Michael Sluydts

448 total citations
9 papers, 300 citations indexed

About

Michael Sluydts is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Michael Sluydts has authored 9 papers receiving a total of 300 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Materials Chemistry, 4 papers in Electrical and Electronic Engineering and 2 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Michael Sluydts's work include Quantum Dots Synthesis And Properties (3 papers), Machine Learning in Materials Science (3 papers) and Chalcogenide Semiconductor Thin Films (3 papers). Michael Sluydts is often cited by papers focused on Quantum Dots Synthesis And Properties (3 papers), Machine Learning in Materials Science (3 papers) and Chalcogenide Semiconductor Thin Films (3 papers). Michael Sluydts collaborates with scholars based in Belgium, Australia and United States. Michael Sluydts's co-authors include Stefaan Cottenier, Kurt Lejaeghere, Zeger Hens, Kim De Nolf, Véronique Van Speybroeck, Lode Duprez, Koenraad Theuwissen, Tom Dhaene, Richard Čapek and Sofie Abé and has published in prestigious journals such as Journal of the American Chemical Society, ACS Nano and Chemistry of Materials.

In The Last Decade

Michael Sluydts

9 papers receiving 296 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael Sluydts Belgium	7	243	142	36	35	25	9	300
Ivan S. Novikov Russia	8	375 1.5×	84 0.6×	35 1.0×	17 0.5×	38 1.5×	11	416
Alice Castan France	10	272 1.1×	139 1.0×	89 2.5×	15 0.4×	12 0.5×	10	378
Samare Rostami Iran	7	258 1.1×	90 0.6×	47 1.3×	33 0.9×	10 0.4×	14	315
Shanzhong Wang Singapore	8	357 1.5×	181 1.3×	76 2.1×	14 0.4×	13 0.5×	11	422
Dmitry Krasikov United States	15	461 1.9×	425 3.0×	102 2.8×	15 0.4×	20 0.8×	31	602
Tania E. Sandoval Chile	10	176 0.7×	287 2.0×	31 0.9×	8 0.2×	9 0.4×	23	338
David S. D. Gunn United Kingdom	10	189 0.8×	131 0.9×	59 1.6×	22 0.6×	5 0.2×	16	300
K. Tabata Japan	8	270 1.1×	141 1.0×	85 2.4×	132 3.8×	11 0.4×	25	415
Stephen Eltinge United States	3	401 1.7×	97 0.7×	56 1.6×	24 0.7×	6 0.2×	6	439
A. M. Danishevskiı̆ Russia	10	204 0.8×	118 0.8×	67 1.9×	20 0.6×	29 1.2×	39	299

Countries citing papers authored by Michael Sluydts

Since Specialization

Citations

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

Fields of papers citing papers by Michael Sluydts

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Sluydts

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Sluydts. A scholar is included among the top collaborators of Michael Sluydts 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 Michael Sluydts. Michael Sluydts is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

Sluydts, Michael, et al.. (2024). The devil in the details: lessons from Li ₆ PS ₅ X for robust high-throughput workflows. Journal of Materials Chemistry A. 13(1). 526–539. 1 indexed citations

Bilal, Muhammad, Syed Muhammad Alay-e-Abbas, Michael Sluydts, et al.. (2021). DFT insights into surface properties of anti-perovskite 3D topological crystalline insulators: A case study of (001) surfaces of Ca3SnO. Physics Letters A. 408. 127469–127469. 6 indexed citations

Sluydts, Michael, et al.. (2020). Compact representations of microstructure images using triplet networks. npj Computational Materials. 6(1). 21 indexed citations

Sluydts, Michael, et al.. (2020). Race against the Machine: can deep learning recognize microstructures as well as the trained human eye?. Scripta Materialia. 193. 33–37. 24 indexed citations

Sluydts, Michael, et al.. (2017). Mechanistic Insights in Seeded Growth Synthesis of Colloidal Core/Shell Quantum Dots. Chemistry of Materials. 29(11). 4719–4727. 30 indexed citations

Sluydts, Michael, et al.. (2016). High-Throughput Screening of Extrinsic Point Defect Properties in Si and Ge: Database and Applications. Chemistry of Materials. 29(3). 975–984. 10 indexed citations

Lejaeghere, Kurt, et al.. (2016). Error estimates for density-functional theory predictions of surface energy and work function. Physical review. B.. 94(23). 112 indexed citations

Nolf, Kim De, Richard Čapek, Sofie Abé, et al.. (2015). Controlling the Size of Hot Injection Made Nanocrystals by Manipulating the Diffusion Coefficient of the Solute. Journal of the American Chemical Society. 137(7). 2495–2505. 63 indexed citations

Sluydts, Michael, Kim De Nolf, Véronique Van Speybroeck, Stefaan Cottenier, & Zeger Hens. (2015). Ligand Addition Energies and the Stoichiometry of Colloidal Nanocrystals. ACS Nano. 10(1). 1462–1474. 33 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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