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SCIENCE CHINA Physics, Mechanics & Astronomy, Volume 62 , Issue 10 : 107006(2019) https://doi.org/10.1007/s11433-019-9401-x

Concentrated suspensions of Brownian beads in water: dynamic heterogeneities through a simple experimental technique

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  • ReceivedJan 31, 2019
  • AcceptedMar 27, 2019
  • PublishedJun 19, 2019
PACS numbers

Abstract

Concentrated suspensions of Brownian hard-spheres in water are an epitome for understandingthe glassy dynamics of both soft materials and supercooled molecular liquids.From an experimental point of view, such systems are especially suited to perform particle tracking easily, and,therefore, are a benchmark for novel optical techniques, applicable when primary particles cannot be resolved.Differential variance analysis (DVA) is one such novel technique thatsimplifies significantly the characterization of structural relaxation processes of soft glassy materials, since it isdirectly applicable to digital image sequences of the sample.DVA succeeds in monitoring not only the average dynamics, but also its spatio-temporal fluctuations, known as dynamic heterogeneities.In this work, we study the dynamics of dense suspensions of Brownian beads in water, imaged through digital video-microscopy,by using both DVA and single-particle tracking.We focus on two commonly used signatures of dynamic heterogeneities:the dynamic susceptibility, $\chi_4$, and the non-Gaussian parameter, $\alpha_2$.By direct comparison of these two quantities, we are able to highlight similarities and differences.We do confirm that $\chi_4$ and $\alpha_2$ provide qualitatively similar information, but we findquantitative discrepancies in the scalings of characteristic time and length scale on approaching the glass transition.


References

[1] L. Berthier, G. Biroli, J.-P. Bouchaud, L. Cipelletti, and W. van Saarloos, Dynamical heterogeneities in glasses, colloids, and granular media, (OUP Oxford, Oxford, 2011). Google Scholar

[2] Berthier L.. Physics, 2011, 4: 42 CrossRef ADS arXiv Google Scholar

[3] Pusey P. N., van Megen W.. Nature, 1986, 320: 340-342 CrossRef ADS Google Scholar

[4] Weeks E. R., Crocker J. C., Levitt A. C., Schoffeld A., Weitz D. A.. Science, 2000, 287: 627-631 CrossRef ADS Google Scholar

[5] Kegel W. K., van Blaaderen A.. Science, 2000, 287: 290-293 CrossRef ADS Google Scholar

[6] Glotzer S. C.. J. Non-Crystalline Solids, 2000, 274: 342-355 CrossRef ADS Google Scholar

[7] Bennemann C., Donati C., Baschnagel J., Glotzer S. C.. Nature, 1999, 399: 246-249 CrossRef ADS Google Scholar

[8] Hima Nagamanasa K., Gokhale S., Sood A. K., Ganapathy R.. Nat. Phys., 2015, 11: 403-408 CrossRef ADS arXiv Google Scholar

[9] Gokhale S., Ganapathy R., Nagamanasa K. H., Sood A. K.. Phys. Rev. Lett., 2016, 116: 068305 CrossRef PubMed ADS Google Scholar

[10] Ganapathi D., Nagamanasa K. H., Sood A. K., Ganapathy R.. Nat. Commun., 2018, 9: 397 CrossRef PubMed ADS arXiv Google Scholar

[11] Hallett J. E., Turci F., Royall C. P.. Nat. Commun., 2018, 9: 3272 CrossRef PubMed ADS Google Scholar

[12] Robinson J. F., Turci F., Roth R., Royall C. P.. Phys. Rev. Lett., 2019, 122: 068004 CrossRef PubMed ADS arXiv Google Scholar

[13] Ballesta P., Duri A., Cipelletti L.. Nat. Phys., 2008, 4: 550-554 CrossRef Google Scholar

[14] Colin R., Alsayed A. M., Gay C., Abou B.. Soft Matter, 2015, 11: 9020-9025 CrossRef PubMed ADS arXiv Google Scholar

[15] Trappe V., Pitard E., Ramos L., Robert A., Bissig H., Cipelletti L.. Phys. Rev. E, 2007, 76: 051404 CrossRef PubMed ADS Google Scholar

[16] Philippe A. M., Cipelletti L., Larobina D.. Macromolecules, 2017, 50: 8221-8230 CrossRef ADS Google Scholar

[17] Keys A. S., Abate A. R., Glotzer S. C., Durian D. J.. Nat. Phys., 2007, 3: 260-264 CrossRef ADS Google Scholar

