Abstract
The profile management of nanomaterials requires a complicated synergy between component function and shape, material, process, and costs. This study attempts to uncover these relationships by grouping nanomaterial profile components with matching characteristics using cluster analysis. The analysis resulted in the identification of 11 distinct clusters, out of which the physicochemical properties appear to have the higher complexity. We found that this is an efficient method for inspecting the heterogeneity of the nanomaterial profile building blocks and for quantifying nanomaterial characteristics. Using the Cynefin framework, we identified the parameters, which allowed us to comprehend the complexity of the issues, design relative strategies, and overcome difficulties stemming from the application of reductionist approaches on complicated circumstances. It introduces the emergence and implications of “complex” approaches within nanomaterial profile. Cost lies in the disorder domain and the urgency to address the critical issue of asymmetric information calls to understand complex relations. The crux of the issue is the lack of a connected profiling chain that links the nanomaterial development process steps, cost, risk, and toxicity studies, which could reduce opposition from “nano-skeptics” providing sufficient safeguards given the predictive growth of nanomaterials.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arts JHE, Hadi M, Keene AM, Kreiling R, Lyon D, Maier M, Michel K, Petry T, Sauer UG, Warheit D, Wiench K, Landsiedel R (2014) A critical appraisal of existing concepts for the grouping of nanomaterials. Regul Toxicol Pharmacol 70(2):492–506. https://doi.org/10.1016/j.yrtph.2014.07.025
Baez B (2002) Confidentiality in qualitative research: reflections on secrets, power and agency. Qual Res 2(1):35–58. https://doi.org/10.1177/1468794102002001638
Bosch OJH, Nguyen NC, Maeno T, Yasui T (2013) Managing complex issues through evolutionary learning laboratories: managing complex issues through ELLabs. Syst Res Behav Sci 30(2):116–135. https://doi.org/10.1002/sres.2171
Boverhof DR, Bramante CM, Butala JH, Clancy SF, Lafranconi M, West J, Gordon SC (2015) Comparative assessment of nanomaterial definitions and safety evaluation considerations. Regul Toxicol Pharmacol 73(1):137–150. https://doi.org/10.1016/j.yrtph.2015.06.001
Delgado GC (2010) Economics and governance of nanomaterials: potential and risks. Technol Soc 32(2):137–144. https://doi.org/10.1016/j.techsoc.2010.03.002
French S, Carter E, Niculae C (2007) Decision support in nuclear and radiological emergency situations: are we too focused on models and technology? Int J Emerg Manag 4(3):421. https://doi.org/10.1504/IJEM.2007.014295
Gan G, Ma C, Wu J (2007) Data clustering: theory, algorithms, and applications. In: ASA-SIAM series on statistics and applied probability 20. SIAM, Society for Industrial and Applied Mathematics ; American Statistical Association, Philadelphia, Pa.: Alexandria, Va
Godwin H, Nameth C, Avery D, Bergeson LL, Bernard D, Beryt E, Boyes W, Brown S, Clippinger AJ, Cohen Y, Doa M, Hendren CO, Holden P, Houck K, Kane AB, Klaessig F, Kodas T, Landsiedel R, Lynch I, Malloy T, Miller MB, Muller J, Oberdorster G, Petersen EJ, Pleus RC, Sayre P, Stone V, Sullivan KM, Tentschert J, Wallis P, Nel AE (2015) Nanomaterial categorization for assessing risk potential to facilitate regulatory decision-making. ACS Nano 9(4):3409–3417. https://doi.org/10.1021/acsnano.5b00941
Gorzeń-Mitka I, Okręglicka M (2014) Improving decision making in complexity environment. Procedia Econ Financ 16:402–409. https://doi.org/10.1016/S2212-5671(14)00819-3
