J Nanopart Res (2012) 14:1283 DOI 10.1007/s11051-012-1283-9
PERSPECTIVES
Nanotechnology patenting trends through an environmental lens: analysis of materials and applications Megan E. Leitch • Elizabeth Casman Gregory V. Lowry
•
Received: 16 August 2012 / Accepted: 2 November 2012 / Published online: 25 November 2012 Springer Science+Business Media Dordrecht 2012
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
Electronic supplementary material The online version of this article (doi:10.1007/s11051-012-1283-9) contains supplementary material, which is available to authorized users. M. E. Leitch Department of Civil and Environmental Engineering, Center for the Environmental Implications of NanoTechnology (CEINT), Carnegie Mellon University, Pittsburgh, USA E. Casman 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 G. V. Lowry (&) 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 e-mail:
[email protected]
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. Keywords Engineered nanomaterials Nanotechnology life-cycle assessment
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Nano-enabled products Environmental implications of nanotechnology Environmental release of nanomaterials Nanotoxicology Environmental nanotechnology
Introduction The goal of the many interdisciplinary, internationally collaborating research groups studying environmental health and safety (EHS) issues for nanotechnology is to proactively identify attendant risks before significant ecological consequences occur (Holdren 2011; Klaine et al. 2012; Wiesner et al. 2009). Certain nanomaterial types have been prioritized for study according to their potential for toxicity, and by estimates of the potential for exposure to the materials, i.e., the belief that they will be released into the environment in large amounts (Robichaud et al. 2009). Nanomaterials having high exposure and toxicity potential are deemed to be the most environmentally relevant for study. In the absence of global or national mandatory reporting of industrial ENM production and use, however, relevance determinations for ENMtypes have been speculative. (Rocks et al. 2009; Roco et al. 2011). An influential database, the Woodrow Wilson International Center for Scholar’s ‘‘Project on Emerging Nanotechnologies’’ (PEN) inventory of internet-advertised nano-enabled consumer products (Rejeski 2009), has been the basis for several estimates of ENM mass entering the environment, i.e., exposure potential. (Gottschalk et al. 2009, 2010; Money et al. 2012; Mueller and Nowack 2008; Som et al. 2011). This focus on existing consumer products has, for example, stimulated a significant amount of study of the fate, behavior, and effects of silver nanoparticles in the environment (Fabrega et al. 2011; Johnston et al. 2010; Levard et al. 2012; Lowry et al. 2012; Marambio-Jones and Hoek 2010; Wijnhoven et al. 2009). Many of these studies cite the large number of consumer products containing silver nanoparticles and the known bactericidal and aquatic toxicity of Ag as justification for their study. Alternatively, the patent literature can be used to determine the types of ENMs likely to emerge in commerce. Patents have long been recognized as an excellent information source for describing the development of technological fields (Griliches 1990), and industries such as biotechnology, electronics, and
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information technology have been extensively studied though intellectual property (IP) data mining. IP documents are not only rich in details regarding what materials are used and the inventions’ intended applications but they also reflect an intermediary domain between academic studies and commercial production of nanomaterials. Patents represent the latest advancements in science that inventors believe could improve products or processes. Previous patent research provided insight into the development of the nanotechnology industry over the past 20 years. Published studies of nanotechnology patents have typically mined geographical, assignee, inventor, topic, and/or citation data. For example, nanotechnology patents have been examined to determine prolific countries and institutions and their common invention classifications (Alencar and Porter 2007; Chen et al. 2008; Dang et al. 2010; Li et al. 2007, 2009), to compare patenting activity of small and large firms (Ferna´ndez-ribas 2010), to describe the correlation of nano-patenting activity with research funding (Grimpe and Patuelli 2009; Hullmann 2006), as well as to discover and ‘‘map’’ frequently occurring topics in nanotechnology patents (Huang et al. 2003, 2004; Igami 2008; Li et al. 2009; Liu et al. 2009). None of these studies, however, were tailored for the nanotechnology EHS community, which is concerned with identifying emergent consumer and industrial uses of nanotechnology, the types and chemical composition of ENMs they contain, and assessing likely life-cycle ENM releases from products during manufacture, use and disposal stages. The goal of this research is to provide an overview of nanotechnology IP trends, complimentary to the PEN consumer product inventory, to help identify potentially ‘‘risky’’ nanomaterials for study by the nano-EHS community. We identify common nanoenabled products and applications from the past 10 years of patented inventions and particularly strong upward and downward trends in patented producttypes over the past 5 years. Nanotechnology patents’ material-type and chemical-element compositions are also assessed in order to better understand the use of toxic materials by this industry and the use of rare elements, which may be energy-intensive to extract and therefore be valuable for end-of-product-life recycling. This aggregated information is used to identify emerging nano-functional materials with the greatest need for environmental toxicity and riskassessment studies.
