Samuel G. Lambrakos

2.2k total citations · 1 hit paper
150 papers, 1.7k citations indexed

About

Samuel G. Lambrakos is a scholar working on Mechanical Engineering, Mechanics of Materials and Materials Chemistry. According to data from OpenAlex, Samuel G. Lambrakos has authored 150 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Mechanical Engineering, 32 papers in Mechanics of Materials and 30 papers in Materials Chemistry. Recurrent topics in Samuel G. Lambrakos's work include Welding Techniques and Residual Stresses (34 papers), Advanced Welding Techniques Analysis (18 papers) and Additive Manufacturing Materials and Processes (12 papers). Samuel G. Lambrakos is often cited by papers focused on Welding Techniques and Residual Stresses (34 papers), Advanced Welding Techniques Analysis (18 papers) and Additive Manufacturing Materials and Processes (12 papers). Samuel G. Lambrakos collaborates with scholars based in United States, Greece and Switzerland. Samuel G. Lambrakos's co-authors include Andrew Shabaev, John G. Michopoulos, Noam Bernstein, Gabriele Rainò, Georgian Nedelcu, Thilo Stöferle, Rainer F. Mahrt, Roman Vaxenburg, Maksym V. Kovalenko and Michael A. Becker and has published in prestigious journals such as Nature, The Journal of Chemical Physics and Physical review. B, Condensed matter.

In The Last Decade

Samuel G. Lambrakos

142 papers receiving 1.7k citations

Hit Papers

Bright triplet excitons i... 2018 2026 2020 2023 2018 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Samuel G. Lambrakos United States 15 895 888 360 360 139 150 1.7k
Chih‐Hsiang Ho United States 17 630 0.7× 634 0.7× 246 0.7× 455 1.3× 175 1.3× 63 1.7k
Zhen‐Gang Zhu China 26 1.5k 1.7× 965 1.1× 680 1.9× 716 2.0× 144 1.0× 165 2.9k
Б. В. Потапкин Russia 28 1.3k 1.4× 982 1.1× 601 1.7× 164 0.5× 139 1.0× 130 2.2k
Ruqing Xu United States 24 961 1.1× 239 0.3× 240 0.7× 454 1.3× 198 1.4× 77 1.8k
Xi Li China 21 1.6k 1.8× 800 0.9× 601 1.7× 159 0.4× 179 1.3× 92 2.3k
Keiji Watanabe Japan 22 908 1.0× 496 0.6× 160 0.4× 369 1.0× 68 0.5× 164 2.0k
F. Sato Japan 29 1.1k 1.3× 1.5k 1.7× 563 1.6× 283 0.8× 189 1.4× 192 2.7k
Akiko Kumada Japan 25 1.2k 1.3× 1.5k 1.7× 408 1.1× 132 0.4× 130 0.9× 232 2.1k
Dehua Li China 24 711 0.8× 860 1.0× 891 2.5× 351 1.0× 164 1.2× 170 2.1k
Bo Liu China 25 381 0.4× 1.3k 1.4× 444 1.2× 315 0.9× 164 1.2× 114 2.3k

Countries citing papers authored by Samuel G. Lambrakos

Since Specialization
Citations

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

Fields of papers citing papers by Samuel G. Lambrakos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Samuel G. Lambrakos. 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 Samuel G. Lambrakos. The network helps show where Samuel G. Lambrakos may publish in the future.