Gubrium JF, Holstein JA (eds) (2002) Handbook of interview research: context & method. Sage Publications, Thousand Oaks, Calif
Gudkov V, Montealegre V, Nussinov S, Nussinov Z (2008) Community detection in complex networks by dynamical simplex evolution. Phys Rev E 78(1). https://doi.org/10.1103/PhysRevE.78.016113
Guo Q, Lu X, Gao Y, Zhang J, Yan B, Su D, Song A, Zhao X, Wang G (2017) Cluster analysis: a new approach for identification of underlying risk factors for coronary artery disease in essential hypertensive patients. Sci Rep 7(1). https://doi.org/10.1038/srep43965
Hansen SF, Jensen KA, Baun A (2014) NanoRiskCat: a conceptual tool for categorization and communication of exposure potentials and hazards of nanomaterials in consumer products. J Nanopart Res 16(1). https://doi.org/10.1007/s11051-013-2195-z
Harvey WS (2011) Strategies for conducting elite interviews. Qual Res 11(4):431–441. https://doi.org/10.1177/1468794111404329
Hassanien AE, Azar AT, Snasael V, Kacprzyk J, Abawajy JH (eds) (2015) Big data in complex systems. Vol. 9. Studies in Big data. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-11056-1
Heimo T, Kumpula JM, Kaski K, Saramäki J (2008) Detecting modules in dense weighted networks with the Potts method. J Stat Mech: Theory Exp 2008(8):P08007. https://doi.org/10.1088/1742-5468/2008/08/P08007
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. 1st MIT Press ed. In: Complex adaptive systems. MIT Press, Cambridge, Mass
Holzinger M, Le Goff A, Cosnier S (2014) Nanomaterials for biosensing applications: a review. Frontiers in Chemistry 2(August). https://doi.org/10.3389/fchem.2014.00063
Hsu JJ, Finkelstein DM, Schoenfeld DA (2015) Outcome-driven cluster analysis with application to microarray data. Edited by Guy Brock. PLOS ONE 10(11):e0141874. https://doi.org/10.1371/journal.pone.0141874
Johnson SC (1967) Hierarchical clustering schemes. Psychometrika 32(3):241–254. https://doi.org/10.1007/BF02289588
Kempermann G (2017) Cynefin as reference framework to facilitate insight and decision-making in complex contexts of biomedical research. Front Neurosci 11(November). https://doi.org/10.3389/fnins.2017.00634
Kurtz CF, Snowden DJ (2003) The new dynamics of strategy: sense-making in a complex and complicated world. IBM Syst J 43(3)
Lancaster K (2017) Confidentiality, anonymity and power relations in elite interviewing: conducting qualitative policy research in a politicised domain. Int J Soc Res Methodol 20(1):93–103. https://doi.org/10.1080/13645579.2015.1123555
Laux P, Tentschert J, Riebeling C, Braeuning A, Creutzenberg O, Epp A, Fessard V, Haas K-H, Haase A, Hund-Rinke K, Jakubowski N, Kearns P, Lampen A, Rauscher H, Schoonjans R, Störmer A, Thielmann A, Mühle U, Luch A (2018) Nanomaterials: certain aspects of application, risk assessment and risk communication. Arch Toxicol 92(1):121–141. https://doi.org/10.1007/s00204-017-2144-1
Merriam SB, Tisdell EJ (2016) Qualitative research: a guide to design and implementation. In: Fourth edition. The Jossey-Bass higher and adult education series. Jossey-Bass, San Francisco, CA
Mikecz R (2012) Interviewing elites: addressing methodological issues. Qual Inq 18(6):482–493. https://doi.org/10.1177/1077800412442818
Morris ZS (2009) The truth about interviewing elites. Politics 29(3):209–217. https://doi.org/10.1111/j.1467-9256.2009.01357.x
Mucha PJ, Richardson T, Macon K, Porter MA, Onnela J-P (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980):876–878. https://doi.org/10.1126/science.1184819
Ngô C, Van de Voorde M (2014) Nanotechnology in a nutshell. Atlantis Press, Paris. https://doi.org/10.2991/978-94-6239-012-6
