Abstract
Due to their favorable electrical and optical properties, quantum dots (QDs) nanostructures have found numerous applications including nanomedicine and photovoltaic cells. However, increased future production, use, and disposal of engineered QD products also raise concerns about their potential environmental impacts. The objective of this work is to establish a modeling framework for predicting the diffusion dynamics and concentration of toxic materials released from Trioctylphosphine oxide-capped CdSe. To this end, an agent-based model simulation with reaction kinetics and Brownian motion dynamics was developed. Reaction kinetics is used to model the stability of surface capping agent particularly due to oxidation process. The diffusion of toxic Cd2+ ions in aquatic environment was simulated using an adapted Brownian motion algorithm. A calibrated parameter to reflect sensitivity to reaction rate is proposed. The model output demonstrates the stochastic spatial distribution of toxic Cd2+ ions under different values of proxy environmental factor parameters. With the only chemistry considered was oxidation, the simulation was able to replicate Cd2+ ion release from Thiol-capped QDs in aerated water. The agent-based method is the first to be developed in the QDs application domain. It adds both simplicity of the solubility and rate of release of Cd2+ ions and complexity of tracking of individual atoms of Cd at the same time.
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References
Aldana J, Wang YA, Peng XG (2001) Photochemical instability of CdSe nanocrystals coated by hydrophilic thiols. J Am Chem Soc 123(36):8844–8850. doi:10.1021/ja016424q
Andrews SS, Bray D (2004) Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys Biol 1(3):137–151. doi:10.1088/1478-3967/1/3/001
Azimi M, Jamali Y, Mofrad MRK (2011) “Accounting for Diffusion in Agent Based Models of Reaction-Diffusion Systems with Application to Cytoskeletal Diffusion.” Plos One 6 (9). doi: 10.1371/journal.pone.0025306
Chen Jixin (2010) Nanofabrication, plasmon enhanced fluorescence and photo-oxidation kinetics of CdSe nanoparticles. DOCTOR OF PHILOSOPHY, Chemistry, Texas A&M University, College Station
Christoph L (2005) Small sizes that matter: opportunities and risks of Nanotechnologies Organization for Economic Co-Operation and Development (OECD), and Allianz Center for Technology, Paris, Munchen. http://www.oecd.org/science/nanosafety/44108334.pdf
Derfus AM, Chan WCW, Bhatia SN (2004) Probing the cytotoxicity of semiconductor quantum dots. Nano Lett 4(1):11–18. doi:10.1021/nl0347334
Dong X, Foteinou PT, Calvano SE, Lowry SF, Androulakis IP (2010) Agent-Based Modeling of endotoxin-induced acute inflammatory response in human blood leukocytes. Plos One 5(2):e9249. doi:10.1371/journal.pone.0009249
Eckelman MJ, Mauter MS, Isaacs JA, Elimelech M (2012) New perspectives on nanomaterial aquatic ecotoxicity: production impacts exceed direct exposure impacts for carbon nanotubes. Environ Sci Technol 46(5):2902–2910. doi:10.1021/es203409a
Emonet T, Macal CM, North MJ, Wickersham CE, Cluzel P (2005) AgentCell: a digital single-cell assay for bacterial chemotaxis. Bioinformatics 21(11):2714–2721. doi:10.1093/bioinformatics/bti391
EPA 2009 Nanomaterial research strategy, EPA 620/K-09/011
Figgemeier E, Hagfeldt A (2004) Are dye-sensitized nano-structured solar cells stable? An overview of device testing and component analyses. Int J Photoenergy 6(3):127–140. doi:10.1155/s1110662x04000169
Geranio L, Heuberger M, Nowack B (2009) The behavior of silver nanotextiles during washing. Environ Sci Technol 43(21):8113–8118. doi:10.1021/es9018332
Gordon N, Sagman U (2003) Nanomedicine taxonomy. Canadian Institutes of Health Research & Canadian NanoBusiness Alliance, Toronto
Gottschalk F, Sonderer T, Scholz RW, Nowack B (2009) Modeled environmental concentrations of engineered nanomaterials (TiO2, ZnO, Ag, CNT, Fullerenes) for different regions. Environ Sci Technol 43(24):9216–9222. doi:10.1021/es9015553
Hines DA, Becker MA, Kamat PV (2012) Photoinduced surface oxidation and its effect on the exciton dynamics of CdSe quantum dots. J Phys Chem C 116(24):13452–13457. doi:10.1021/jp303659g
