Dataset Available

Transport and Diffusion of nanoparticles in confined media

Authors: Krishnamoorti, Ramanan
Published On: Sep 20 2014 00:16 UTC
DOI: https://doi.org/10.7266/N7NG4NK4
UDI: R1.x141.064:0036
Files

Individual Files

No. of Downloads: 6

No. of Files: 7

File Size: 118.1 MB

File Format(s):
Origin Project file (.opj) -- opens with free download from http://www.originlab.com/viewer/

Project Information

Funded By:
Gulf of Mexico Research Initiative

Funding Cycle:
RFP-I

Research Group:
Consortium for the Molecular Engineering of Dispersant Systems (C-MEDS)

Point of Contact

Vijay T. John
Tulane University / Department of Chemical and Biomolecular Engineering
vj@tulane.edu

Theme keywords

diffusion, porous media, non-Gaussian dynamics, nanoparticles

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Abstract:

Movie files for the transport and diffusion of nanoparticles in confined media and representative of the mechanisms involved for the action of such nanoparticles in controlling interfacial properties of oil and water.

Suggested Citation:

Krishnamoorti, Ramanan. 2014. Transport and Diffusion of nanoparticles in confined media. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7NG4NK4

Publications:

He, K., Babaye Khorasani, F., Retterer, S. T., Thomas, D. K., Conrad, J. C., & Krishnamoorti, R. (2013). Diffusive Dynamics of Nanoparticles in Arrays of Nanoposts. ACS Nano, 7(6), 5122–5130. doi:10.1021/nn4007303

Purpose:

The fate of engineered nanoparticles and nano-scale contaminants in aquatic systems depends in part on their transport properties in environmental porous media like saturated soils. Confining a micrometer-sized colloidal particle changes its transport properties: particles near walls or in enclosed chambers diffuse more slowly than those in bulk. Here, we focused on transport at zero shear rate and investigated the dynamics of nanoparticles diffusing in nanopost arrays.

Data Parameters and Units:

1. displacement_distribution.opj: a. Table Stretched gaussian+ Gaussian fit Graph1: For data, delta_x (mu-m) verses G(delta_x, delta_t) [data, good fits, gamma=2, gamma=1.8, gamma = 1.3] b. Table dt0517: A(X1), nopost(Y1), s8um(X2), C(Y2), s1.2um(X3), E(Y3), B(Y3), B1(Y3), B2(Y3) Graph4: For t=0.517s, delta_x (mu-m) verses G(delta_x,delta_t) [No post, 8 mu-m spacing, 1.2 mu-m spacing] c. Table dt0.183: A(X1), nopost(Y1), s8um(X2), C(Y2), s1.2um(X3), E(Y3), B(Y3), B1(Y3), B2(Y3) Graph5: For t=0.183s, delta_x (mu-m) verses G(delta_x,delta_t) [No post, 8 mu-m spacing, 1.2 mu-m spacing] d. Table dt0.85: A(X1), nopost(Y1), s8um(X2), C(Y2), s1.2um(X3), E(Y3), B(Y3), B1(Y3), B2(Y3) Graph6: For t=0.85s, delta_x (mu-m) verses G(delta_x,delta_t) [No post, 8 mu-m spacing, 1.2 mu-m spacing] 2. ISF_R200.opj: [Nopost, S8um, S4um, S2um, S16um, S12um, S18um, S6um, S10um] t(X), q1(Y) through q101(Y) 3. ISF_R300.opj: [Nopost, S8um, S4um, S2um, S16um, S12um, S18um, S6um, S10um] t(X), q1(Y) through q101(Y) 4. ISF_R400.opj: [Nopost, S8um, S4um, S2um, S16um, S12um, S18um, S6um, S10um] t(X), q1(Y) through q101(Y) 5. MSD_and_decay_length.opj: a. Table MSD Graph1: delta_t verses MSD [no post, spacing8, spacing4, spacing1.8, spacing1.2] b. Tables of gamma(x,t) vs t fit of Data2 Table Data2 Graph2: delta_t verses gamma(t) [no post, spacing8, spacing4, spacing1.8, spacing1.2] 6. MSD_Decay_length1.opj: a. Table MSD: t(X1),nopost(Y1), C(X2), S8(Y2), E(X3), S4(Y3), G(X4), S1,8(Y4), I(X5), S1.2(Y5) Decaylength: A(X), nopost(Y), C(yEr-), s4(Y), G(yEr-), s1.8(Y), I(yEr-), s1.2(Y), K(yEr-) Graph1: delta_x (mu-m) verses gamma (delta_t) (mu-m) [no post, spacing8, spacing4, spacing1.8, spacing1.2] 7. Physics_stretched_Gaussian.opj a. Data1: A(X), b0.8(Y), b0.85(Y), b0.95(Y), b0.95(Y), b0.99(Y) Graph1, Graph2, Graph3: S=D/D verses P(s,r) where r=[0.8, 0.85, 0.95, 0.99]

Individual Files: