Hydration Shells of DNA from the Point of View of Terahertz Time-Domain Spectroscopy

Hydration plays a fundamental role in DNA structure and functioning. However, the hydration shell has been studied only up to the scale of 10–20 water molecules per nucleotide. In the current work, hydration shells of DNA were studied in a solution by terahertz time-domain spectroscopy. The THz spectra of three DNA solutions (in water, 40mm MgCl2 and 150 mM KCl) were transformed using an effective medium model to obtain dielectric permittivities of the water phase of solutions. Then, the parameters of two relaxation bands related to bound and free water molecules, as well as to intermolecular oscillations, were calculated. The hydration shells of DNA differ from undisturbed water by the presence of strongly bound water molecules, a higher number of free molecules and an increased number of hydrogen bonds. The presence of 40 mM MgCl2 in the solution almost does not alter the hydration shell parameters. At the same time, 150 mM KCl significantly attenuates all the found effects of hydration. Different effects of salts on hydration cannot be explained by the difference in ionic strength of solutions, they should be attributed to the specific action of Mg2+ and K+ ions. The obtained results significantly expand the existing knowledge about DNA hydration and demonstrate a high potential for using the THz time-domain spectroscopy method.

Computational Modelling for the Optical Properties of Dye Molecules Adsorbed onto Metallic Nanoparticles

<p><b>The study of light scattering by particles has become fundamental and applied interests in the fields of chemistry, biology, and most importantly in physics. In this context, this thesis focuses on understanding the optical properties of dye layers adsorbed onto metallic nanoparticles (NP), which is essential for interpreting the results of plasmon-dye coupling experiments. To model such a system, Mie theory is often used to solve for the exact solution to Maxwell’s equations for spherical homogeneous and isotropic coated NP. The effects of the NP’s plasmon resonances on the optical properties of the adsorbed dye layer have been predicted using an effective medium model, where the dye-layer is treated as an isotropic layer with an effective dielectric function accounting for the dye resonance. However, this isotropic shell model is inadequate as it cannot account for the dye surface concentration and the anisotropy of the optical response of the dye layer.</b></p> <p>In this thesis, we introduce anisotropic effects within Mie theory and develop microscopic models to define effective dielectric functions which explicitly include the dye-concentration effect in the shell model. Combining anisotropic Mie theory with a concentration-dependent effective shell model allows us to form new theoretical tools to model the optical properties of adsorbed dye layers on metallic NPs of spherical shape. With this new refined effective medium model, we are then able to study shell models for elongated particles beyond the quasi-static approximation. This is implemented using the finite element method (FEM) to numerically solve Maxwell’s equations. The FEM implementation is then used to investigate how the NP’s plasmon resonance can be affected by the dye’s orientation and location on the NP’s surface. We show that the orientation and location of the dye molecules on the NP determine how strongly the plasmon resonance is shifted.</p> <p>The results of this work will improve our ability to accurately model the optical properties of anisotropic molecules adsorbed on metallic NPs. This is important in a number of applications including the development of localised surface plasmon resonance (LSPR) sensing and the design of plasmonic devices.</p>

Computational Modelling for the Optical Properties of Dye Molecules Adsorbed onto Metallic Nanoparticles

<p><b>The study of light scattering by particles has become fundamental and applied interests in the fields of chemistry, biology, and most importantly in physics. In this context, this thesis focuses on understanding the optical properties of dye layers adsorbed onto metallic nanoparticles (NP), which is essential for interpreting the results of plasmon-dye coupling experiments. To model such a system, Mie theory is often used to solve for the exact solution to Maxwell’s equations for spherical homogeneous and isotropic coated NP. The effects of the NP’s plasmon resonances on the optical properties of the adsorbed dye layer have been predicted using an effective medium model, where the dye-layer is treated as an isotropic layer with an effective dielectric function accounting for the dye resonance. However, this isotropic shell model is inadequate as it cannot account for the dye surface concentration and the anisotropy of the optical response of the dye layer.</b></p> <p>In this thesis, we introduce anisotropic effects within Mie theory and develop microscopic models to define effective dielectric functions which explicitly include the dye-concentration effect in the shell model. Combining anisotropic Mie theory with a concentration-dependent effective shell model allows us to form new theoretical tools to model the optical properties of adsorbed dye layers on metallic NPs of spherical shape. With this new refined effective medium model, we are then able to study shell models for elongated particles beyond the quasi-static approximation. This is implemented using the finite element method (FEM) to numerically solve Maxwell’s equations. The FEM implementation is then used to investigate how the NP’s plasmon resonance can be affected by the dye’s orientation and location on the NP’s surface. We show that the orientation and location of the dye molecules on the NP determine how strongly the plasmon resonance is shifted.</p> <p>The results of this work will improve our ability to accurately model the optical properties of anisotropic molecules adsorbed on metallic NPs. This is important in a number of applications including the development of localised surface plasmon resonance (LSPR) sensing and the design of plasmonic devices.</p>

Effective Medium Model Applied to Biopolymer Solutions

Studying dielectric properties of heterogeneous systems is challenged by a problem of uncertainty of the ratio between dielectric permittivity of the system and dielectric permittivities of its components. Such ratios can be obtained in some cases using theoretical effective medium models. However, such models have not yet been developed for all the systems possible. Particularly, there is no effective medium model with filamentary inclusions. Such a theoretical model elaborated based on the fundamental principles of electrodynamics of continuous media is suggested in the present work. Any point of a filamentary inclusion with a length that is significantly greater than the thickness can be regarded as being located in a long cylinder-like fragment of the inclusion with stochastic direction of the cylinder axis relative to the external electric field. With this regard, electric field strength and electric induction values were averaged across the entire volume of a two-phase dielectric material. As a result, a model linking the dielectric permittivity of the two-phase system and the dielectric permittivities of both phases was elaborated. The model appears to be highly relevant for studying solutions of biopolymers, such as nucleic acids, fibrillar proteins and protein aggregates, polysaccharides, by means of electrical impedance spectroscopy, dielectric spectroscopy, and terahertz time-domain spectroscopy. The suggested theoretical model was successfully validated on a DNA solution within the terahertz region.