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Methods Gathering patent and product data For this study, which endeavored to identify any invention incorporating nano-scale materials, a keyword search of the patent ‘‘Claims’’ section was the chosen. Two Boolean terms were designed, influenced by Huang et al. (2003, 2004), Kostoff et al. (2006, 2007), and Maghrebi et al. (2010), with key differences being a reduced emphasis on microscopy and the inclusion of a word-proximity search to help collect documents with (intentionally or unintentionally) obfuscated language. The goal for the first search term was to gather only patents specifically referring to engineered nanoparticles (ENPs) with 2- or 3-dimensions at the nano-scale, and the second term sought to include all patents from the previous search, plus those with any nanotechnology-related claim. Inspection of results indicated no irrelevant patents were returned with these search terms. A common weakness of keyword searches should be noted here; it is easy to inspect for false positives (irrelevant hits), but difficult to determine the rate of false negatives (how many relevant documents were missed out of millions of patent applications and grants). Our 10-word-proximity search would catch the phrase ‘‘25-nm TiO2 particles’’ but not ‘‘Particles of TiO2, which vary in surface chemistry, crystalline structure, and fabrication method, with diameter in the 10–25 nm range.’’ We settled on our final search terms by reviewing previous authors’ nanotechnology keyword searches to make sure we were not missing any obvious terms, then choosing our proximity (10 or 15 words regardless of whether they appear in the same sentence) based on other authors’ search methodology and some basic trial-and-error to identify and minimize irrelevant hits. Patent volume recall was comparable with other studies searching the patent ‘‘Claims’’ section (Huang et al. 2004; Li et al. 2009; Porter et al. 2007). See the Supplementary Material for the full queries. Using the above method, nanotechnology and nanoparticle patent documents were identified from three IP sources: United States Patent and Trade Office (USPTO) Grants, USPTO Applications, and World Intellectual Property Organization (WIPO) Documents. The USPTO was chosen in lieu of other toptier global IP offices, such as the Japan Patent Office (JPO), the European Patent Office (EPO), the Korea
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Intellectual Property Office (KIPO), and China’s emergent State Intellectual Property Office (SIPO), due to its consistently high volume of patenting activity over the past 10 years (second only to the JPO in total grants) and its high assignee diversity (51 % of 2009 patent applications came from non-US residents; the highest non-resident percentage in the top-five offices). The WIPO was chosen for its status as a publishing-house which quickly reserves international ‘‘priority’’ for an invention and its assignee (WIPO 2012a). In theory, each of WIPO’s 185 member states honor WIPO IP documents by granting national-level patents only to the original WIPO assignees. Patent data gathered from WIPO may therefore be considered as both a counter-point to USPTO patent data, and a source of slightly more recent IP, as it has increasingly become one of the first patent offices to which internationally relevant inventions are submitted (Bawa 2004; Grupp and Schmoch 1999). The patent search and analytics firm, Landon IP, executed the queries in March 2011. Because this study is focused on current and emerging nanotechnology trends, patent documents published in the year 2000 and later were collected. It should also be noted that a full forward-citation count, which is the number of times a patent is cited by subsequent patents, was not included in these datasets. This metric is often used by social scientists or economists to weight the relative knowledge- or monetary-value of individual patents in IP studies (Bass and Kurgan 2009; Hall et al. 2011; Meyer et al. 2010; Trajtenberg 1990).1 We considered the simple patent-count analyses undertaken in this exploratory study adequate because citation-weighting for recent patents or applications (\5 years since publication) requires extrapolation with high uncertainty, and an analogous method to weight the importance of individual PEN consumer products does not currently exist. The PEN inventory of nano-enabled consumer products was provided directly by the Woodrow 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).
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Wilson International Center for Scholars. Beginning in 2005, Center researchers used ‘‘systematic webbased searches’’ to compile and publish an inventory of products available for consumer purchase and advertised as employing nanotechnology. Additional criteria for inclusion in the inventory were that the product should be readily viewable on the internet and the nanotechnology claims should be subjectively reasonable (PEN Website 2012a, b), so that the database may be validated by anyone with an internet connection. The Woodrow Wilson Institute clearly states in its publications and online resources that the PEN database is not intended to be a comprehensive list of all worldwide products containing nanotechnology. Despite this admitted lack of comprehensiveness, the PEN inventory is the largest compiled list of nano-enabled consumer products that is publicly available, and researchers around the globe frequently cite it as justification for their environmental and public policy studies. PEN’s unbiased nature (PEN Website 2012b) and the inventory’s frequent use by the academic community are the reasons it was selected for comparison with IP inventories. The PEN inventory analyzed in this study was updated in March 2011 and includes 1,317 products. Data mining There were two main patent data-mining goals: (1) to extract the material-types and elements used in nanoenabled inventions and (2) to identify the products and processes improved by them. Patent abstracts, rather than claims, were chosen as the source of information for the materials analysis to ensure only the most relevant invention materials were represented. The Claims sections very often listed materials and elements inventors did not intend to use in their invention, along with the materials they did intend to use, probably to avoid patent infringement issues. Alternatively, authors sometimes list entire sections of the periodic table in an attempt to make their invention claim as broad as possible. Given this, we decided to mine the abstracts because they pulled relevant data more consistently despite the fact that the abstracts are indeed more general and contain less materials data. The condensed nature of the abstracts also made text mining less computationally burdensome than if the lengthy claims sections were chosen for analysis. For the product/ process analysis, primary and secondary International
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Patent Classifications (IPCs) were mined. The computer programs used for data-cleaning and vector-based manipulation of abstract and IPC data were: – –
–
–
Microsoft Excel 2007 Weka v.3.6.4, an open-source machine-learning software developed at the University of Wakaido (Hall et al. 2009) Summarization integrated development environment (SIDE), a Weka add-on developed at Carnegie Mellon University NodeXL Version 1.0.1.210, a network-graphing open-source template for Microsoft Excel supported by the Social Media Research Foundation (Hansen et al. 2010) Methods employed for each are described below.
Materials identification To determine the chemical composition of nanoenabled products within the patents the appearance frequency of material-types and elements in the patent text must be determined. To find this, standard bag-ofwords vectors were created for each of the six patent datasets: Patents were scanned for words appearing in five or more invention abstracts (with stemming off, minus stopwords, such as ‘‘and,’’ ‘‘the,’’ ‘‘to’’). In this way, approximately 10,000 unique commonly appearing words were identified per patent database. Then, each text abstract was transformed into a word vector with 10,000 binary dimensions, each dimension representing whether that particular word appeared in the abstract (‘‘1’’) or not (‘‘0’’). Simple analysis of the vectors provided, for each word, a count of how many patent documents it appears in, and which other words it commonly appears with. All materials-specific words were identified from the 10,000, and extensive association-rule analysis was performed to remove synonyms in the same abstract and reduce any ambiguity caused by multi-word materials terms. This ensured that, if the words ‘‘carbon-nanotube’’ and ‘‘CNT’’ appeared together in the same abstract, it was only tallied once when counting the total number of patents containing carbon nanotubes. Similarly, when an element such as ‘‘tungsten’’ appeared, checks were made to see whether it appeared alongside ‘‘oxide,’’ ‘‘nitride,’’ or the abbreviation for any tungsten-containing compounds. Finally, words like ‘‘lead’’ and ‘‘tin’’ were disambiguated, as these could refer a conductive connector or TiN
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(titanium nitride), respectively. Materials-category analysis was performed using the cleaned data. USPTO materials-word vectors were also consolidated into element vectors, the vectors representing individual patents with 92 binary dimensions (or ‘‘attributes’’), with each dimension representing a chemical element. A ‘‘1’’ was assigned to a dimension if the particular element appeared in the patent abstract, and a ‘‘0’’ if not. These vectors were then analyzed to compare elements’ abundance in nanopatents with their earth abundance and to examine common co-occurrences of elements. To visualize elements’ appearance together in USPTO 2000–2011 nanotechnology patent grants, a Fruchterman–Reingold (FR) network diagram was created using the open-source template for Microsoft Excel, NodeXL.2 The FR algorithm (Fruchterman 1991) assigns each element to a single ‘‘vertex’’ which uniformly repels all other vertices. If two elements are mentioned in the same patent abstract, either as part of a compound or separately, their two vertices are connected by an ‘‘edge.’’ In this case, the strength of the ‘‘attractive’’ edge connection (roughly analogous to a spring constant) is proportional to the number of patent abstracts in which the two elements appear together. Vertices are first laid out randomly in a bounded plane, then the FR algorithm iterates toward a graph layout that optimizes distances between all vertices by balancing the forces of repulsion and attraction. In the case of these patent element datasets, the diagram stabilized after 40,000–50,000 iterations and element groupings were consistent regardless of the random starting layout.