Hotze Ernest M, Phenrat Tanapon, Lowry Gregory V (2010) Nanoparticle aggregation: challenges to understanding transport and reactivity in the environment. J Environ Qual 39:1909–1924
Jamieson T, Bakhshi R, Petrova D, Pocock R, Imani M, Seifalian AM (2007) Biological applications of quantum dots. Biomaterials 28(31):4717–4732. doi:10.1016/j.biomaterials.2007.07.014
Jin S, Hu YX, Gu ZJ, Liu L, Wu HC (2011) Application of quantum dots in biological imaging. J Nanomater. doi:10.1155/2011/834139
Kirschling TL, Golas PL, Unrine JM, Matyjaszewski K, Gregory KB, Lowry GV, Tilton RD (2011) Microbial bioavailability of covalently bound polymer coatings on model engineered nanomaterials. Environ Sci Technol 45(12):5253–5259. doi:10.1021/es200770z
Klann MT, Lapin A, Reuss M (2011) Agent-based simulation of reactions in the crowded and structured intracellular environment: Influence of mobility and location of the reactants. BMC Syst Biol 5:71. doi:10.1186/1752-0509-5-71
Kuan Z, Rui-bin Q, Hao-ran Z, Jun-qing N (2011) An Agent-based Modeling Approach for Stochastic Molecular Events of Biochemical Networks. Intell Comput Technol Automa 1:759–763
Lowry GV, Gregory KB, Apte SC, Lead JR (2012) Transformations of nanomaterials in the environment. Environ Sci Technol 46(13):6893–6899. doi:10.1021/es300839e
Maynard AD, Warheit DB, Philbert MA (2011) The new toxicology of sophisticated materials: nanotoxicology and beyond. Toxicol Sci 120:S109–S129. doi:10.1093/toxsci/kfq372
Meng L, Song ZX (2004) Applications of quantum dots to biological medicine. Prog Biochem Biophys 31(2):185–187
Mueller NC, Nowack B (2008) Exposure modeling of engineered nanoparticles in the environment. Environ Sci Technol 42(12):4447–4453. doi:10.1021/es7029637
Nowack B (2009) The behavior and effects of nanoparticles in the environment. Environ Pollut 157(4):1063–1064. doi:10.1016/j.envpol.2008.12.019
Nowack B, Ranville JF, Diamond S, Gallego-Urrea JA, Metcalfe C, Rose J, Horne N, Koelmans AA, Klaine SJ (2012) Potential scenarios for nanomaterial release and subsequent alteration in the environment. Environ Toxicol Chem 31(1):50–59. doi:10.1002/etc.726
Oberdorster G (2010) Safety assessment for nanotechnology and nanomedicine: concepts of nanotoxicology. J Intern Med 267(1):89–105. doi:10.1111/j.1365-2796.2009.02187.x
O’Brien N, Cummins E (2008) Recent developments in nanotechnology and risk assessment strategies for addressing public and environmental health concerns. Hum Ecol Risk Assess 14(3):568–592. doi:10.1080/10807030802074261
Petersen EJ, Lam T, Gorham JM, Scott KC, Long CJ, Stanley D, Sharma R, Liddle JA, Pellegrin B, Nguyen T (2014) Methods to assess the impact of UV irradiation on the surface chemistry and structure of multiwall carbon nanotube epoxy nanocomposites. Carbon 69:194–205. doi:10.1016/j.carbon.2013.12.016
Peulen TO, Wilkinson KJ (2011) Diffusion of nanoparticles in a biofilm. Environ Sci Technol 45(8):3367–3373. doi:10.1021/es103450g
Pogson M, Smallwood R, Qwarnstrom E, Holcombe M (2006) Formal agent-based modelling of intracellular chemical interactions. Biosystems 85(1):37–45. doi:10.1016/j.biosystems.2006.02.004
Scown TM, van Aerle R, Tyler CR (2010) Review: do engineered nanoparticles pose a significant threat to the aquatic environment? Crit Rev Toxicol 40(7):653–670. doi:10.3109/10408444.2010.494174
Wiesner MR, Lowry GV, Alvarez P, Dionysiou D, Biswas P (2006) Assessing the risks of manufactured nanomaterials. Environ Sci Technol 40(14):4336–4345. doi:10.1021/es062726m
Wohlleben W, Meier MW, Vogel S, Landsiedel R, Cox G, Hirth S, Tomovic Z (2013) Elastic CNT-polyurethane nanocomposite: synthesis, performance and assessment of fragments released during use. Nanoscale 5(1):369–380. doi:10.1039/c2nr32711b
Yong KT, Law WC, Hu R, Ye L, Liu LW, Swihart MT, Prasad PN (2013) Nanotoxicity assessment of quantum dots: from cellular to primate studies. Chem Soc Rev 42(3):1236–1250. doi:10.1039/c2cs35392j
Acknowledgments
Datu Buyung Agusdinata acknowledges the support from Northern Illinois University Research and Artistry Grant. Tao Xu also acknowledges the support from NSF (CBET-1150617). The authors would like to thank Faqian Liu for some of the parameter specifications.