O’Connor RV, Lepmets M (2015) Exploring the use of the cynefin framework to inform software development approach decisions. ACM Press, New York, pp 97–101. https://doi.org/10.1145/2785592.2785608
Oomen A, Bleeker E, Bos P, van Broekhuizen F, Gottardo S, Groenewold M, Hristozov D et al (2015) Grouping and read-across approaches for risk assessment of nanomaterials. Int J Environ Res Public Health 12(10):13415–13434. https://doi.org/10.3390/ijerph121013415
Peters RJB, Bouwmeester H, Gottardo S, Amenta V, Arena M, Brandhoff P, Marvin HJP, Mech A, Moniz FB, Pesudo LQ, Rauscher H, Schoonjans R, Undas AK, Vettori MV, Weigel S, Aschberger K (2016) Nanomaterials for products and application in agriculture, feed and food. Trends Food Sci Technol 54(August):155–164. https://doi.org/10.1016/j.tifs.2016.06.008
Pitkethly MJ (2004) Nanomaterials – the driving force. Mater Today 7(12):20–29. https://doi.org/10.1016/S1369-7021(04)00627-3
Remington K, Pollack J (2007) Tools for complex projects. Gower, Aldershot, England; Burlington, VT
Roco MC (2004) Nanoscale science and engineering: unifying and transforming tools. AICHE J 50(5):890–897. https://doi.org/10.1002/aic.10087
Saha P (2014) A systemic perspective to managing complexity with enterprise architecture. In: Advances in business information systems and analytics (ABISA) book series. Business Science Reference, an imprint of IGI Global, Hershey, PA
Shin S, Song I, Um S (2015) Role of physicochemical properties in nanoparticle toxicity. Nanomaterials 5(3):1351–1365. https://doi.org/10.3390/nano5031351
Som C, Nowack B, Krug HF, Wick P (2013) Toward the development of decision supporting tools that can be used for safe production and use of nanomaterials. Acc Chem Res 46(3):863–872. https://doi.org/10.1021/ar3000458
Stone V, Pozzi-Mucelli S, Tran L, Aschberger K, Sabella S, Vogel U, Poland C, Balharry D, Fernandes T, Gottardo S, Hankin S, Hartl MGJ, Hartmann N, Hristozov D, Hund-Rinke K, Johnston H, Marcomini A, Panzer O, Roncato D, Saber AT, Wallin H, Scott-Fordsmand JJ (2014) ITS-NANO - prioritising nanosafety research to develop a stakeholder driven intelligent testing strategy. Part Fibre Toxicol 11(1):9. https://doi.org/10.1186/1743-8977-11-9
Toivonen R, Kivelä M, Saramäki J, Viinikainen M, Vanhatalo M, Sams M (2012) Networks of emotion concepts. Edited by Olaf Sporns. PLoS One 7(1):e28883. https://doi.org/10.1371/journal.pone.0028883
Van Breukelen GJP, Candel MJJM (2012) Calculating sample sizes for cluster randomized trials: we can keep it simple and efficient! J Clin Epidemiol 65(11):1212–1218. https://doi.org/10.1016/j.jclinepi.2012.06.002
Young JC, Rose DC, Mumby HS, Benitez-Capistros F, Derrick CJ, Finch T, Garcia C, Home C, Marwaha E, Morgans C, Parkinson S, Shah J, Wilson KA, Mukherjee N (2018) A methodological guide to using and reporting on interviews in conservation science research. Methods Ecol Evol 9(1):10–19. https://doi.org/10.1111/2041-210X.12828
Zeng A, Shen Z, Zhou J, Wu J, Fan Y, Wang Y, Eugene Stanley H (2017) The science of science: from the perspective of complex systems. Phys Rep 714–715(November):1–73. https://doi.org/10.1016/j.physrep.2017.10.001
Zhang Z, Murtagh F, Van Poucke S, Lin S, Lan P (2017) Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R. Ann Transl Med 5(4):75. https://doi.org/10.21037/atm.2017.02.05
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gkika, D.A., Ovaliadis, K., Vordos, N. et al. Managing complexity: the case of nanomaterials. J Nanopart Res 21, 17 (2019). https://doi.org/10.1007/s11051-018-4456-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11051-018-4456-3