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(designating the technology Section, Class, and Subclass) contain sufficient granularity for this study. Similar to the materials analysis, binary vectors were created for each patent document describing their assignment to each Section, Class, and Subclass level. This allowed a quick summation of the vectors to determine the number of patents assigned to each IPC.3 To compare nano-enabled product patents with the PEN inventory, it was necessary to classify all 1,317 PEN products using 4-character IPC Subclasses. A prior categorization made by researchers at the Wilson Center was incompatible because it defined producttypes, but did not specify the functionality nanotechnology provides. For example, under PEN’s system a cell phone with a nano-enabled microprocessor falls in the same category as a cell phone with a silver-ENPcoated case, ‘‘Mobile Devices and Communications.’’ In contrast, the IPC allows classification of PEN products not only by industry but also by nanofunctionality. The Samsung Anycall E628 was given three IPC codes: H04M (cell phone) ? A61L (antimicrobial) ? C09D (coating), which distinguished it from the Apple iPhone: H04M (cell phone) ? G06F (digital data processing) ? H01S (stimulated emission) ? H01L (semiconductor). This strategy provides a better comparison to the nano-patents and a fresh look at the PEN product data. Once classified, the product IPC dataset was evaluated by the same method as the patents IPCs, and comparisons between datasets were made at the Section, Class, and Subclass level.
3
Product/process identification The IPC is a hierarchical categorization system employed by most major patent offices to identify a patent’s ‘‘technology area,’’ and was used in this study to evaluate product and process trends in all datasets. A patent is typically assigned one ‘‘Primary’’ and several ‘‘Secondary’’ IPC classes by the inventors and patent examiner; in this study, each assignment was treated with equal value. Though classification codes contain between 10 and 12 characters, the first four 2
A network was also created for the USPTO 2010 Applications dataset, but clustering was so similar it did not warrant separate reporting.
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.
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Results and discussion A high-level analysis of the extracted nano-enabled patents (Fig. 1) reveals nanotechnology-related inventions accounted for 1.4 % of all USPTO Grants, 1.3 % of all USPTO Applications and 1.4 % of all WIPO documents during the first decade of the new millennium (USPTO 2012; WIPO 2012b). The full-scale nanotechnology search gathered substantially more inventions than the ENP search alone, illustrating the relative importance of inventions with nano-scale features but not containing nanoparticles. One notable difference between the WIPO and USPTO patent data is the ratio of ENP-specific documents to total nanotechnology documents, with ENPs accounting for 39 % of the nanotechnology patents in the USPTO, and 70 % in the WIPO dataset. This substantial gap is most likely explained by dissimilarities in IP producttypes submitted to USPTO and WIPO and their respective NP-appearance ratios, illustrated in the next sections. A noteworthy feature of Fig. 1 is the small number of advertised nano-enabled consumer products relative to the number of patents. This disparity highlights two commonly recognized limitations of patent research as a market indicator; most patents will never be manufactured and it is difficult to predict which IP will be successful. Scholars have estimated that only 5 % of all U.S. patents are licensed (Lemley 2001), and a similarly limited number of granted patents are
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eventually produced (Allison et al. 2004; Trajtenberg 1990). Not only are the licensing and manufacturing rates low, but raw patenting activity is not correlated with production. Research shows that the reasons firms patent their inventions, or avoid patenting them, differ widely by industry (Cohen et al. 2000). It stands to reason that the percentage of patents actually used in manufacturing also varies by industry, confounding patenting-to-production correlations in cross-discipline fields such as nanotechnology. Another correlation-obscuring factor frequently mentioned by economists is the long and variable lag between patenting and production (Branstetter 2001; Brouwer and Kleinknecht 1999; Gort and Klepper 1982; Pakes and Schankerman 1984). Commercialization of nanotechnology inventions also could be affected by a ‘‘patent thicket,’’ a phenomenon detailed by Lemley (2005) and Makker (2011) in which early, extensive, and overlapping patenting of fundamental nanotechnology building-blocks slows industry market-entry due to limited licensing and threat of litigation. The non-comprehensive nature of the PEN inventory also contributes to the size disparity between the patent and product datasets. A company may have little reason to advertise the inclusion of a newly patented technology when it is used solely in internal manufacturing processes (as a degreasing agent, for example), or when the technology is a small component of a complex product (a printed circuit in an electronic device). Finally, there is some hesitancy among nanotech companies to label products as nanoenabled. This is driven by both consumer perception of nanotechnology and EHS regulatory concerns (Andersen 2011). These factors not only affect the dataset size but also seem to play a role in the divergence of PEN and patent product-type distributions below. Product-type analysis
Fig. 1 Inventions containing Nanotechnology, published Apr 2001–Mar 2011. Overview of IP documents retrieved using two lexical search methods. Total column height represents the number of inventions gathered by the ‘‘Nanotechnology’’ Boolean search. PEN consumer products not sorted by NP-use
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Patents and the PEN inventory were examined at three hierarchical levels of their assigned IPCs. At the most aggregated ‘‘Section’’ Level of the IPC there are eight main technology categories to which patents are assigned (coded by the colors named below in all figures): • •
A—Human Necessities (Red) B—Performing Operations and Transportation (Blue)
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A
A A
90%
B B
A
80%
B C
70%
C 60%
C
% Representation of Section-Level Assignments in the International Patent Classification (IPC) System
B
50%
G
amongst Nanotech Inventions G
40%
G
30%
20%
C
H H
D H
10%
H
0%
US Patent Grants
A: Human Necessities
US Patent Applications
WIPO Documents
PEN Product Inventory
4.15%
7.85%
14.41%
37.43%