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Appendix: Parameter specifications
Appendix: Parameter specifications
Total mass of CdSe
For a quantum dot with a diameter of 6.5 nm (whole diameter of 8 nm), the volume should be = 143.72 nm3.
The weight of the each CdSe quantum dot is m = V × ρ = 143.72 nm3 × 5.816 g/cm3 = 835.88 × 10−21 g.
For a 1 cm2 area with a quantum layer of 150 nm, the quantity of the close-packed quantum dots should be (1 cm/8 nm) × (1 cm/8 nm) × (150 nm/8 nm) = 2.81 × 1013.
So, the whole weight of the quantum dots needed for a 1 cm2 area with a quantum layer of 150 nm is
No. of ions released by a fully oxidized CdSe quantum dot particle
For a CdSe quantum dot with a diameter of 6.5 nm
So, each CdSe quantum dot contains the quantity of the CdSe molecules
That is, each CdSe quantum dot with a diameter of 6.5 nm finally will produce 2632 Cd2 + by the free oxidation.
Diffusion coefficients
The diffusion coefficients for particles Cd2+ and O2 will be derived under the following conditions:
T = 293.15 K, b = 6, ηB = 0.001003 kg/ms,
r 0 (CdSe capped with TOPO) = 4.523 × 10−9 m,
r 0 (CdSe) = 3.25 × 10−9 m,
r 0 (Cd2+) = 4.85 × 10−11 m,
r 0 (O2) = 1.20 × 10−10 m \(\Rightarrow\) D CdSe = 6.5841 × 10−11 m2 s−1,
D CdSe with TOPO = 4.731 × 10−11 m2 s−1,
D Cd2+ = 4.412 × 10−9 m2 s−1,
D O2 = 1.814 × 10−9 m2 s−1.
Assuming that the only diffusing particles in our systems are oxygen and Cd2+, we do not need to calculate diffusion coefficient and movement probabilities for other particles. However, since CdSe with capping agent and CdSe particles are considered in the model, their sizes are studied for determining the lattice discretization length \((\Delta L)\), which is the size of the largest particle in the system.
Based on Eq. 2, in order to derive movement probability for each particle, first movement probability value of one (T = 1) is assigned to the fastest particle, since no particle will be diffusing faster than this particle. Second, \(\Delta L\) will be length of the cubic lattice’s side, which is assumed to be 9.046 × 10−9 m since it is the size of the largest particle in our system (i.e., CdSe particle capped with TOPO). Third, by inputting the diffusion coefficient of the fastest particle (i.e., Cd2+ ion), the equation will be solved in terms of \(\Delta t\), which is going to be the fixed time-step in the model. Finally, by knowing \(\Delta L\), \(\Delta t\), and the diffusion coefficient (D) for each particle, movement probability will be derived for each individual particle (i.e., the agent in the agent-based model). In the following model, particles of the same type are considered to have the same size and same diffusion coefficient, which denotes to have same movement probability in that state.
\(\Delta L\) = Size of the Largest Particle = Size of CdSe with TOPO = 9.046 × 10−9 m
T = 1.0 for Cd2+ which is our fastest particle (due to having the largest diffusion coefficient)
D = 4.4120 × 10−9 m2s−1 for Cd2+
The volume of quasi 3-D cubic lattice
The simulated quasi 3-D cubic lattice structure has a dimension with length, width, and height of ∆X × ∆L, ∆Y × ∆L, and 1 × ∆L, respectively. We know the fixed value for discretization lattice = ∆L = (\(9.046 \times 10^{ - 9} m)\). We assume same length and width (\(\Delta X = \Delta Y\)), which is determined by matching the concentration of the Cd2+ resulting from the simulation with that of Derfus et al. research data (Derfus et al. 2004). It can be established that
The volume of quasi 3-D cubic lattice structure = (∆X × ∆L). (∆Y × ∆L).(1 × ∆L)
= (108,848 × 9.046.10−9) (108,848 × 9.046.10−9) (1 × 9.046.10−9) = 8.77 × 10−15 m3.
The number of dissolved O2
The number of dissolved O2 is calculated based on the number of O2 in water at 20 C, 1 atm, which is about 1.7 × 1020/L, or about 1.35 × 109 O2 in the simulated value of 8.77 × 10−15 m3.
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Agusdinata, D.B., Amouie, M. & Xu, T. Diffusion dynamics and concentration of toxic materials from quantum dots-based nanotechnologies: an agent-based modeling simulation framework. J Nanopart Res 17, 26 (2015). https://doi.org/10.1007/s11051-014-2844-x
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DOI: https://doi.org/10.1007/s11051-014-2844-x