k 1.7× 618 1.1× 717 1.3× 166 0.6× 30 0.2× 79 2.6k
Bilge Baytekin Türkiye 25 1.5k 2.0× 525 0.9× 186 0.3× 54 0.2× 84 0.6× 62 2.6k
Xize Niu United Kingdom 31 2.6k 3.5× 1.4k 2.4× 220 0.4× 83 0.3× 69 0.5× 59 3.0k

Countries citing papers authored by Stephen A. Sarles

Since Specialization
Citations

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

Fields of papers citing papers by Stephen A. Sarles

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stephen A. Sarles. 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 Stephen A. Sarles. The network helps show where Stephen A. Sarles may publish in the future.

Co-authorship network of co-authors of Stephen A. Sarles

This figure shows the co-authorship network connecting the top 25 collaborators of Stephen A. Sarles. A scholar is included among the top collaborators of Stephen A. Sarles 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 Stephen A. Sarles. Stephen A. Sarles 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.
2.
Shrestha, Som, et al.. (2025). Neuron‐Inspired Biomolecular Memcapacitors Formed Using Droplet Interface Bilayer Networks. Advanced Electronic Materials. 11(6). 2 indexed citations
3.
Sarles, Stephen A., et al.. (2024). Droplet Polymer Bilayers for Bioelectronic Membrane Interfacing. Journal of the American Chemical Society. 146(21). 14391–14396. 3 indexed citations
4.
Hussein, Essraa A., et al.. (2024). Highly Resistive Biomembranes Coupled to Organic Transistors enable Ion‐Channel Mediated Neuromorphic Synapses. Advanced Electronic Materials. 11(4). 3 indexed citations
5.
Barrera, Francisco N., et al.. (2024). Biomolecular Neuristors from Functionalized Lipid Membranes. Advanced Functional Materials. 34(49). 3 indexed citations
6.
Wright, Joseph P., Stephen A. Sarles, & Jin‐Song Pei. (2023). DC operating points of Mott neuristor circuits. Microelectronic Engineering. 284-285. 112124–112124. 2 indexed citations
7.
8.
Inman, Daniel J., et al.. (2023). Sensory Adaptation in Biomolecular Memristors Improves Reservoir Computing Performance. SHILAP Revista de lepidopterología. 5(8). 24 indexed citations
9.
Spittle, Stephanie, et al.. (2022). Entrapment and Voltage-Driven Reorganization of Hydrophobic Nanoparticles in Planar Phospholipid Bilayers. ACS Applied Materials & Interfaces. 14(49). 54558–54571. 9 indexed citations
10.
Heberle, Frederick A., et al.. (2022). Homogeneous hybrid droplet interface bilayers assembled from binary mixtures of DPhPC phospholipids and PB-b-PEO diblock copolymers. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1864(10). 183997–183997. 7 indexed citations
11.
Sarles, Stephen A., et al.. (2021). Pressure-driven generation of complex microfluidic droplet networks. Microfluidics and Nanofluidics. 25(9). 6 indexed citations
12.
Premadasa, Uvinduni I., et al.. (2021). Nanoparticle-Induced Disorder at Complex Liquid–Liquid Interfaces: Effects of Curvature and Compositional Synergy on Functional Surfaces. ACS Nano. 15(9). 14285–14294. 26 indexed citations
13.
Najem, Joseph S., et al.. (2019). Memristive plasticity in artificial electrical synapses via geometrically reconfigurable, gramicidin-doped biomembranes. Nanoscale. 11(40). 18640–18652. 26 indexed citations
14.
Chowdhury, Azhad U., Graham J. Taylor, Vera Bocharova, et al.. (2019). Insight into the Mechanisms Driving the Self-Assembly of Functional Interfaces: Moving from Lipids to Charged Amphiphilic Oligomers. Journal of the American Chemical Society. 142(1). 290–299. 36 indexed citations
15.
Najem, Joseph S., Md Sakib Hasan, R. Stanley Williams, et al.. (2019). Dynamical nonlinear memory capacitance in biomimetic membranes. Nature Communications. 10(1). 3239–3239. 81 indexed citations
16.
Song, Woochul, Himanshu Joshi, Ratul Chowdhury, et al.. (2019). Artificial water channels enable fast and selective water permeation through water-wire networks. Nature Nanotechnology. 15(1). 73–79. 141 indexed citations
17.
Taylor, Graham J., et al.. (2018). Evaporation-induced monolayer compression improves droplet interface bilayer formation using unsaturated lipids. Biomicrofluidics. 12(2). 24101–24101. 20 indexed citations
18.
Najem, Joseph S., Graham J. Taylor, Ryan Weiss, et al.. (2018). Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics. ACS Nano. 12(5). 4702–4711. 150 indexed citations
19.
Taylor, Graham J., et al.. (2018). Electrophysiological interrogation of asymmetric droplet interface bilayers reveals surface-bound alamethicin induces lipid flip-flop. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1861(1). 335–343. 42 indexed citations
20.
Tian, Weidong, et al.. (2013). Validity of a New Respiratory Resistance Measurement Device to Detect Glottal Area Change. Journal of Voice. 27(3). 299–304. 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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