B: Transportation and Perf orming Operations
14.24%
17.80%
15.38%
22.89%
C: Chemistry/Metallurgy
19.00%
20.97%
26.88%
19.89%
D: Textiles/Paper
1.80%
1.88%
2.24%
10.25%
E: Fixed Constructions
0.29%
0.34%
0.45%
1.75%
F: Mechanical Engineering/ Lighting/ Heating/ Weapons/ Blasting
1.44%
1.57%
1.76%
1.71%
G: Physics
22.11%
18.05%
16.69%
2.01%
H: Electricity
36.95%
31.53%
22.19%
4.06%
Fig. 2 Practical applications of 2000–2011 nanotechnology inventions: section-level IPC assignments for 2000–2011 of patents and PEN products, displayed on a percentage basis
• • • • • •
C—Chemistry and Metallurgy (Gray) D—Textiles and Paper (Black) E—Fixed Constructions (Yellow) F—Mechanical Engineering, Lighting, Heating, Weapons and Blasting (Teal Green) G—Physics (Brown) H—Electricity (Green)
Examination of section-Level IPC assignments for nanotechnology inventions (shown in Fig. 2) shows a stark difference between nano-enabled technologies
that are patented and those that are marketed to consumers.4 Physics and Electricity applications make up a substantial portion (40–60 %) of all nano-enabled patents, but only 6.1 % of products in the PEN Inventory fall in those categories. Conversely, products categorized as Section D (Textiles and Paper) or 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.
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Section A (Human Necessities, which include foods, household goods and appliances, clothing, recreational items, toiletries, cosmetics, and medicine) appeared five or ten times more often in the PEN inventory than the USPTO Grants, respectively. These categories contain products with obvious potential for human and environmental exposure. The likelihood of exposure from products under the Physics and Electricity category is less clear, but conceivably could result from occupational exposure, manufacturing, waste disposal, recycling, or other end-of-life treatments. Figure 2 suggests PEN and patent data are similar in Sections B, C, E, and F, since the percentages of product-types falling in these categories are similar across all datasets. However, more in depth analysis at the Section and Class levels show that the IP data and the PEN data cover different parts of the nanotech product space. Examination at the class level (Fig. 3) shows PEN and IP technology types differ substantially within Sections B and C. Patents categorized in Section B most often describe manufacturing processes (B32, B01, B05), whereas PEN commercial products with Section B assignments are typically for vehicular improvements (B60) or cleaning applications (B08). Within Section C, coatings (C09, C23) are a commonly employed nano-enabled technology for all datasets. Patented inventions, however, contain novel inorganic, organic, and biological chemistries (C01, C07, C12) far more frequently than commercial products, where nano-enabled ceramics (C04) are proportionally more important. The top 15 IPC classes for USPTO Nanotechnology Grants (Fig. 3, Column 1) were analyzed to determine the percentage of grants in each class containing nanoparticle (NP)-specific terminology (Fig. 4); the percentages varied widely. Categories of inventiontypes that more frequently contain NPs include polymer compositions (C08, C09), new chemistries (C01, C07) and surgical, medicinal, hygienic, and cosmetic applications (A61). On the other hand, electronics, information technology, and optics inventions submitted to the USPTO tended not to contain traditional NPs in their descriptions, but instead employed nano-scale coatings, layers, or surface treatments. Such frequent patenting of these inventions suggests the EHS community may benefit from investigation of life-cycle durability of nano-scale layering and coating applications. In many electronics, information storage, and optical applications,
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likelihood of consumer exposure to released nanomaterials in the use-phase is low. In the manufacturing, recycling, or disposal stages of these products’ lifecycles, however, it may be prudent to explore factors affecting flaking or friability of nano-scale layers. The final patent utility analysis in this study focuses on USPTO Applications published between January 2006 and March 2011. In that time, 37,150 nanotechrelated patent applications were published, increasing linearly each year from 5,680 in 2006 to 8,800 in 2010. The additional 3 months of 2011 applications (2,090 in all) were aggregated with the 2010 data in the following trend analysis and figures. IPC trends are reported on a percentage basis to normalize both the expansion of patenting activity and the systemic yearly increase in IPC-Subclass assignments per patent; in 2006 inventors and examiners assigned each nanotechnology patent application to 1.66 different 4-character IPC Subclasses on average and by 2010 this figure rose to 1.88. High-level IPC Section trends (Fig. 5) confirm the dominance of electrical applications among nanotechnology inventions, but reveals a slight decline in percentage of total applications in the past 5 years, a trend also displayed by Physics applications. Conversely, there are slight increases in Human Necessities and Chemistry applications, and a stark increase in Section B ‘‘Performing operations’’ nanotechnology patent applications. To determine what particular fields are driving these increases and declines, the 637 IPC Subclasses are examined for trends. To examine the proportion of applications assigned to each IPC Subclass across 5 years, Subclasses with the strongest trends (selected by high R2 value) were plotted in Figs. 6, 7, and 8. Figures 6 and 7 show trending ‘‘high-volume’’ technology types that appear in more than 1 % of nanotechnology patent applications. Figure 8 displays upward trends for ‘‘emergent’’ technology types assigned to at least 40 nanotech applications over the 5-year time period. As in the Section-Level graph, patent application percentage decreased for some electronics and information technologies (Fig. 6). Of particular note are the relative importance of semiconductor and solid-state devices (H01L), which account for a full 30 % of all inventions in this time period. Innovation also decreased significantly for information storage mechanisms (G11B, G11C) between 2006 and 2010. It is important to note, with the exception of G11B, all negative
G11 Information Storage G02 Optics G01 Measuring; Testing C08 B32
Organic Macromolecular Compounds/ Compositions Layered Products
C01
Inorganic Chemistry
C09
Dyes; Paints; Polishes; Natural Resins; Adhesives
B01
Physical Or Chemical Processes Or Apparatus
A61 Medical or Veterinary Science; Hygiene C23
Coating Metallic Material; Metallic Coatings; Chem. Surface Treatment; Corrosion Inhib.
