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Topic · Biomedical Engineering

Characterization and Applications of Magnetic Nanoparticles

Characterization and Applications of Magnetic Nanoparticles is a research topic within Biomedical Engineering. Science Explorer counts 20k research works in it since 1950. 14.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the research and applications of magnetic particle imaging (MPI) and ferrofluids. The topics covered include nanoparticle tracers, biosensing, in vivo imaging, dipolar interactions, real-time monitoring, and biomedical applications.

  • Magnetic Particle Imaging
  • Ferrofluids
  • Nanoparticles
  • Biosensors
  • Imaging
  • Magnetism
  • Nanomedicine
  • Biomedical Applications
  • Dipolar Interactions
  • Real-Time Monitoring
Research works
20k
fractional, since 1950
In the world top 10%
2.8k
per year above
Top-10% rate
14.0%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-2%
the tick is no change

Which countries lead Characterization and Applications of Magnetic Nanoparticles research?

By volume, China and the United States publish the most (663 and 299 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 663 works
  2. 2 United States 299 works
  3. 3 Russia 243 works
  4. 4 India 241 works
  5. 5 Germany 166 works
  6. 6 Japan 157 works
  7. 7 France 105 works
  8. 8 Spain 70 works
  9. 9 United Kingdom 68 works
  10. 10 Iran 57 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 32.1%United States: 14.5%Russia: 11.8%India: 11.7%6 others listed: 30.0%32%largest
China663 · 32.1%United States299 · 14.5%Russia243 · 11.8%India241 · 11.7%6 others listed622 · 30.0%

Shares of the rows listed above, not of the whole node.

Which institutions lead Characterization and Applications of Magnetic Nanoparticles research?

By volume in 2022–2025, Beihang University publishes the most Characterization and Applications of Magnetic Nanoparticles research, followed by Ural Federal University and Tsinghua University.

By volume, 2022–2025

  1. 1 Beihang UniversityChina 31 works
  2. 2 Ural Federal UniversityRussia 29 works
  3. 3 Tsinghua UniversityChina 26 works
  4. 4 Chinese Academy of SciencesChina 19 works
  5. 5 Tohoku UniversityJapan 15 works
  6. 6 Centre National de la Recherche ScientifiqueFrance 15 works
  7. 7 Lomonosov Moscow State UniversityRussia 15 works
  8. 8 Institute of Continuous Media MechanicsRussia 13 works
  9. 9 North-Caucasus Federal UniversityRussia 12 works
  10. 10 University of Chinese Academy of SciencesChina 12 works

Where is Characterization and Applications of Magnetic Nanoparticles research done?

The largest centres of Characterization and Applications of Magnetic Nanoparticles research in 2022–2025 are Beijing (China), Moscow (Russia), Tokyo (Japan) and Yekaterinburg (Russia). Among places with at least 20 works in it, it is an unusually large share of all research in Yekaterinburg.

Largest cities, 2022–2025

  1. 1 Beijing China 172 works
  2. 2 Moscow Russia 73 works
  3. 3 Tokyo Japan 38 works
  4. 4 Yekaterinburg Russia 37 works
  5. 5 Paris France 36 works
  6. 6 Shanghai China 35 works
  7. 7 Nanjing China 33 works
  8. 8 Wuhan China 27 works
  9. 9 Tehran Iran 25 works
  10. 10 Xi'an China 25 works

Where it is the local speciality

  1. YekaterinburgRU · 36.7 works14×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Characterization and Applications of Magnetic Nanoparticles than the world average.

See Characterization and Applications of Magnetic Nanoparticles on the map

Where is the best place to study Characterization and Applications of Magnetic Nanoparticles?

Among universities, judged by research, Beihang University, Ural Federal University and Tsinghua University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%mean 6.68%fractional works in this node (log) →share in the world top 10% →Beihang University: 31, 9.8%Ural Federal University: 29, 0.6%Tsinghua University: 26, 12.4%University of Chinese Academy of Sciences: 12, 16.8%Guangxi University of Science and Technology: 9, 10.4%North-Caucasus Federal University: 12, 0.0%Lomonosov Moscow State University: 15, 0.9%Tohoku University: 15, 3.4%Beijing Jiaotong University: 12, 8.6%Indian Institute of Science Bangalore: 8, 3.9%University of Chines…Tsinghua UniversityBeihang UniversityUral Federal Univers…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Beihang UniversityChina 66.69.8%10.2×31 +36.1%
2 Ural Federal UniversityRussia 54.70.6%36.3×29 +63.4%
3 Tsinghua UniversityChina 54.312.4%4.8×26 +40.8%
4 University of Chinese Academy of SciencesChina 52.016.8%2.6×12 +203.9%
5 Guangxi University of Science and TechnologyChina 47.810.4%37.0×9 —
6 North-Caucasus Federal UniversityRussia 46.40.0%63.7×12 +1226.2%
7 Lomonosov Moscow State UniversityRussia 43.70.9%6.2×15 +164.0%
8 Tohoku UniversityJapan 42.43.4%8.8×15 +3.1%
9 Beijing Jiaotong UniversityChina 35.68.6%7.0×12 -55.9%
10 Indian Institute of Science BangaloreIndia 34.43.9%7.8×8 +73.2%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Characterization and Applications of Magnetic Nanoparticles research growing?

Output in 2018–2022 was 2% lower than in 2013–2017, peaking in 2024. The fastest-growing topics are Characterization and Applications of Magnetic Nanoparticles.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.