Co-authorship network of co-authors of Samuel G. Lambrakos

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel G. Lambrakos. A scholar is included among the top collaborators of Samuel G. Lambrakos 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 Samuel G. Lambrakos. Samuel G. Lambrakos 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.
Duncan, J.L., et al.. (2025). Spectrum-feature control of diffuse reflectance for NIR-SWIR absorbing dyes based on surface topology. Journal of Electromagnetic Waves and Applications. 39(18). 2355–2374.
2.
Zervaki, A. D., et al.. (2023). Inverse Thermal Analysis as a Tool for Optimizing Concentrated Solar Energy Elaboration of Wear Resistant Surface Layers. Metals. 13(5). 942–942. 1 indexed citations
3.
Ghosh, Dibyajyoti, Kevin L. Jensen, Daniel Finkenstadt, et al.. (2021). Cesium-Coated Halide Perovskites as a Photocathode Material: Modeling Insights. The Journal of Physical Chemistry Letters. 12(27). 6269–6276. 8 indexed citations
4.
Thompson, Sarah, et al.. (2021). Camouflage pattern segmentation for calculation of apparent reflectance spectra. 27–27. 1 indexed citations
5.
Shabaev, Andrew, et al.. (2018). Modeling apparent camouflage-pattern color using segment-weighted spectra. Journal of Electromagnetic Waves and Applications. 33(5). 541–556. 7 indexed citations
6.
Becker, Michael A., Alexander L. Efros, Georgian Nedelcu, et al.. (2018). Bright Triplet Emission from Lead Halide Perovskite Nanocrystals. Data Archiving and Networked Services (DANS). 2 indexed citations
7.
Lambrakos, Samuel G.. (2014). Inverse Thermal Analysis of Stainless Steel Deep-Penetration Welds Using Volumetric Constraints. Journal of Materials Engineering and Performance. 23(6). 2219–2232. 7 indexed citations
8.
Lambrakos, Samuel G., Andrew Shabaev, & Lili Huang. (2014). Inverse Thermal Analysis of a Titanium Laser Weld Using Multiple Constraint Conditions. Journal of Materials Engineering and Performance. 23(6). 2233–2240. 2 indexed citations
9.
Huang, Lulu, Andrew Shabaev, Samuel G. Lambrakos, & Lou Massa. (2012). Ground-State Features in the THz Spectra of Molecular Clusters of β-HMX. Applied Spectroscopy. 66(10). 1242–1248. 1 indexed citations
10.
Michopoulos, John G., John C. Hermanson, Athanasios Iliopoulos, Samuel G. Lambrakos, & Tomonari Furukawa. (2011). Data-Driven Design Optimization for Composite Material Characterization. Journal of Computing and Information Science in Engineering. 11(2). 18 indexed citations
11.
Fernsler, R. F., S. P. Slinker, & Samuel G. Lambrakos. (2008). A numerical model and scaling relationship for energetic electron beams propagating in air. Journal of Applied Physics. 104(6). 13 indexed citations
12.
Michopoulos, John G. & Samuel G. Lambrakos. (2006). Underlying issues associated with validation and verification of dynamic data driven simulation. Winter Simulation Conference. 2093–2100. 6 indexed citations
13.
Lambrakos, Samuel G., et al.. (2005). Analyses of hydrogen sorption kinetics and thermodynamics of magnesium–misch metal alloys. Journal of Alloys and Compounds. 407(1-2). 240–248. 20 indexed citations
14.
Lambrakos, Samuel G., et al.. (2004). Properties and Effects of Water-Soluble Inhibitors on the Corrosion Rates of Structural Metals. Journal of Materials Engineering and Performance. 13(6). 766–774. 1 indexed citations
15.
Lambrakos, Samuel G., et al.. (2004). Evaluation of the Effectiveness of Wash Water Additives on the Corrosion Behavior of Metal Structures Exposed to Marine Environments. CORROSION. 60(7). 611–621. 1 indexed citations
16.
Lagakos, N., et al.. (2000). A Fiber Optic Sensor for Detection of Degradation in Aircraft Lap Joints. CORROSION. 1–9. 2 indexed citations
17.
Lambrakos, Samuel G., et al.. (1999). Haz hardness in laser beam welds. E138–E145. 2 indexed citations
18.
Lambrakos, Samuel G., et al.. (1999). Closed-Cell Polymer Foams for Corrosion Control in Confined Metal Spaces. CORROSION. 55(5). 530–539. 3 indexed citations
20.
Georgiadis, R., et al.. (1997). Approach to Determine Electrochemical Interface Structure from Surface Optical Spectroscopy. Applied Spectroscopy. 51(3). 323–331. 2 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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