2
0.0620
8
0.0536
C12 C04
Cements; Concrete; Artificial Stone; Ceramics; Refractories
21
B29
Working Of Plastics; Working Of Substances In A Plastic State
22
B60
Vehicles In General
D06
Treatment Of Textiles; Laundering; Misc. Flexible Materials
Furniture; Domestic Articles Or Appliances; Suction A47 Cleaners A41 Wearing Apparel A45 Hand Or Travelling Articles
ts 0.0048
0.0012
0.0006
0.0782
6 0.0014
89 0.0002
0.2187
9
63
95
0.0456
3 0.0014
0.0009
0.0003
0.2027
14
68
81
uc
0.0048
0.0021
0.0010
0.1078
4
65
67
NP ro d
0.0047
0.0018
0.0019
0.0562
5
63
53
0.0003
0.0162
0.0023
0.0020
0.0850
13
36
55
96
0.0138
0.0043
0.0022
0.0023
7
38
51
91
0.0646
0.0251
0.0049
0.0000
51
20
42
69
0.0392
0.0128
0.0169
0.0623
75
22
17
52
0.0183
0.0408
0.0181
0.0008
12
7
24
51
0.0483
0.0195
0.0204
0.0425
58
11
13
47
0.0218
0.0638
0.0216
0.0121
16
18
18
38
0.0234
0.0373
0.0260
0.0273
33
10
8
16
0.0344
0.0327
0.0270
0.5581
19
16
14
15
0.1815
0.0364
0.0361
0.0676
1
15
16
** Inventions are often assigned to more than one class; therefore percentages add to more than 100%
0.2361
0.0732
0.0490
* H01 includes: -- Semiconductors and other solid state devices, -- Conversion of chemical energy into electrical energy, e.g. batteries -- Electric discharge tubes or discharge lamps -- Devices using stimulated emission -- Conductors; insulators; dielectrics
10
12
15 0.0468
2
2 0.1029
0.0487
0.0023
0.0620
5
11
14
0.0688
0.0536
0.0526
0.0152
51
8
2
11
0.0332
0.0513
9
10
19
Cleaning
10
0.0645
29
6 0.0468
0.0567
9
0.0933
0.0929
0.0577
0.0061
11
13
12
Fraction of Patents/ Products Assigned**
0.0053
46 0.1150
0.0690
(out of 129 classes)
48
4
3
7
Biochemistry; Microbiology; Enzymology; Genetic Engineering; Beer; Spirits; Wine
B08
0.0800
0.0629
0.0167
0.0560
3
6
6
Nano-technology
A63 Sports; Games; Amusements
PE
0.0806
27
9
Appearance of Top 15 Product "Functions" In Nanotechnology Patent/Product Data Sets, By International Patent Class (IPC) Rank
0.0456
0.0246
0.0695
4
5
B82
14
5
4
0.2793
0.0666
0.1003
13
B05
14
7
3
Misc. Electric Techniques Employing Static H05 Electricity, Printed Circuits, X-Ray, Plasma
Spraying Or Atomising In General; Applying Liquids To Surfaces
1 0.4249
0.1007
12
Organic Chemistry
1 0.5121
Photography; Cinematography; Electrography; G03 Holography
C07
W Do IPO cu me nts
PT Gr O an ts
1
A p U SP pli TO cat ion s
H01 Basic Electric Elements*
Page 9 of 23
US
Co
IP CC
las s
Int de er Pa natio t en n (IP t Cl al C) ass
J Nanopart Res (2012) 14:1283
0.1048
8 0.0004
0.0797
Fig. 3 Appearance of Top 15 IPC classes in nanotechnology patents and PEN product datasets, including primary and secondary IPCs
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Fig. 4 Proportions of NP-containing nanotechnology patents: top 15 classes assigned to USPTO Nanotechnology Grants. Dataset average = 39 %, class pie-size relative to percentage of nanotechnology patents assigned
Fig. 5 IPC Section-level trends for 2006–2010 USPTO nanotechnology patent applications
Subclass trends occurred on a percentage basis only and did not represent a decrease in absolute numbers of patent applications per year. This indicates these downward trending nanotechnology fields may not be waning so much as others are accelerating.5 The 5
A table showing absolute counts of IPC Subclasses assigned to 2006–2010 USPTO Applications is presented in the Supplementary Material.
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decrease in nano-enabled hard disk drive (G11B) patent applications between 2006 and 2011 is not only anomalous; it seems counter-intuitive considering the on-going densification of information storage (Kryder 2009; Powell 2008; Walter 2005). A likely explanation for this drop in G11B patents is a concurrent reduction in patenting organizations; over this time period, six major magnetic disk drive manufacturers consolidated into three. Despite this anomaly,
J Nanopart Res (2012) 14:1283
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Fig. 6 Downward trends in frequently assigned IPC Subclasses for 2006–2010 USPTO nanotechnology patent applications
examination of other patenting trends can provide a window into various industries’ valuation of nanotechnology. Aside from generally supporting sectionlevel IPC trends, upward-sloping Subclass data (Figs. 7 and 8) reveal a rush by the nanotechnology industry to patent in a few broad, cross-discipline fields: (1)
Medicine and biotechnology (A61K, A61P, C12N, C07H, A01N)
(2) (3)
Layering, coating, and other surface treatments (B32B, B05D, C23C, H05K, C25D, B05C), and Manufacturing processes and methods (B05D, C23C, B01J, B29C, C08F, H05K, C25D, E21B, B05C, B23P, D03D)
A mid-2000s upward bio-nanotechnology trend has been documented by previous authors (Islam 2010; Roco et al. 2011; Takeda et al. 2009). Here, we also
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Page 12 of 23
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Fig. 7 Upward trends in frequently assigned IPC Subclasses for 2006–2010 USPTO nanotechnology patent applications
show the nanotechnology industry’s escalating interest in ‘‘1-nano-dimension’’ configuration such as layers and coatings and its increased patenting of manufacturing processes. The above trends as well as the prevalence of the electronics and information technology patenting are corroborated by the following materials and element analysis.
