Dynamic light scattering | EPFL Graph Search

Dynamic light scattering (DLS) is a technique in physics that can be used to determine the size distribution profile of small particles in suspension or polymers in solution. In the scope of DLS, temporal fluctuations are usually analyzed using the intensity or photon auto-correlation function (also known as photon correlation spectroscopy - PCS or quasi-elastic light scattering - QELS). In the time domain analysis, the autocorrelation function (ACF) usually decays starting from zero delay time, and faster dynamics due to smaller particles lead to faster decorrelation of scattered intensity trace. It has been shown that the intensity ACF is the Fourier transform of the power spectrum, and therefore the DLS measurements can be equally well performed in the spectral domain. DLS can also be used to probe the behavior of complex fluids such as concentrated polymer solutions. Setup A monochromatic light source, usually a laser, is shot through a polarizer and into a sample. The s

Polyphenols as Morphogenetic Agents for the Controlled Synthesis of Mesoporous Silica Nanoparticles

Jialuo Luo, Alina Osypova, René M. Rossi, Fabien Sorin

Non-surfactant-induced synthesis of mesoporous silica nanoparticles (MSNPs) is gaining increasing interest because of their low toxicity and simple purification compared to conventional surfactant-based methods. Tannic acid (TA), considered as a glucose-derived polyphenol, was first employed a few years ago and has attracted great research interest. Despite recent progress, the mechanisms resulting in the porous structure remain to be elucidated. In this work, we have employed TA and four structurally related polyphenols (gallic acid, ethyl gallate, eudesmic acid, and quercetin) to elucidate the effect of the chemical structure and properties of polyphenols on their templating ability. Our results unravel the mechanism of MSNP formation templated by TA, which form a supramolecular framework as the skeleton for the silica species to attach. The structure of the supramolecular network results in irregular pores. Additionally, the pK(a) value of the templates may be accounted for the particle size. Small-angle X-ray scattering was used to provide precise information on the morphology, especially the porosity of the resulting MSNPs in addition to electron microscopy, dynamic light scattering, nitrogen adsorption and desorption Brunauer-Emmett-Teller method, and thermogravimetric analysis.

AMER CHEMICAL SOC2019

Scaling properties of DNA knots studied by atomic force microscopy

Erika Ercolini

The main subject of the present thesis is the experimental study of the scaling properties of DNA knots of different complexities by tapping mode atomic force microscopy (AFM) in air. Homo- or heterogeneous mixture of DNA knot types were deposited onto mica in regimes of (i) strong adsorption, which induces a kinetic trapping of the molecules, and of (ii) weak adsorption, which permits relaxation on the surface. The contour of each knotted molecule was analyzed by a box counting algorithm, giving the number of boxes containing a part of the molecule, N(L), as a function of the box size, L, allowing to recover the relation N(L) ≈ L-df, where df is the fractal dimension and ν = 1/df is the scaling exponent. This relationship is complicated by the presence of a persistence length of DNA (about 50 nm) which introduces a crossover from a rigid rod behavior to a self-avoiding walk behavior. In (i) the radius of gyration of the adsorbed DNA knot scales with the 3D Flory exponent ν ≈ 0.58 within error. In (ii), the value ν ≈ 0.66, intermediate between the 3D and 2D (ν = 3/4) exponents, was found, indicating an incomplete 2D relaxation or a different polymer universality class. A different analysis, where the fractal dimension was determined by a customized box counting algorithm giving the knot mass as a function of the box size, yielded compatible results. In the case of weak binding conditions, AFM images show evidence of the localization of knot crossings, which is an effect theoretically predicted for knotted polymers confined in two dimensions (flat knots). Part of this thesis is dedicated to a fascinating application of AFM: the imaging of biomolecules in an aqueous buffer. In particular, images of plasmids adsorbed to mica strong enough to be visualized and loosely enough to see them moving in consecutive scans will be shown. The possibility of imaging under buffer molecules loosely anchored to the substrate opens the way to the investigation of biological processes near physiological conditions. The first step of the study of homologous recombination will be presented. We will also show images concerning our study of the activity of topoisomerase I on supercoiled plasmids. Clusters of DNA molecules and proteins were found when imaging in presence of magnesium, in agreement with recent findings by AFM in air. The research on topoisomerases and their interactions with inhibitors or poisons is a hot topic, being these enzymes targets of anti-cancer drugs. Our study paves the way to the investigation of the activity of topoisomerase II, the enzyme which removes knots from DNA. Finally, the principles of static and dynamic light scattering, and the results of dynamic light scattering on latex beads of known size will be presented. Particular emphasis will be given to the discussion of the contribution of light scattering techniques for future experiments on the study of the static and dynamic properties of DNA knotted molecules in solution. These experiments would contribute to shed light on the possible dependence of the gyration radius of knotted polymers on the knot type, and would allow testing theoretical predictions about their relaxation time.

EPFL2008

Sparsely Observed Functional Time Series: Theory and Applications

Tomas Rubin

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well happen that the statistician only has access to a relatively low number of sparse measurements for each random curve. These discrete measurements may be moreover irregularly scattered in each curve's domain, missing altogether for some curves, and be contaminated by measurement noise. This sparse sampling protocol escapes from the reach of established estimators in functional time series analysis and therefore requires development of a novel methodology. The core objective of this thesis is development of a non-parametric statistical toolbox for analysis of sparsely observed functional time series data. Assuming smoothness of the latent curves, we construct a local-polynomial-smoother based estimator of the spectral density operator producing a consistent estimator of the complete second order structure of the data. Moreover, the spectral domain recovery approach allows for prediction of latent curve data at a given time by borrowing strength from the estimated dynamic correlations in the entire time series across time. Further to predicting the latent curves from their noisy point samples, the method fills in gaps in the sequence (curves nowhere sampled), denoises the data, and serves as a basis for forecasting. A classical non-parametric apparatus for encoding the dependence between a pair of or among a multiple functional time series, whether sparsely or fully observed, is the functional lagged regression model. This consists of a linear filter between the regressors time series and the response. We show how to tailor the smoother based estimators for the estimation of the cross-spectral density operators and the cross-covariance operators and, by means of spectral truncation and Tikhonov regularisation techniques, how to estimate the lagged regression filter and predict the response process. The simulation studies revealed the following findings: (i) if one has freedom to design a sampling scheme with a fixed number of measurements, it is advantageous to sparsely distribute these measurements in a longer time horizon rather than concentrating over a shorter time horizon to achieve dense measurements in order to diminish the spectral density estimation error, (ii) the developed functional recovery predictor surpasses the static predictor not exploiting the temporal dependence, (iii) neither of the two considered regularisation techniques can, in general, dominate the other for the estimation in functional lagged regression models. The new methodologies are illustrated by applications to real data: the meteorological data revolving around the fair-weather atmospheric electricity measured in Tashkent, Uzbekistan, and at Wank mountain, Germany; and a case study analysing the dependence of the US Treasury yield curve on macroeconomic variables. As a secondary contribution, we present a novel simulation method for general stationary functional time series defined through their spectral properties. A simulation study shows universality of such approach and superiority of the spectral domain simulation over the temporal domain in some situations.