Simplified Seismic Modelling of Fractured Rock: How Effective is the Localised Effective Medium Compared to Explicit Representation of Individual Fractures

Seismic waves can be an effective probe to retrieve fracture properties particularly when measurements are coupled with forward and inverse modelling. These seismic models then need an appropriate representation of the fracturing. The fractures can be modelled either explicitly, considering zero thickness frictional slip surfaces, or by considering an effective medium which incorporates the effect of the fractures into the properties of the medium, creating anisotropy in the wave velocities. In this work, we use a third approach which is a hybrid of the previous two. The area surrounding the predefined fracture is treated as an effective medium and the rest of the medium is made homogeneous and isotropic, creating a Localised Effective Medium (LEM). LEM can be as accurate as the explicit but more efficient in run-time. We have shown that the LEM model can closely match an explicit model in reproducing waveforms recorded in a laboratory experiment, for wave propagating parallel and perpendicular to the fractures. The LEM model performs close to the explicit model when the wavelength is much larger than the element size and larger than the fracture spacing. By the definition of the LEM model, we expect that as the LEM layer becomes coarser the model will start approaching the effective medium result. However, what are the limitations of the LEM and is there a balance between the stiffness, the frequency and the thickness, where the LEM performs close to an explicit model or approaches the effective medium model? To define the limits of the LEM we experiment varying fracture stiffness and source frequency. We then compare for each frequency and stiffness the explicit and effective medium with five models of LEM with different thickness. Finally, we conclude that the thick LEM layers with lower resolution perform the same as the thinner and finer resolution LEM layers for lower frequencies and higher fracture stiffness.

Porous and cracked rocks elasticity: Macroscopic poroelasticity and effective media theory

Macroscopic poroelasticity and effective medium theory are two independent approaches which can be used to analyze the role of pores, cracks, and fluid on elastic properties. Macroscopic poroelasticity belongs to the macroscopic framework of thermodynamics whereas effective medium theory expresses the medium properties in terms of microstructural characteristics (pore and crack shape, etc.) and component properties (fluid properties, solid grain properties, etc.). In this paper, we review the fundamental assumptions and results of both approaches, and show that they are complementary but do not apply over the same range of conditions. A compilation of data is reported, in various dry and saturated rocks, to show the validity of the Gassmann equation and the dispersion between unrelaxed modulus –where effective medium model applies- and relaxed modulus –where poroelasticity applies.

DEEP DIELECTRIC-BASED WATER SATURATION IN FRESHWATER AND MIXED SALINITY ENVIRONMENTS

It is desirable to evaluate the possibility of developing a deeper dielectric permittivity based Sw measurement for various petrophysical applications. The low frequency, (< MHz), resistivity-based method for water saturation (Sw) evaluation is the desired method in the industry due to its deepest depth of investigation (DOI, up to 8 ft). However, the method suffers from higher uncertainty when formation water is very fresh or has mixed salinity. Dielectric permittivity and conductivity dispersion have been used to estimate Sw and salinity. The current dielectric dispersion tools, however, have very shallow DOI due to their high measurement frequency up to GHz, which most likely confines the measurements within the near wellbore mud-filtrate invaded zones. In this study, effective medium-model simulations were conducted to study different electromagnetic (EM) induced-polarization effects and their relationships to rock petrophysical properties. Special attention is placed on the complex conductivity at 2 MHz due to its availability in current logging tools. It is known that the complex dielectric saturation interpretation at the MHz range is quite difficult due to lack of fully understood of physics principles on complex dielectric responses, especially when only single frequency signal is used. Therefore, our study is focused on selected key parameters: water-filled porosity, salinity, and grain shape, and their effects on the modeled formation conductivity and permittivity. To simulate field logs, some of the petrophysical parameters mentioned above are generated randomly within expected ranges. Formation conductivity and permittivity are then calculated using our petrophysical model. The calculated results are then mixed with random noises of 10% to make them more realistic like downhole logs. The synthetic conductivity and permittivity logs are used as inputs in a neural network application to explore possible correlations with water-filled porosity. It is found that while the conductivity and permittivity logs are generated from randomly selected petrophysical parameters, they are highly correlated with water-filled porosity. Furthermore, if new conductivity and permittivity logs are generated with different petrophysical parameters, the correlations defined before can be used to predict water-filled porosity in the new datasets. We also found that for freshwater environments, the conductivity has much lower correlation with water-filled porosity than the one derived from the permittivity. However, the correlations are always improved when both conductivity and permittivity were used. This exercise serves as proof of concept, which opens an opportunity for field data applications. Field logs confirm the findings in the model simulations. Two propagation resistivity logs measured at 2 MHz are processed to calculate formation conductivity and permittivity. Using independently estimated water-filled porosity, a model was trained using a neural network for one of the logs. Excellent correlation between formation conductivity and permittivity and water-filled porosity is observed for the trained model. This neural network- generated model can be used to predict water content from other logs collected from different wells with a coefficient of correlation up to 96%. Best practices are provided on the performance of using conductivity and permittivity to predict water-filled porosity. These include how to effectively train the neural network correlation models, general applications of the trained model for logs from different fields. With the established methodology, deep dielectric-based water saturation in freshwater and mixed salinity environments is obtained for enhanced formation evaluation, well placement, and reservoir saturation monitoring.