M. Casas

2.7k citations
132 papers · 2.0k · h-index 24

Impact in

Papers in

M. Casas

129 papers receiving 1.9k citations

Peers

M. Casas
Comparison fields: 5 of 129
  • Statistical and Nonlinear Physics 461
  • Atomic and Molecular Physics, and Optics 1.1k
  • Radiological and Ultrasound Technology 106
  • Analytical Chemistry 193
  • Artificial Intelligence 567
Replace Francisco M. Fernández with:
Francisco M. Fernández Argentina
D. J. Douglas Canada
Sunil Mittal United States
R. C. Thompson United Kingdom
Joel Tellinghuisen United States
Xiang Liu China
Lu Yu China
G. C. Joshi Australia
Robert K. Boyd Canada
Burkhard Luy Germany
M. Casas relative to Francisco M. Fernández Argentina Francisco M. Fernández's profile →
Citations per field
00.5×10×20×30×35.3×
Francisco M. Fernández · 1×
Citations per year

Countries citing papers authored by M. Casas

Since Specialization
Citations

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

Fields of papers citing papers by M. Casas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside M. Casas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with M. Casas Line = papers co-authored together M. Casas links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 132 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1999156
2 2007122
3 2008109
4 201270
5 201062
6 199660
7 200557
8 200952
9 199452
10 200849
11 200149
12 199048
13 201148
14 201147
15 200647
16 201044
17 199143
18 201035
19 200230
20 201129

About M. Casas

M. Casas is a scholar working on Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics, Artificial Intelligence, Nuclear and High Energy Physics and Molecular Biology, having authored 132 papers that have together received 2.0k indexed citations. Recurring topics across this work include Quantum Mechanics and Applications (30 papers), Quantum Information and Cryptography (24 papers), Quantum Computing Algorithms and Architecture (16 papers), Cold Atom Physics and Bose-Einstein Condensates (16 papers), Quantum Mechanics and Non-Hermitian Physics (14 papers), Statistical Mechanics and Entropy (13 papers), Advanced Thermodynamics and Statistical Mechanics (13 papers) and Nuclear physics research studies (12 papers). The work is most often cited by research in Statistical and Nonlinear Physics (461 citations), Atomic and Molecular Physics, and Optics (1.1k citations), Radiological and Ultrasound Technology (106 citations), Analytical Chemistry (193 citations) and Artificial Intelligence (567 citations). M. Casas has collaborated with scholars based in Spain, Argentina and South Africa. Frequent co-authors include A. Plastino, Josep Amengual i Batle, A. Borrás, F. Garcías, Vı́ctor Cerdà, A. Rigó, A. Puente, Jessica Avivar, Laura Ferrer and A. P. Majtey. Their work appears in journals such as Physical Review A, Physics Letters A, Physica A Statistical Mechanics and its Applications, Applied Radiation and Isotopes and Nuclear Physics A.

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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