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Nanotechnology patenting trends through an environmental lens: analysis of materials and applications

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Abstract

Many international groups study environmental health and safety (EHS) concerns surrounding the use of engineered nanomaterials (ENMs). These researchers frequently use the “Project on Emerging Nanotechnologies” (PEN) inventory of nano-enabled consumer products to prioritize types of ENMs to study because estimates of life-cycle ENM releases to the environment can be extrapolated from the database. An alternative “snapshot” of nanomaterials likely to enter commerce can be determined from the patent literature. The goal of this research was to provide an overview of nanotechnology intellectual property trends, complementary to the PEN consumer product database, to help identify potentially “risky” nanomaterials for study by the nano-EHS community. Ten years of nanotechnology patents were examined to determine the types of nano-functional materials being patented, the chemical compositions of the ENMs, and the products in which they are likely to appear. Patenting trends indicated different distributions of nano-enabled products and materials compared to the PEN database. Recent nanotechnology patenting is dominated by electrical and information technology applications rather than the hygienic and anti-fouling applications shown by PEN. There is an increasing emphasis on patenting of nano-scale layers, coatings, and other surface modifications rather than traditional nanoparticles, and there is widespread use of nano-functional semiconductor, ceramic, magnetic, and biological materials that are currently less studied by EHS professionals. These commonly patented products and the nano-functional materials they contain may warrant life-cycle evaluations to determine the potential for environmental exposure and toxicity. The patent and consumer product lists contribute different and complementary insights into the emerging nanotechnology industry and its potential for introducing nanomaterials into the environment.

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Notes

  1. Nanotechnology researchers have also used forward and backward citations to determine the relationship between academic and patent literature, particularly to show knowledge-sharing trends. (Finardi 2011; Igami 2008; Li et al. 2009; Wang and Guan 2011; Zucker et al. 2007). Inclusion of bibliometric analyses is also valuable to map the emerging nanotechnology field, i.e., its interdisciplinary, multidisciplinary or “general purpose” nature (Igami 2008; Schultz and Joutz 2010; Youtie et al. 2007).

  2. A network was also created for the USPTO 2010 Applications dataset, but clustering was so similar it did not warrant separate reporting.

  3. Parenthetically, the 10,000-word abstract vectors for the materials analysis are rich in mineable information; modern machine-learning researchers commonly employ hierarchical clustering or classification methods to word vectors to find common themes and evaluate relationships between documents. In this study, simple hierarchical clustering of the patent abstract word vectors was attempted to learn product/process trends, but results were not adequate to categorize inventions by their purpose or usage. A state-of-the-art algorithm would be required to interpret patent text in this way; therefore, the readily available IPC was used instead. There is minor risk associated with this study’s reliance on third-party invention classifications rather than unbiased text-mining algorithms. Though the inventors and examiners are field experts and should therefore make few if any unintentional errors in their IPC assignments, hidden agendas could exist behind the assignment of a given invention to one or more IPC categories, whether to increase scope of the claim or avoid the appearance of infringement. We have accepted these risks because using the IPC provided more relevant and granular information than our simple algorithms.

  4. Examples of more specific product or technology types belonging in each broad Section-Level are revealed in the following IPC Class and SubClass analyses. A table with this information is also provided in the Supplementary Material.

  5. A table showing absolute counts of IPC Subclasses assigned to 2006–2010 USPTO Applications is presented in the Supplementary Material.

  6. There are very few published studies data-mining for material usage trends in nanotechnology patents or journals, and those that exist tend to explore single technology fields, such as Menéndez-Manjón’s (Menéndez-Manjón et al. 2011) analysis of nano-energy applications. To our knowledge, design of a sophisticated text-mining algorithm for characterization of nano-scale materials described in academic and IP literature, excluding non-nano materials, has not yet been achieved in any published study. This algorithm, if it existed, would be valuable for cataloging common and emergent material characteristics of ENPs only, not the full product/invention. Creation of such an algorithm, however, was beyond the scope of this study.

  7. Between 2001 and 2011, the annual percentage of nanotechnology inventions which contain nanotubes increased significantly in both the Applications and Patent datasets (R 2 for linear and quadratic regressions range between 0.85 and 0.96 with P values between 0.038 and 3E−6). Regressions are provided in the Supplementary Material.

  8. Listed elements are 25 or more “ranks” higher than their earth abundance.

  9. One limitation of the binary element tallying method is that stoichiometric composition of molecules is not accounted for when summing element frequencies. For example, biological molecules appearing in patent abstracts contain more carbon than nitrogen, sulfur and phosphorus, however, each element is counted the same. This led to a boost in ranking for N, S and P, however, ranking of non-biological elements were relatively unaffected by trial inclusion of stoichiometry.

  10. The entire FR diagram, showing all vertices (elements appearing in USPTO 2000–2011 Nanotechnology Grants dataset) and all edges (co-occurrences in the same abstract) is supplied in the Supplementary Material along with an element co-occurrence matrix. Without filters, the data are too cluttered to draw case-by-case conclusions about elements’ association with each other but do display tight clustering of element types. Detailed diagrams and matrices such as these, though unfit for concise presentation in a journal article, could be of interest to nano-toxicologists determining relevant mixtures of elements to test.

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Acknowledgments

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 0750271 as well as by the National Science Foundation and the Environmental Protection Agency under NSF Cooperative Agreement EF-0830093, Center for the Environmental Implications of NanoTechnology (CEINT). Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the Environmental Protection Agency. This work has not been subjected to EPA review and no official endorsement should be inferred. Additional support came from the Prem Narain Srivastva Legacy Fellowship and the 9–11 GI Bill. We thank Dr. Stephen Tedeschi from Landon IP and Dr. Todd Kuiken from Woodrow Wilson International Center for Scholars for their assistance in gathering datasets. We also thank Dr. Jurron Bradley from LUX research and Dr. Lee Branstetter for advice on patent analysis as well as Dr. Mark Kryder for discussions of the field of non-volatile memory. Finally, we thank Elijah Mayfield for assistance with WEKA and SIDE implementation, and summer research assistant Shawn Kollesar for his data-mining assistance.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Center for the Environmental Implications of NanoTechnology (CEINT), Carnegie Mellon University, Pittsburgh, USA

    Megan E. Leitch

  2. Department of Engineering and Public Policy, Center for the Environmental Implications of NanoTechnology (CEINT), Carnegie Mellon University, Baker Hall 129, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA

    Elizabeth Casman

  3. Department of Civil and Environmental Engineering, Center for the Environmental Implications of NanoTechnology (CEINT), Carnegie Mellon University, Porter Hall 119, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Gregory V. Lowry

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  1. Megan E. Leitch
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Correspondence to Gregory V. Lowry.

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Leitch, M.E., Casman, E. & Lowry, G.V. Nanotechnology patenting trends through an environmental lens: analysis of materials and applications. J Nanopart Res 14, 1283 (2012). https://doi.org/10.1007/s11051-012-1283-9

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