To assess the environmental fate of engineered nanoparticles (ENPs), it is essential to understand their interactions with dissolved organic matter (DOM). The highly complex nature of the interactions between DOM and ENPs and other particulate matter (PM) requires investigating a wide range of material types under different conditions. However, despite repeated calls for an increased diversity of the DOM and PM studied, researchers increasingly focus on certain subsets of DOM and PM. Considering the discrepancy between the calls for more diversity and the research actually carried out, we hypothesize that materials that were studied more often are more visible in the scientific literature and therefore are more likely to be studied again. To investigate the plausibility of this hypothesis, we developed an agent-based model simulating the material choice in the experiments studying the interaction between DOM and PM between 1990 and 2015. The model reproduces the temporal trends in the choice of materials as well as the main properties of a network that displays the DOM and PM types investigated experimentally. The results, which support the hypothesis of a positive reinforcing material choice, help to explain why calls to increase the diversity of the materials studied are repeatedly made and why recent criticism states that the selection of materials is unbalanced.

Aix Marseille Université CNRS IRD INRA Coll France CEREGE Aix en Provence France

Bureau de Recherches Géologiques et Minières Orléans France

Institute for Chemical and Bioengineering ETH Zürich Zürich Switzerland

RECETOX Masaryk University Brno Czech Republic

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Colvin VL. The potential environmental impact of engineered nanomaterials. Nat Biotechnol. 2003;21(10): 1166–1170. doi: 10.1038/nbt875 PubMed DOI

Jośko I, Oleszczuk P. Manufactured nanomaterials: the connection between environmental fate and toxicity. Crit Rev Environ Sci Technol. 2013;43(23): 2581–2616. doi: 10.1080/10643389.2012.694329 DOI

Praetorius A, Scheringer M, Hungerbühler K. Development of environmental fate models for engineered nanoparticles–a case study of TiO2 nanoparticles in the Rhine River. Environ Sci Technol. 2012;46(12): 6705–6713. doi: 10.1021/es204530n PubMed DOI

Sani-Kast N, Scheringer M, Slomberg D, Labille J, Praetorius A, Ollivier P, et al. Addressing the complexity of water chemistry in environmental fate modeling for engineered nanoparticles. Sci Total Environ. 2015;535: 150–159. doi: 10.1016/j.scitotenv.2014.12.025 PubMed DOI

Leenheer JA, Croué JP. Characterizing aquatic dissolved organic matter. Environ Sci Technol. 2003;37(1): 18A–26A. doi: 10.1021/es032333c PubMed DOI

Aiken GR, Hsu-Kim H, Ryan JN. Influence of dissolved organic matter on the environmental fate of metals, nanoparticles, and colloids. Environ Sci Technol. 2011;45(8): 3196–3201. doi: 10.1021/es103992s PubMed DOI

Baalousha M. Aggregation and disaggregation of iron oxide nanoparticles: Influence of particle concentration, pH and natural organic matter. Sci Total Environ. 2009;407(6): 2093–2101. doi: 10.1016/j.scitotenv.2008.11.022 PubMed DOI

Liu J, Legros S, von der Kammer F, Hofmann T. Natural organic matter concentration and hydrochemistry influence aggregation kinetics of functionalized engineered nanoparticles. Environ Sci Technol. 2013;47(9): 4113–4120. doi: 10.1021/es302447g PubMed DOI

Majedi SM, Kelly BC, Lee HK. Role of combinatorial environmental factors in the behavior and fate of ZnO nanoparticles in aqueous systems: a multiparametric analysis. J Hazard Mater. 2014;264: 370–379. doi: 10.1016/j.jhazmat.2013.11.015 PubMed DOI

Louie SM, Tilton RD, Lowry GV. Critical review: Impacts of macromolecular coatings on critical physicochemical processes controlling environmental fate of nanomaterials. Environ Sci Nano. 2016;3(2): 283–310. doi: 10.1039/C5EN00104H DOI

Sani-Kast N, Labille J, Ollivier P, Slomberg D, Hungerbühler K, Scheringer M. A network perspective reveals decreasing material diversity in studies on nanoparticle interactions with dissolved organic matter. Proc Natl Acad Sci U S A. 2017;114(10): E1756–E1765. doi: 10.1073/pnas.1608106114 PubMed DOI PMC

