Skip to main content
SpringerLink
Log in

The structure and infrastructure of the global nanotechnology literature

  • Published:
Journal of Nanoparticle Research Aims and scope Submit manuscript

Abstract

Text mining is the extraction of useful information from large volumes of text. A text mining analysis of the global open nanotechnology literature was performed. Records from the Science Citation Index (SCI)/Social SCI were analyzed to provide the infrastructure of the global nanotechnology literature (prolific authors/journals/institutions/countries, most cited authors/papers/journals) and the thematic structure (taxonomy) of the global nanotechnology literature, from a science perspective. Records from the Engineering Compendex (EC) were analyzed to provide a taxonomy from a technology perspective.

  • The Far Eastern countries have expanded nanotechnology publication output dramatically in the past decade.

  • The Peoples Republic of China ranks second to the USA (2004 results) in nanotechnology papers published in the SCI, and has increased its nanotechnology publication output by a factor of 21 in a decade.

  • Of the six most prolific (publications) nanotechnology countries, the three from the Western group (USA, Germany, France) have about eight percent more nanotechnology publications (for 2004) than the three from the Far Eastern group (China, Japan, South Korea).

  • While most of the high nanotechnology publication-producing countries are also high nanotechnology patent producers in the US Patent Office (as of 2003), China is a major exception. China ranks 20th as a nanotechnology patent-producing country in the US Patent Office.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bhushan B., 2004. Springer Handbook of Nanotechnology. Springer

  • Colton R.J. (2004). Nanoscale measurements and manipulation. Journal of Vacuum Science and Technology B 22(4):1609–1635

    Article  CAS  Google Scholar 

  • Davidse R.J., Van Raan A.F.J. (1997). Out of particles: impact of CERN, DESY, and SLAC research to fields other than physics. Scientometrics 40(2):171–193

    Article  Google Scholar 

  • Dowling A. et al., 2004. Nanoscience and Nanotechnologies: Opportunities and Uncertainties. The Royal Society and the Royal Academy of Engineering. 29 July

  • Freitas R.A., 1999. Nanomedicine, Vol. 1: Basic Capabilities. Landes Bioscience

  • Freitas R.A., 2003. Nanomedicine, Vol. 2: Biocompatibility. Landes Bioscience

  • Garfield E. (1985). History of citation indexes for chemistry - a brief review. JCICS 25(3):170–174

    CAS  Google Scholar 

  • Goddard W.A., D.W. Brenner, S.E. Lyshevski & G.J. Iafrate, 2002. Handbook of Nanoscience, Engineering, and Technology. CRC Press

  • Goldman J.A., Chu W.W., Parker D.S., Goldman R.M. (1999). Term domain distribution analysis: a data mining tool for text databases. Methods of Information in Medicine 38: 96–101

    CAS  Google Scholar 

  • Gordon M.D., Dumais S. (1998). Using latent semantic indexing for literature based discovery. Journal of the American Society for Information Science 49(8):674–685

    CAS  Google Scholar 

  • Greengrass E., 1997. Information Retrieval: An Overview. National Security Agency. TR-R52–02–96

  • Hearst M.A., 1999. Untangling text data mining. Proceedings of ACL 99, the 37th Annual Meeting of the Association for Computational Linguistics, University of Maryland, June 20–26

  • Huang Z., Chen H., Chen Z.K., and Roco M.C. (2004). International Nanotechnology Development in 2003; Country, Institution, and Technology Field Analysis based on USPTO Patent Database. Journal of Nanoparticle Research 6:325–354

    Article  Google Scholar 

  • Karypis G., 2005. CLUTO – A Clustering Toolkit. http://www.cs.umn.edu/∼ ∼cluto

  • Kostoff R.N. (1998). The use and misuse of citation analysis in research evaluation. Scientometrics 43(1):27–43

    Article  Google Scholar 

  • Kostoff R.N. (2003a). Text mining for global technology watch. In: Drake M. (ed) Encyclopedia of Library and Information Science, Second Edition. Marcel Dekker, Inc., New York, NY, Vol. 4. pp. 2789–2799

    Google Scholar 

  • Kostoff R.N., 2003b Stimulating innovation. In: Larisa V. Shavinina (ed.). International Handbook of Innovation. Elsevier Social and Behavioral Sciences, Oxford, U.K., pp. 388–400

  • Kostoff R.N. (2003c). Bilateral asymmetry prediction. Medical Hypotheses 61(2): 265–266

    Article  Google Scholar 

  • Kostoff R.N. (2005a). Systematic acceleration of radical discovery and innovation in science and technology. DTIC Technical Report Number ADA430720 (http://www.dtic.mil/). Defense Technical Information Center, Fort Belvoir, VA

    Google Scholar 

  • Kostoff R.N., Del Rio J.A., García E.O., Ramírez A.M., Humenik J.A. (2001). Citation mining: integrating text mining and bibliometrics for research user profiling. Journal of the American Society for Information Science and Technology 52(13):1148–1156

    Article  Google Scholar 

  • Kostoff R.N., Eberhart H.J., Toothman D.R. (1997). Database Tomography for information retrieval. Journal of Information Science 23(4):301–311

    Article  Google Scholar 

  • Kostoff R.N., Green K.A., Toothman D.R. and Humenik J.A. (2000). Database Tomography applied to an aircraft science and technology investment strategy. Journal of Aircraft 37(4):727–730

    Article  Google Scholar 

  • Kostoff R.N., J.S. Murday, C.G.Y. Lau & W.M. Tolles, 2005a. The Seminal Literature of Nanotechnology Research. DTIC Technical Report Number ADA435986 (http://www.dtic.mil/). Defense Technical Information Center. Fort Belvoir, VA. Also, an abridged version is published in this issue

  • Kostoff R.N., Shlesinger M., Malpohl G. (2004b). Fractals roadmaps using bibliometrics and database tomography. Fractals 12(1): 1–16

    Google Scholar 

  • Kostoff R.N., Shlesinger M., Tshiteya R. (2004a). Nonlinear dynamics roadmaps using bibliometrics and Database Tomography. International Journal of Bifurcation and Chaos 14(1):61–92

    Article  Google Scholar 

  • Kostoff R.N., Stump J.A., Johnson D., Murday J.S., Lau C.G.Y., Tolles WM. (2005e). The structure and infrastructure of the global nanotechnology literature. DTIC Technical Report Number ADA435984 (http://www.dtic.mil/). Defense Technical Information Center, Fort Belvoir, VA

    Google Scholar 

  • Kricka L.J. and Fortina P. (2002). Nanotechnology and applications: An all-language literature survey including books and patents. Clinical Chemistry 48(4):662–665

    PubMed  CAS  Google Scholar 

  • Losiewicz P., Oard D., Kostoff R.N. (2000). Textual data mining to support science and technology management. Journal of Intelligent Information Systems 15: 99–119

    Article  Google Scholar 

  • MacRoberts M., MacRoberts B. (1996). Problems of citation analysis. Scientometrics 36(3):435–444

    Article  Google Scholar 

  • Narin F., 1976. Evaluative bibliometrics: the use of publication and citation analysis in the evaluation of scientific activity (monograph). NSF C-637. National Science Foundation. Contract NSF C-627. NTIS Accession No. PB252339/AS

  • Narin F., Olivastro D., Stevens K.A. (1994). Bibliometrics theory, practice and problems. Evaluation Review 18(1):65–76

    Google Scholar 

  • Schubert A., Glanzel W., Braun T. (1987). Subject field characteristic citation scores and scales for assessing research performance. Scientometrics 12(5–6):267–291

    Article  Google Scholar 

  • SCI (2005) Science Citation Index. Institute for Scientific Information, Phila., PA

    Google Scholar 

  • Simon J. (2005). Micro- and nanotechnologies: dullish electrons and smart molecules. Comptes Rendus Chimie 8(5):893–902

    Article  CAS  Google Scholar 

  • Swanson D.R. (1986) Fish Oil, Raynauds Syndrome, and undiscovered public knowledge. Perspect Biol Med. 30(1):7–18

    PubMed  CAS  Google Scholar 

  • Swanson D.R., Smalheiser N.R. (1997). An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artif Intell 91(2):183–203

    Article  Google Scholar 

  • TREC (Text Retrieval Conference), 2004. Home Page, http://trec.nist.gov/

  • Viator J.A., Pestorius F.M. (2001). Investigating trends in acoustics research from 1970–1999. Journal of the Acoustical Society of America 109(5):1779–1783 Part 1

    Article  PubMed  CAS  Google Scholar 

  • Winkmann G., Schlutius S., Schweim H.G. (2002). Citation rates of medical German-language journals in English-language papers - do they correlate with the Impact Factor, and who cites?. Klinische Monatsblatter fur Augenheilkunde 219(1–2):72–78

    Article  CAS  Google Scholar 

  • Zhao Y., Karypis G. (2004). Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning 55(3): 311–331

    Article  Google Scholar 

  • Zhu D.H. & A.L. Porter, 2002. Automated extraction and visualization of information for technological intelligence and forecasting. Technological Forecasting and Social Change. 69 (5):495–506.