[18] Park J. A., Kim J. H., Bi D., Mitchel J. A., Qazvini N. T., Tantisira K., Park C. Y., McGill M., Kim S. H., Gweon B., Notbohm J., Steward Jr R., Burger S., Randell S. H., Kho A. T., Tambe D. T., Hardin C., Shore S. A., Israel E., Weitz D. A., Tschumperlin D. J., Henske E. P., Weiss S. T., Manning M. L., Butler J. P., Drazen J. M., Fredberg J. J.. Nat. Mater., 2015, 14: 1040-1048 CrossRef PubMed ADS Google Scholar

[19] Solomon M. J., Spicer P. T.. Soft Matter, 2010, 6: 1391 CrossRef ADS Google Scholar

[20] Perakis F., Amann-Winkel K., Lehmkühler F., Sprung M., Mariedahl D., Sellberg J. A., Pathak H., Sp?h A., Cavalca F., Schlesinger D., Ricci A., Jain A., Massani B., Aubree F., Benmore C. J., Loerting T., Grübel G., Pettersson L. G. M., Nilsson A.. Proc. Natl. Acad. Sci. USA, 2017, 114: 8193-8198 CrossRef PubMed Google Scholar

[21] Cipelletti L., Bissig H., Trappe V., Ballesta P., Mazoyer S.. Time-resolved correlation: a new tool for studying temporally heterogeneous dynamics. J Phys-Condens Matter, 2003, 15: S257-S262 CrossRef ADS Google Scholar

[22] Duri A., Sessoms D. A., Trappe V., Cipelletti L.. Phys. Rev. Lett., 2009, 102: 085702 CrossRef PubMed ADS arXiv Google Scholar

[23] Katsuragi H., Abate A. R., Durian D. J.. Soft Matter, 2010, 6: 3023 CrossRef ADS arXiv Google Scholar

[24] Nordstrom K. N., Gollub J. P., Durian D. J.. Phys. Rev. E, 2011, 84: 021403 CrossRef PubMed ADS arXiv Google Scholar

[25] Cerbino R., Trappe V.. Phys. Rev. Lett., 2008, 100: 188102 CrossRef PubMed ADS arXiv Google Scholar

[26] Cerbino R., Cicuta P.. J. Chem. Phys., 2017, 147: 110901 CrossRef PubMed ADS Google Scholar

[27] Pastore R., Pesce G., Caggioni M.. Sci. Rep., 2017, 7: 43496 CrossRef PubMed ADS arXiv Google Scholar

[28] Zhang C., Gnan N., Mason T. G., Zaccarelli E., Scheffold F.. J. Stat. Mech., 2016, 2016: 094003 CrossRef ADS arXiv Google Scholar

[29] Helfferich J., Brisch J., Meyer H., Benzerara O., Ziebert F., Farago J., Baschnagel J.. Eur. Phys. J. E, 2018, 41: 71 CrossRef PubMed Google Scholar

[30] Pastore R., Pica Ciamarra M., Pesce G., Sasso A.. Soft Matter, 2015, 11: 622-626 CrossRef PubMed ADS arXiv Google Scholar

[31] E. Jones, T. Oliphant, and P. Peterson, http://www.scipy.org. Google Scholar

[32] Perez F, Granger B E. IPython: A System for Interactive Scientific Computing. Comput Sci Eng, 2007, 9: 21-29 CrossRef ADS Google Scholar

[33] V. Nigro, B. Ruzicka, B. Ruta, F. Zontone, M. Bertoldo, E. Buratti, and R. Angelini, arXiv preprint,. arXiv Google Scholar

[34] Bouzid M., Colombo J., Barbosa L. V., Del Gado E.. Nat. Commun., 2017, 8: 15846 CrossRef PubMed ADS arXiv Google Scholar

[35] Ferrero E. E., Martens K., Barrat J. L.. Phys. Rev. Lett., 2014, 113: 248301 CrossRef PubMed ADS arXiv Google Scholar

[36] N. Gnan and E. Zaccarelli, arXiv preprint,. arXiv Google Scholar

[37] Wu Z. W., Kob W., Wang W. H., Xu L.. Nat. Commun., 2018, 9: 5334 CrossRef PubMed ADS arXiv Google Scholar

[38] Taborek P., Kleiman R. N., Bishop D. J.. Phys. Rev. B, 1986, 34: 1835-1840 CrossRef ADS Google Scholar