Philippe A, Schaumann GE. Interactions of dissolved organic matter with natural and engineered inorganic colloids: A review. Environ Sci Technol. 2014;48(16): 8946–8962. doi: 10.1021/es502342r PubMed DOI

Maynard AD, Aitken RJ. ‘Safe handling of nanotechnology’ ten years on. Nat Nanotechnol. 2016;11(12): 998–1000. doi: 10.1038/nnano.2016.270 PubMed DOI

Wei LJ, Wei J L. Polya’s Urn Model. In: Wiley StatsRef: Statistics Reference Online. Chichester, UK: John Wiley & Sons, Ltd; 2014. Available from: http://doi.wiley.com/10.1002/9781118445112.stat05795 DOI

Hayhoe M, Alajaji F, Gharesifard B. A Polya contagion model for networks; 2017. Preprint. Available from arXiv:1705.02239. Cited 12 December 2017.

Wagner U, Taudes A. A multivariate Polya model of brand choice and purchase incidence. Marketing Sci. 1986;5(3): 219–244. doi: 10.1287/mksc.5.3.219 DOI

Gong T, Shuai L, Tamariz M, Jäger G, Linkenkaer-Hansen K. Studying language change using price equation and Pólya-urn dynamics. PLoS One. 2012;7(3): e33171 doi: 10.1371/journal.pone.0033171 PubMed DOI PMC

Vespignani A. Modelling dynamical processes in complex socio-technical systems. Nat Phys. 2011;8(1): 32–39. doi: 10.1038/nphys2160 DOI

Payette N. Agent-based models of science In: Scharnhorst A, Börner K, van den Besselaar P, editors. Models of Science Dynamics: Encounters Between Complexity Theory and Information Sciences. Berlin, Heidelberg: Springer; 2012. p. 127–157. Available from: https://doi.org/10.1007/978-3-642-23068-4_4 DOI

Gilbert N. A simulation of the structure of academic science. Sociol Res Online. 1997;2(2). Available from: http://www.socresonline.org.uk/2/2/3.html doi: 10.5153/sro.85 DOI

Weisberg M, Muldoon R. Epistemic landscapes and the division of cognitive labor. Philos Sci. 2009;76(2): 225–252. doi: 10.1086/644786 DOI

Balietti S, Mäs M, Helbing D. On disciplinary fragmentation and scientific progress. PLoS One. 2015;10(3): e0118747 doi: 10.1371/journal.pone.0118747 PubMed DOI PMC

R Core Team. R: A Language and Environment for Statistical Computing; 2013. Available from: http://www.r-project.org/

Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, et al. A standard protocol for describing individual-based and agent-based models. Ecol Modell. 2006;198(1): 115–126. doi: 10.1016/j.ecolmodel.2006.04.023 DOI

Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF. The ODD protocol: a review and first update. Ecol Modell. 2010;221(23): 2760–2768. doi: 10.1016/j.ecolmodel.2010.08.019 DOI

van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2): 523–538. doi: 10.1007/s11192-009-0146-3 PubMed DOI PMC

Grillo R, Rosa AH, Fraceto LF. Engineered nanoparticles and organic matter: A review of the state-of-the-art. Chemosphere. 2015;119: 608–619. doi: 10.1016/j.chemosphere.2014.07.049 PubMed DOI

Newman MEJ. Coauthorship networks and patterns of scientific collaboration. Proc Natl Acad Sci U S A. 2004;101(suppl 1): 5200–5205. doi: 10.1073/pnas.0307545100 PubMed DOI PMC

Newman MEJ. The structure of scientific collaboration networks. Proc Natl Acad Sci U S A. 2001;98(2): 404–409. doi: 10.1073/pnas.98.2.404 PubMed DOI PMC

Morel CM, Serruya SJ, Penna GO, Guimarães R. Co-authorship network analysis: a powerful tool for strategic planning of research, development and capacity building programs on neglected diseases. PLoS Negl Trop Dis. 2009;3(8): e501 doi: 10.1371/journal.pntd.0000501 PubMed DOI PMC

Grim P. Threshold phenomena in epistemic networks. In: AAAI Fall Symposium Series; 2009. pp. 53–60.