    Article  Google Scholar 

Download references

Author information

Author notes

  1. Dustin Johnson

    Present address: Northrop Grumman TASC, 12015 Lee Jackson Highway, Fairfax, VA, 22033, USA

  2. James S. Murday

    Present address: Chemistry Division, Code 6100, Naval Research Laboratory, Washington, DC, 20375, USA

  3. Clifford G.Y. Lau

    Present address: Institute for Defense Analyses, 4850 Mark Center Drive, Alexandria, VA, 22311, USA

Authors and Affiliations

  1. Office of Naval Research, 875 N. Randolph St., Arlington, VA, 22217, USA

    Ronald N. Kostoff, Jesse A. Stump, Dustin Johnson, James S. Murday & Clifford G.Y. Lau

  2. 8801 Edward Gibbs Place, Alexandria, VA, 22309, USA

    William M. Tolles

Authors
  1. Ronald N. Kostoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jesse A. Stump
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dustin Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James S. Murday
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Clifford G.Y. Lau
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. William M. Tolles
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ronald N. Kostoff.

Appendix 1 – EC and SCI factor analysis

Appendix 1 – EC and SCI factor analysis

Factor analysis of a text database aims to reduce the number of words/phrases (variables) in a system, and to detect structure in the relationships among words/phrases. Word/phrase correlations are computed, and highly correlated groups (factors) are identified. The relationships of these words/phrases to the resultant factors are displayed clearly in the factor matrix, whose rows are words/phrases and columns are factors. In the factor matrix, the matrix elements Mij are the factor loadings, or the contribution of word/phrase i (in row i) to the theme of factor j (in column j). The theme of each factor is determined by those words/phrases that have the largest values of factor loading. Each factor has a positive value tail and negative value tail. For each factor, one of the tails dominates in terms of absolute value magnitude. This dominant tail is used to determine the central theme of each factor.

Factor analyses were performed on the EC and SCI retrievals. Factor matrices ranging from 2 to 32 factors were generated, the main themes identified, and the themes were manually categorized into a hierarchical taxonomy. The SCI taxonomy is presented first, followed by the EC taxonomy.

SCI taxonomy

Level 1

  • Instruments (XRD-TEM-SEM)

  • Phenomena/Properties (Crystal structure)

Level 2

  • Instruments (XRD-TEM-SEM; Differential calorimetry)

  • Phenomena/Properties (Crystal structure; Surface adsorption (SAM/Film deposition))

Level 3

  • Instruments (XRD-TEM-SEM; Differential calorimetry; AFM)

  • Phenomena/Properties (Crystal structure; Surface adsorption (SAM/Film deposition); Photoluminescence (Quantum dots); Catalysis

EC taxonomy

For a two factor analysis, the main thrusts are:

  1. (1)

    Films

  2. (2)

    Nanocomposites–Clay/Differential calorimetry

For a four-factor analysis, the main thrusts are:

  1. (1)

    Films (hardness, mechanical properties)

  2. (2)

    Nanocomposites–Clay/Differential calorimetry

  3. (3)

    Nanoparticle formation/reaction/catalysis

  4. (4)

    Microstructure (Ni/Zr/C/B)

For an eight-factor analysis, the main thrusts are:

  1. (1)

    Differential calorimetry/Nanocomposites–Clay

  2. (2)

    Films (temperature/thickness/deposition)

  3. (3)

    XRD/TEM (size, catalysis)

  4. (4)

    Ni/Cu (alloys, Fe, Co)

  5. (5)

    Hardness/Mechanical properties

  6. (6)

    CNT

  7. (7)

    SAMs

  8. (8)

    Crystal structure

These results contrast the differences between the SCI and EC databases from the factor matrix perspective, as well as the differences between document clustering-based taxonomies and factor matrix-based taxonomies. The document clustering taxonomies are categorized essentially by structures (e.g., nanowires, nanotubes, nanoparticles, films) and phenomena (optics, magnetics). The SCI factor matrix taxonomies are characterized by instruments (XRD, TEM, SEM, AFM, differential calorimetry) and the quantities they measure (crystal structure, surface adsorption, photoluminescence). The EC factor matrix taxonomies are characterized by structures (films, nanocomposites, nanoparticles, microstructures).

At the first level of the factor matrix taxonomies, the science focus of the SCI, which concentrates on instrumentation and basic scientific phenomena (crystal structure), is clearly seen. The technology focus of the EC, which concentrates on structures and materials (films, nano-composites-clay) is also evident.

At the second level, the science focus of the SCI remains the same, with additional instrumentation and measured phenomena shown. The EC focus continues on particles and microstructure. At the third level, the focus of the EC on structures and materials continues (CNT, SAMs, alloys, mechanical properties), but some of the applied research aspects begin to emerge (XRD/TEM, crystal structure).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kostoff, R.N., Stump, J.A., Johnson, D. et al. The structure and infrastructure of the global nanotechnology literature. J Nanopart Res 8, 301–321 (2006). https://doi.org/10.1007/s11051-005-9035-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11051-005-9035-8

Keywords

Search

Navigation