[39] Saika-Voivod I., Poole P. H., Sciortino F.. Nature, 2001, 412: 514-517 CrossRef PubMed Google Scholar

[40] Ito K., Moynihan C. T., Angell C. A.. Nature, 1999, 398: 492-495 CrossRef ADS Google Scholar

[41] Chen S. H., Mallamace F., Mou C. Y., Broccio M., Corsaro C., Faraone A., Liu L.. Proc. Natl. Acad. Sci. USA, 2006, 103: 12974-12978 CrossRef PubMed ADS Google Scholar

[42] Huang C., Wikfeldt K. T., Tokushima T., Nordlund D., Harada Y., Bergmann U., Niebuhr M., Weiss T. M., Horikawa Y., Leetmaa M., Ljungberg M. P., Takahashi O., Lenz A., Ojamae L., Lyubartsev A. P., Shin S., Pettersson L. G. M., Nilsson A.. Proc. Natl. Acad. Sci. USA, 2009, 106: 15214-15218 CrossRef PubMed ADS Google Scholar

[43] Mallamace F.. Proc. Natl. Acad. Sci. USA, 2009, 106: 15097-15098 CrossRef PubMed ADS Google Scholar

[44] Mallamace F, Branca C, Corsaro C. Transport properties of glass-forming liquids suggest that dynamic crossover temperature is as important as the glass transition temperature. Proc Natl Acad Sci USA, 2010, 107: 22457-22462 CrossRef PubMed ADS Google Scholar

[45] De Marzio M., Camisasca G., Rovere M., Gallo P.. J. Chem. Phys., 2016, 144: 074503 CrossRef PubMed ADS Google Scholar

[46] Stanley H. E., Kumar P., Xu L., Yan Z., Mazza M. G., Buldyrev S. V., Chen S. H., Mallamace F.. Physica A-Statistical Mech. its Appl., 2007, 386: 729-743 CrossRef ADS Google Scholar

[47] Xu L., Mallamace F., Yan Z., Starr F. W., Buldyrev S. V., Eugene Stanley H.. Nat. Phys., 2009, 5: 565-569 CrossRef ADS Google Scholar

[48] Gallo P., Amann-Winkel K., Angell C. A., Anisimov M. A., Caupin F., Chakravarty C., Lascaris E., Loerting T., Panagiotopoulos A. Z., Russo J., Sellberg J. A., Stanley H. E., Tanaka H., Vega C., Xu L., Pettersson L. G. M.. Chem. Rev., 2016, 116: 7463-7500 CrossRef PubMed Google Scholar

[49] Mallamace F., Corsaro C., Stanley H. E., Mallamace D., Chen S. H.. J. Chem. Phys., 2013, 139: 214502-214502 CrossRef PubMed ADS Google Scholar

[50] Angelini R., Zulian L., Fluerasu A., Madsen A., Ruocco G., Ruzicka B.. Soft Matter, 2013, 9: 10955 CrossRef ADS Google Scholar

[51] Augusto de Melo Marques F., Angelini R., Zaccarelli E., Farago B., Ruta B., Ruocco G., Ruzicka B.. Soft Matter, 2015, 11: 466-471 CrossRef PubMed ADS Google Scholar

[52] Hohenberg P. C., Halperin B. I.. Rev. Mod. Phys., 1977, 49: 435-479 CrossRef ADS Google Scholar

[53] Whitelam S., Berthier L., Garrahan J. P.. Phys. Rev. Lett., 2004, 92: 185705 CrossRef PubMed ADS Google Scholar

[54] Pastore R., Ciamarra M. P., Coniglio A.. Fractals, 2013, 21: 1350021 CrossRef ADS arXiv Google Scholar

[55] Debenedetti P. G., Stillinger F. H.. Nature, 2001, 410: 259-267 CrossRef PubMed Google Scholar

[56] Cavagna A.. Phys. Rep., 2009, 476: 51-124 CrossRef ADS arXiv Google Scholar

[57] Royall C. P., Williams S. R.. Phys. Rep., 2015, 560: 1-75 CrossRef ADS arXiv Google Scholar

[58] Pastore R., Coniglio A., de Candia A., Fierro A., Pica Ciamarra M.. J. Stat. Mech., 2016, 2016: 054050 CrossRef ADS arXiv Google Scholar

[59] Stevenson J. D., Schmalian J., Wolynes P. G.. Nat. Phys., 2006, 2: 268-274 CrossRef ADS Google Scholar