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Materials analysis The Wilson Center summarized PEN inventory materials in a March 2011 online update: The most common material mentioned in the product descriptions is now silver (313
J Nanopart Res (2012) 14:1283
products). Carbon, which includes fullerenes, is the second most referenced (91), followed by titanium (including titanium dioxide) (59), silica (43), zinc (including zinc oxide) (31), and gold (28). - http://www.nanotechproject.org Materials data were extracted from just 43 % of inventoried nano-enabled products due to the vague nature of most PEN product advertisements. We reviewed abstracts from 2000–2011 USPTO Nanotechnology Grants and USPTO Nanotech Applications published Jan 2010–March 2011 for all materials employed in the inventions.6 By comparison, specific mineable materials details existed in 73 % of the USPTO Grant abstracts and 75 % of the USPTO Applications abstracts. The particular patent datasets were chosen to compare general use of materials in nanotechnology (represented by the full decade of USPTO grants) with those in emergent technologies (the recent year of USPTO applications). Categories of materials patented First, all abstract materials-words (disambiguated as described above) were assigned to specific categories. Then the categories were graphed by appearance frequency in the patent abstracts (Figs. 9, 10). Top materials associated with each category are presented in Table 1. Throughout the decade, 29 % of materials mentioned in USPTO Nanotechnology Grants were carbon based. By 2010, however, this proportion rose to 40 %. Each carbon-based category grew by at least 10 % compared to its 2000–2011 Grant value, including a 140 % increase in the usage of biological molecules. Materials-categorization results generally support IPC utility statistics examined previously. Given the 2006–2011 increase in coating, layering, surface treatment, and bio-nanotechnology applications, the 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 Mene´ndez-Manjo´n’s (Mene´ndez-Manjo´n 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.
Page 13 of 23
concurrent rise in use of associated materials, such polymers, biomolecules, and other organics, is consistent. The 5-year downward trends in electronics applications seems to parallel a decrease in the appearance of semiconductors and ceramics. Increased use of inorganic carbon materials, specifically carbon nanotubes (CNTs), also correlates with the IPC trends. CNTs appeared in 7.3 % of all nanotechnology patents issued over the past decade and accounted for 10.0 % of 2010–2011 patent applications.7 Across both patent datasets, CNTs appear most often in semiconductor and solid-state devices (H01L), but this fraction decreases over time. Increase in the overall patenting of CNTs is accounted for by their increased use in layering technology (B32B), conductive or dielectric materials (H01B), and batteries or fuel cells (H01M). All of these IPC Subclasses showed positive trends in 2006–2011 USPTO Nanotechnology Applications. In the biological sector, protein usage in nanotechnology inventions rose from 0.7 % of 2000–2011 USPTO Grants to 1.97 % of 2010 USPTO Applications; most of the increased protein usage was seen in medical applications under Subclass A61K. As for metals, aluminum, the most commonly appearing metal in both datasets is used infrequently as an ENP, but is regularly employed as a layer, film, substrate, dopant, catalyst, or electrode in nanoenabled technologies, most often in semiconductors (H01L) and in layering applications (B32B). Recent nano-enabled inventions using aluminum metal include more batteries and conductors (H01M, H01B) and fewer information storage applications (G11B) in comparison to the past decade of granted patents (in both absolute numbers and on a ranked percentage basis). Trends in chemical-element appearance Figure 11 displays nanotechnology inventors’ use of elements as compared to their relative abundance (mass basis) in the earth’s crust (Lide 2005) (see the Supplementary Material for the equivalent
7
Between 2001 and 2011, the annual percentage of nanotechnology inventions which contain nanotubes increased significantly in both the Applications and Patent datasets (R2 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.
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Fig. 8 Emergent IPC Subclasses for 2006–2010 USPTO nanotechnology patent applications
sea-abundance graph). Elements were ranked between 1 and 92, higher numbers corresponding to greater abundance. Abundances in patents were calculated by summing element vectors for the 2000–2010 USPTO Grants and the 2010 USPTO Applications datasets, providing for each element a total number of occurrences in each dataset. The earth abundance is reported on a mass basis, so for comparison purposes, the
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calculated patent element occurrences were multiplied by their atomic weight. Elements deemed toxic or carcinogenic by the US Environmental Protection Agency or by the International Agency for Research on Cancer (IARC) were highlighted. Since carcinogenicity of certain elements is an active area of research, only elements listed in IARC Groups 1 (‘‘Carcinogenic to humans’’) or 2a (‘‘Probably carcinogenic to
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Fig. 9 Materials referenced in Abstracts of 1/2000–3/ 2011 USPTO nanotechnology patent grants
Fig. 10 Materials referenced in Abstracts of 1/2010–3/2011 USPTO nanotechnology patent applications
humans’’) were highlighted (IARC Website 2012). A similar cut-off was made for EPA-identified toxic elements; only those with EPA Reportable Quantities or Agency for Toxic Substances and Disease Registry (ASDTR) Toxicity/Environmental Scores of 1, 10, and 100 kg were identified (ATSDR Website 2012).
