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Shape Memory Alloy Transformations

Shape Memory Alloy Transformations is a research topic within Materials Chemistry. Science Explorer counts 20k research works in it since 1952. 9.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the physical metallurgy, properties, and applications of shape memory alloys, particularly emphasizing martensitic transformation, magnetic field-induced strain, elastocaloric effect, and the microstructure of these materials. It covers a wide range of topics including biomedical applications, additive manufacturing, and metamagnetic shape memory alloys.

  • Shape Memory Alloys
  • Martensitic Transformation
  • Magnetic Field Induced Strain
  • Metamagnetic Shape Memory Alloys
  • Elastocaloric Effect
  • Ferromagnetic Shape Memory Alloys
  • Giant Magnetocaloric Effect
  • Microstructure
  • Biomedical Applications
  • Additive Manufacturing
Research works
20k
fractional, since 1952
In the world top 10%
1.9k
per year above
Top-10% rate
9.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+6%
the tick is no change

Which countries lead Shape Memory Alloy Transformations research?

By volume, China and the United States publish the most (1,000 and 256 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 1,000 works
  2. 2 United States 256 works
  3. 3 India 253 works
  4. 4 Russia 218 works
  5. 5 Germany 165 works
  6. 6 Japan 128 works
  7. 7 Italy 82 works
  8. 8 Türkiye 73 works
  9. 9 South Korea 65 works
  10. 10 France 58 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: 43.5%United States: 11.1%India: 11.0%Russia: 9.5%6 others listed: 24.9%44%largest
China1,000 · 43.5%United States256 · 11.1%India253 · 11.0%Russia218 · 9.5%6 others listed572 · 24.9%

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

Which institutions lead Shape Memory Alloy Transformations research?

By volume in 2022–2025, Harbin Institute of Technology publishes the most Shape Memory Alloy Transformations research, followed by National Research Tomsk State University and Northwestern Polytechnical University.

Who are the leading researchers in Shape Memory Alloy Transformations?

The most-cited researchers publishing on Shape Memory Alloy Transformations include Dierk Raabe, Paul K. Chu and Terence G. Langdon.

  1. 1 Dierk Raabe Germany 4.5k citations
  2. 2 Paul K. Chu Hong Kong 3.7k citations
  3. 3 Terence G. Langdon United States 3k citations
  4. 4 J. N. Reddy United States 2.9k citations
  5. 5 Claudia Felser Germany 2.7k citations
  6. 6 J. Eckert Germany 2.7k citations
  7. 7 Long‐Qing Chen United States 2.7k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Shape Memory Alloy Transformations research done?

The largest centres of Shape Memory Alloy Transformations research in 2022–2025 are Beijing (China), Xi'an (China), Harbin (China) and Shanghai (China). Among places with at least 20 works in it, it is an unusually large share of all research in Tomsk and Saarbrücken.

Largest cities, 2022–2025

  1. 1 Beijing China 134 works
  2. 2 Xi'an China 86 works
  3. 3 Harbin China 78 works
  4. 4 Shanghai China 77 works
  5. 5 Moscow Russia 63 works
  6. 6 Chengdu China 61 works
  7. 7 Guangzhou China 46 works
  8. 8 Shenyang China 39 works
  9. 9 Nanjing China 39 works
  10. 10 Tomsk Russia 38 works

Where it is the local speciality

  1. TomskRU · 37.6 works25×
  2. SaarbrückenDE · 21.4 works21×
← less than its size predictsmore →

Location quotient: how much more of its research is in Shape Memory Alloy Transformations than the world average.

See Shape Memory Alloy Transformations on the map

Where is the best place to study Shape Memory Alloy Transformations?

Among universities, judged by research, Southwest Jiaotong University, University of Toledo and Harbin Institute of Technology 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%20%40%mean 17.24%fractional works in this node (log) →share in the world top 10% →Southwest Jiaotong University: 24, 17.9%University of Toledo: 12, 30.7%Harbin Institute of Technology: 42, 12.5%National Research Tomsk State University: 32, 1.0%Hong Kong University of Science and Technology: 11, 20.5%National Institute of Technology Rourkela: 10, 25.2%Northeastern University: 23, 12.4%Xi'an Jiaotong University: 29, 17.0%Tongji University: 18, 27.1%Northwestern Polytechnical University: 32, 8.1%University of ToledoSouthwest Jiaotong U…Harbin Institute of …National Research To…
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 Southwest Jiaotong UniversityChina 70.417.9%10.7×24 +171.4%
2 University of ToledoUnited States 62.230.7%22.2×12 -39.6%
3 Harbin Institute of TechnologyChina 61.012.5%8.8×42 +44.0%
4 National Research Tomsk State UniversityRussia 60.41.0%57.9×32 +250.0%
5 Hong Kong University of Science and TechnologyHong Kong 59.720.5%9.9×11 -16.5%
6 National Institute of Technology RourkelaIndia 59.125.2%14.7×10
7 Northeastern UniversityChina 58.412.4%8.9×23 +129.4%
8 Xi'an Jiaotong UniversityChina 58.017.0%6.1×29 +126.8%
9 Tongji UniversityChina 57.127.1%4.7×18 +105.1%
10 Northwestern Polytechnical UniversityChina 55.78.1%9.4×32 +53.4%

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 Shape Memory Alloy Transformations research growing?

Output in 2018–2022 was 6% higher than in 2013–2017, peaking in 2022. The fastest-growing topics are Shape Memory Alloy Transformations.

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.