[60] Helfferich J., Ziebert F., Frey S., Meyer H., Farago J., Blumen A., Baschnagel J.. Phys. Rev. E, 2014, 89: 042603 CrossRef PubMed ADS Google Scholar

[61] Pastore R, Pesce G, Sasso A. Many facets of intermittent dynamics in colloidal and molecular glasses. Colloids Surfs A-Physicochem Eng Aspects, 2017, 532: 87-96 CrossRef Google Scholar

[62] Pastore R., Coniglio A., Ciamarra M. P.. Soft Matter, 2015, 11: 7214-7218 CrossRef PubMed ADS arXiv Google Scholar

[63] Perakis F., Camisasca G., Lane T. J., Sp?h A., Wikfeldt K. T., Sellberg J. A., Lehmkühler F., Pathak H., Kim K. H., Amann-Winkel K., Schreck S., Song S., Sato T., Sikorski M., Eilert A., McQueen T., Ogasawara H., Nordlund D., Roseker W., Koralek J., Nelson S., Hart P., Alonso-Mori R., Feng Y., Zhu D., Robert A., Grübel G., Pettersson L. G. M., Nilsson A.. Nat. Commun., 2018, 9: 1917 CrossRef PubMed ADS Google Scholar

[64] Kikutsuji T., Kim K., Matubayasi N.. J. Chem. Phys., 2018, 148: 244501 CrossRef PubMed ADS arXiv Google Scholar

[65] T. Kikutsuji, K. Kim, and N. Matubayasi, arXiv preprint,. arXiv Google Scholar

[66] Mayer P., Bissig H., Berthier L., Cipelletti L., Garrahan J. P., Sollich P., Trappe V.. Phys. Rev. Lett., 2004, 93: 115701 CrossRef PubMed ADS Google Scholar

[67] Guan J., Wang B., Granick S.. ACS Nano, 2014, 8: 3331-3336 CrossRef PubMed Google Scholar

[68] Sch?tzel K. Correlation techniques in dynamic light scattering. Appl Phys B, 1987, 42: 193-213 CrossRef ADS Google Scholar

[69] Sch?tzel K.. Optica Acta-Int. J. Opt., 1983, 30: 155-166 CrossRef Google Scholar

[70] Lorusso G. F., Minafra A., Capozzi V.. Appl. Opt., 1993, 32: 3867-3870 CrossRef PubMed ADS Google Scholar

[71] van der Kooij H. M., Fokkink R., van der Gucht J., Sprakel J.. Sci. Rep., 2016, 6: 34383 CrossRef PubMed ADS Google Scholar

  • Figure 1

    (Color online) For a sample at volume fraction, $\Phi=0.71$, (a), (b) two frames of a portion of a sample separated by a time-lag, $\Delta~t$=10 s. (c) The resulting differential frame and (d) the square of this latter (color-scales as indicated). (e) Sequence of square differential frames at different times-lags, $\Delta~t$, as indicated (color-scale as in panel (d)). (f) Average intensity variance of differential frames, $V$, as a function of the lag-time $\Delta~t$. The dashed line indicates the long time plateau $V_{\infty}$. (g) For samples at different volume fractions, $\Phi=0.65,~0.71,~0.77,~0.79$ (from left to right): DVA dynamic order parameter, $Q(\Delta~t)$. Solid lines are Kohlrausch-Williams-Watts fit to the late decay. (h) Dynamic susceptibility, $\chi_4$, as function of the time-lag $\Delta~t$. (i) Relaxation time, $\tau$, and the time corresponding the maximum susceptibility, $\Delta~t^*$, as a function of $\Phi$. $\tau$ is fitted by a power-law $\propto~(\Phi_c-\Phi)^{-\gamma}$ (solid line), with $\gamma=2.5\pm0.2$. Inset: maximum dynamic susceptibility, $\chi_4^*$, as a function of the volume fraction, $\Phi$. The solid line is a power-law $~\propto~(\Phi_c-\Phi)^{-\alpha}$, with $\alpha=0.9\pm0.1$. Adapted from ref. [27]under a Creative Commons Attribution 4.0 International License.

  • Figure A2

    (Color online) ACII (solid line) and DVA (dashed line) dynamic order parameter as a function of $\Delta~t$ and for volume fractions, $\phi=0.65$, $0.71$ and $0.79$, from left to right, respectively.

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