An initial observation from Fig. 11 is that seven of the top 10 elements named in nanotech patent grants appear in the bottom 50 % of earth’s crust abundance rankings (the sum of the bottom—50 % element abundances account for less than 30 ppm of the earth’s crust). Very highly patented elements in contrast to
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Table 1 Major materials-science categories and frequently appearing specific materials within USPTO nanotechnology patent grants and patent applications
Carbon-based: Biological molecules
Carbon-based: Synthetic polymer
Carbon-based: Organic molecules (non-polymer)
Carbon-based: Inorganic/crystalline carbon
Carbon-based: Organo-silicons
Not carbon-based: Ceramic/Glass
Not carbon-based: Semiconductors
Not carbon-based: Metal
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USPTO Grants (1/2000–3/2011)
USPTO Applications (1/2010–3/2011)
Top 5 materials
Top 5 materials
% of patents
% of patents
Protein
0.70
Protein
1.97
Nucleic
0.52
Nucleic
1.14
Cellulose
0.41
Peptide
1.10
Peptide
0.32
Biomolecule
0.69
Oligonucleotide
0.31
Amino
0.58
Resin
3.15
Resin
2.67
Plastic
1.05
Copolymer
1.38
Thermoplastic
0.86
Thermoplastic
0.98
Copolymer
1.04
Plastic
0.84
Polyester
0.66
Gel
0.51
Alkyl Hydrocarbon
0.85 0.87
Hydrocarbon Alkyl
1.07 1.00
Surfactant
0.74
Surfactant
0.95
Ester
0.48
Oil
0.79
Alcohol
0.45
Amine
Nanotube
7.32
Nanotube
Fullerene
1.02
Fullerene
1.01
Diamond
0.83
Diamond
0.79
Graphite
0.76
Graphene
0.65
Carbide
0.67
Graphite
0.63
Silane
0.46
Silane
0.50
0.66 10.01
Silicone
0.33
Silicone
0.47
Teos
0.12
Polysilicon
0.23
Alkoxysilane
0.12
Siloxane
0.17
Siloxane
0.11
Polysiloxane
0.12
Silica Metal oxide
4.53 2.18
Silica Metal oxide
3.39 2.41
Titania
1.20
Titania
1.79
Alumina
1.16
nanotube (non-carbon)
1.00
Nitride (silicon)
1.13
silicate/clay
0.83
Silicon
8.31
Silicon
5.00
Germanium
1.32
CaN
1.13
GaN
1.26
Zinc oxide
0.76
Polysilicon
0.97
Germanium
0.71
GaAs
0.85
Metal-oxide-semicond.
0.62
Alloy
3.18
Aluminum
2.03
Aluminum
2.76
Alloy
1.77
Titanium
1.92
Silver
1.46
Copper
1.78
Copper
1.43
Cobalt
1.34
Lithium
1.24
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Table 1 continued
Not carbon-based: Non-metal non-carbon
Not carbon-based: Phosphors
USPTO Grants (1/2000–3/2011)
USPTO Applications (1/2010–3/2011)
Top 5 materials
Top 5 materials
% of patents
Water
4.06
Water
5.20
Oxygen
3.37
Oxygen
3.16
Hydrogen
2.39
Hydrogen
2.45
Nitrogen
2.09
Nitrogen
1.74
Boron
1.08
Phosphorus
0.57
Phosphor
1.06
Phosphor
1.01
Nanophosphor
0.02
Nanophosphors
0.03
Thioaluminate
0.02
zn2sio4mn
0.01
zns–siosub2
0.01
their abundance8 include precious metals (palladium, silver, platinum, gold), metalloids (germanium, arsenic, antimony, tellurium), and select transition metals (gallium, molybdenum, rubidium, cadmium, tin, hafnium, tantalum, tungsten, iridium, bismuth).9 Conversely, some of the world’s most plentiful elements, particularly the alkali metals, are rarely mentioned. Rare-earth elements (e.g., Nd, Y), most commonly appearing in Subclasses C09K, H01L, B32B, A61K, are also noticeably under-patented by the USPTO, especially given their varied and frequent usage for technical, medical, and industrial applications. One possible explanation for this dearth of rareearth metal-containing nanotechnology inventions is a geographical bias; their most active mining and R&D occurs in China (Fifarek et al. 2008; USGS Website 2012) but the USPTO and WIPO datasets traditionally contain few patents from Chinese inventors compared to the SIPO. Another speculation is rare-earth-containing nanotechnology inventions are veiled by trade secrecy to avoid licensing or legal battles.
8
% of patents
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.
Element ranks for the USPTO Grants and Applications datasets track closely to each other with few exceptions. Figure 11 shows recent nanotechnology patent applicants used phosphorus, sulfur, europium, and selenium more often than in the previous decade. Phosphorus and sulfur are most often found in bionanotechnology applications, europium in semiconductor applications, and selenium has seen a recent boost in layering applications. On the other hand, strontium, chlorine, thorium, arsenic, and rhodium became less common by 10 ranks or more. Much of the decline of arsenic-, strontium-, and rhodiumcontaining patent applications may be attributed to a reduction in their use for electronics inventions (H01L or G11B), and chlorine appeared far less often in chemistry-related patents manipulating inorganic (C01) or macromolecular (C08) materials. Of the 19 toxic or carcinogenic elements considered here based on the criteria above, the majority were not patented frequently enough to rank in the top 50 % of USPTO Grant or Application element abundance. Exceptions to this (and their most common IPCSubclass assignments) were copper (H01L), arsenic (H01L, H01S—Devices using Stimulated Emission), cobalt (G11B, B32B), nickel (H01L, G11B), lead (H01L), palladium (G11B, H01L, B01J), chromium (G11B, B32B), and cadmium (H01L). Considering the extensive overlap in technological applications, a deeper look was taken to determine whether these toxic elements commonly occur together in the same inventions. This examination of element co-occurrence in patent abstracts was performed for the entire
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10-year USPTO nanotechnology patent grant dataset to gain perspective on general trends as well as on clusters of toxic or carcinogenic elements. Figure 12 displays the FR element network diagram in multiple variations; each with a different filter applied to highlight relevant elements.10 The first filter (Fig. 12a) isolates frequent connections between the most commonly appearing elements, those cited in 100 or more abstracts and appearing together 100 or more times. Although the graph only shows 47 elements, there is conspicuous clustering by element type. For example, core organic and biological elements (C, H, O, N, P, and S) appear together frequently, as do common semiconductor-compound elements (Al, Ga, As, In, Sn, N, and P). The most common ceramic oxides (Si, Al, Ti, and Fe) are identified by their close proximity to oxygen in the FR diagram. In addition, elements in the same periodic group or category tended to pair together in patents, particularly the 3d-subshell (first) row of transition metals with each other and with their respective groups (3 through 12). Nanotechnology inventions containing two or more highly connected elements from the list of transition metals: Ti, Cr, Fe, Co, Ni, Cu, Zr, Nb, Mo, Hf, Ta, and W, were likely to be classified as magnetic hard disk components (G11B), semiconductor or solid-state devices (H01L) or layered products (B32B). This holds true for pairing of toxic and carcinogenic transition elements as well. The second filter (Fig. 12b) shows IARC- or ASTDR-classified elements appearing in ten or more patents. Connections between Co, Cr, Pd, and Ni are of similar strength to those between other transition metals and are strongly associated with magnetic recording and layering patents (IPC Subclass G11B, H01F, B32B) and less frequently to semiconductor patents. Lead, cadmium, and arsenic were all most commonly found in H01L
10
Fig. 11 Ranked element earth abundance versus occurrence in nanotech IP documents
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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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Fig. 12 Co-occurrence of chemical elements in USPTO Nanotechnology Grant Abstracts from 2000 to 2010, graphed using FR algorithm, filtered in three ways: a all elements and cooccurrences with 100-patent threshold, b toxic and carcinogenic elements with a 10-patent threshold, and c dark red-colored lines connected to Ag represent 50 or more patent abstracts in which the elements cooccur. (Color figure online)
(semiconductor devices) patents. Compared to most other elements, arsenic was striking in its uniformity of application. Whereas most elements (toxics included) were assigned to a large variety of IPC Subclasses, arsenic appears very commonly in just two: semiconductor devices (H01L) in 49 % of all instances, and devices using stimulated emissions (H01S) in 34 % of all instances. Arsenic is paired with gallium in 85 % of patents because GaAs is a commonly used semiconductor material.
Nano-silver is the most commonly mentioned ENM in the PEN inventory and its co-occurrence with other elements is highlighted in Fig. 12c. Top uses of nanosilver in consumer products are antimicrobial, so one might expect Subclasses A61K (preparations for medical, dental, or toilet purposes) to feature prominently in patents which contain both Ag and the organic elements (C, H, O, and N). This is not the case, however, the most commonly assigned IPC Subclass is G03C, ‘‘Photosensitive materials for photographic
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purposes’’, representing 17 % of all nanotechnology patents containing Ag. Patents that mention Ag with other metals have similar product-types as the transition metals previously discussed; semiconductor devices (10 % of all silver patents), layered products (10 %), and hard drives (7 %). Nine percent of patented silver-containing nano-products are for use in optical elements or the surface treatment of glass. Medical and hygienic inventions make up just 3 % of all silver-containing nanotechnology patents in the past 10 years. This deconstruction of Ag patenting provides further evidence of the divergent nature of the consumer product and IP datasets. This materials and element investigation indicates that some important emerging ENM chemistries have been largely ignored by EHS studies, particularly the top eight most commonly patented toxic elements (Cu, As, Co, Ni, Pb, Pd, Cr, and Cd), and semiconductor or magnetic compositions likely to appear in layered electronic devices. Considering many of the applications typically associated with these materials (hard disks, micro processing chips, etc.) do not lead to high environmental or human health exposure during consumer use, emphasis should be placed on study of beginning- and end-of-life scenarios for these nanotechnology applications (manufacture waste streams, worker safety, recycling, and safe disposal). Manufacturing methods could be vetted using an approach similar to green chemistry ‘‘E-factor’’ analysis, as performed by Eckelman et al. (2008) for ENPfabrication. For example, recycling of these materials will typically include a shredding or grinding step, followed by recovery of valuable materials. Nanocoating or layer fragment release during recycling or other destructive end-of-life scenarios could be evaluated in a manner similar to Wohlleben et al.’s (2011) nanocomposite study.
Conclusions The patent literature and the PEN consumer product inventory paint substantially different pictures of the nanotechnology industry with respect to the types of emergent nano-enabled products and the ENMs they contain. This divergence suggests the environmental community would be prudent to consider both patent and consumer product trends when prioritizing nanomaterials for study. For example, the PEN
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inventory indicates silver is the most prevalent ENM in nanotechnology products. On the other hand, silicon and carbon nanotubes are the most frequently cited materials in nanotechnology inventions. Several other nano-functional metals, semiconductors, ceramics, and biomaterials are also patented more commonly than nano-silver. These ENMs, many of which are photoactive, magnetic, contain transition metals with low earth abundance, and/or contain elements with known toxicity, may merit study for environmental implications. Perspective from combined PEN inventory and IP product trends is also helpful to identify routes of entry for these ENMs into the environment. Classification of common types of nano-enabled products and their lifecycles is necessary to evaluate exposure potential, and the PEN and IP product-type portfolios identify a wide and somewhat divergent range of nanotechnology applications. The consumer product inventory is comprised heavily of hygiene and anti-fouling coating applications, however, most nanotechnology-related inventions between 2000 and 2011 were in the fields of electronics and information technology. Semiconductor and solid-state device patents were particularly abundant, accounting for 38 % of all USPTO nanotechnology patent grants, but in the last 5 years innovation has spiked for nano-enabled biotechnology, layering and coating technologies, and manufacturing processes. This diversity of nanotechnology applications across consumer and patent datasets shows a more comprehensive landscape of the industry, and furthermore identifies EHS research areas for potential exploration by environmental scientists. For example, little has been published evaluating potential environmental implications of nano-enabled biomedical products or ENM manufacturing techniques. In addition, nearly 50 % of studied WIPO and USPTO nanotechnology IP dealt with nano-scale layers, coatings, or other surface modifications, but employed no traditional nanoparticles (with 2 or 3 nano-scale dimensions). The volume of patenting in non-ENPbased layering and coating nanotech applications has not been commensurate with research on the life-cycle of these products. These thin polymeric, ceramic, or semiconductor layers may be subject to flaking or friability during product manufacture, use, and end-oflife phases, yet factors affecting nanomaterial release and potential for human or environmental exposure are not well-understood. In the case of nano-enabled
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electronics and information storage applications (many of which also contain nano-scale layers), there may be little opportunity for physical interaction between the ENMs and humans or the environment in their use-phase, however, greater attention on the beginning- and end-of-life scenarios may be warranted. Consumer advertisements identify nanotechnology products that are successfully manufactured and commercialized, and the patent literature fills in gaps by identifying industry nanotech innovation that is either un-advertised or has not yet reached the marketplace. Product and material distributions from both IP and consumer product surveys viewed together are more comprehensive and environmentally relevant than either survey alone. 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 EF0830093, 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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