Hydrogen, as an energy carrier, is of paramount importance in the energy transition. At the industrial level, it can be derived from various sources, including fossil fuels, biomass, or electrolysis. In Alkaline Water Electrolysis (AWE), the growth of hydrogen bubbles directly impacts system efficiency. Understanding and simulating this growth, attributed to the diffusion of dissolved hydrogen in the supersaturated electrolyte near nucleation sites via diffusive and convective mass transfer, is a crucial step towards advancing knowledge in this field and unlocking new possibilities.

This research focuses on simulating the growth of a single hydrogen bubble in a supersaturated domain, both far from and near the cathode, in a 30 wt% KOH solution. Bubble growth, a mesoscale phenomenon, is investigated using the Lattice Boltzmann Method (LBM). A comprehensive comparison of the Shan-Chen (SC), Colour Gradient (RK), and Interface Tracking Phase-Field (HZC) methods was conducted to measure the intricacies of the multiphase system accurately. The Laplace Law equation served as a benchmark, demonstrating that the HZC method produced the most accurate results.

A continuous species transfer method is employed to track hydrogen transport from the supersaturated electrolyte into the bubble, validated with Newman's analytical solution of mass transfer controlled by pure diffusion inside a sphere. Two cases are then analyzed: one of a single bubble far from an electrode in a supersaturated domain and another of a single bubble near an electrode with a constant hydrogen flux. For the first case, bubble growth follows a power law equivalent to R ∼ t^0.5, while in the second case, growth follows R ∼ t^0.7, matching results from previous studies. Finally, this method is extended to a 3D model; however, the results cannot be directly compared to the 2D model due to the shorter runtime resulting from computational cost.

Capacitive deionisation (CDI) is a desalination technology that removes ionic species from water through electrostatic attraction. Recently, a new CDI cell architecture has been developed with multiple channels and electrodes. Here, the ions are driven to migrate from channel to channel to desalinate the water. The inner electrodes of such a cell function as selective membranes when charged, therefore called capacitive membrane electrodes (CME). This thesis used an experimental approach to investigate the effect of the electrostatic charge and the microstructure of the CMEs on the permselectivity of these CMEs. Five different CMEs were tested in a cell to determine their permselectivities. A porometer (utilising the capillary flow technique) was used to determine the pore size distributions of the CMEs. When comparing the data from both measurements, it was found that CMEs with a higher fluid permeability also showed lower ionic resistances. A lower ionic resistance indicates a lower permselectivity. To achieve good desalination performance in multi-channel CDI, CMEs must offer low ionic resistance when ions are driven from low salinity to high salinity, while still being permselective when ions want to migrate back from high to low salinity.

Enhancing the efficiency of industrial water electrolysis for hydrogen production is vital for the energy transition. In Alkaline Water Electrolysis (AWE), hydrogen is produced at the cathode, and the bubbles are formed when the local hydrogen concentration exceeds the solubility limit. It is important to understand the exact local conditions that result in the nucleation of bubbles in this multi-phase and reactive system. With modeling, it is possible to gain insight into the relation between various local properties, but the model needs to include all relevant physics and chemistry. Thus, this work focuses on the multi-species electrochemical transport phenomena with reaction occurring on the electrode-electrolyte interface.

The electrochemical transport phenomena and the bubble nucleation are meso-scale phenomena occurring at the electrode-electrolyte interface. Lattice Boltzmann Method (LBM) is well suited for modeling meso- scale behavior but it is computationally memory expensive. Consequently, a hybrid approach combining Finite Difference Method (FDM) and LBM has been developed to simulate transport phenomena in the migration-diffusion problem with heterogeneous reaction kinetics. The Debye-Hückel theory is used as a benchmark to validate the developed model. Subsequently, the model is employed to simulate the transport phenomena occurring in the hydrogen half-cell of AWE, with a specific focus on the Hydrogen Evolution Reaction (HER) governed by the Butler-Volmer kinetics equation.

The model captures the dynamic evolution of physical parameters such as electric potential, concentration of species, and fluxes within the system particularly in the Electric-Double layer (EDL). The effect of electrode potential on the distribution of species involved in the reaction are studied by performing simulations for different electrode potential. The influence of secondary fluxes on the species distribution
is studied by implementing a spatially varying boundary condition to the reacting site. Finally, the formulated methodology is extended to solve a multi-phase system with species transportation occurring
around a catalyst particle.

Fresh water is a scarce source. Demand for freshwater is greater than supply, due to the increasing industrialisation of countries and the low natural supply of freshwater sources such as rivers and lakes. Contamination of these natural freshwater sources by industrial wastewater adds to the freshwater deficit. Throughout history, technologies have been developed to increase the supply of fresh water by desalinating other water sources into fresh water. Reverse osmosis is the most widely used desalination technology for seawater desalination, but the energy transition calls for electric-based technologies that can desalinate water using renewable energy sources. Capacitive deionisation is a relatively new desalination technology that uses electrical energy to desalinate brackish water. The capacitive deionisation cell is an electrochemical cell in which the feed water flows between two electrodes. When electricity is applied to these electrodes, ions in the water are attracted to move towards the porous electrodes where they are adsorbed within the electrical double layer. Unfortunately, the capacitive deionisation system has some challenges: it is a batch-system, has a low freshwater production and it has not been built for large-scale production. Avsalt AB, a Swedish company, has developed a new capacitive deionisation architecture to address these challenges, called the multichannel capacitive deionisation system (MC-CDI). This system consists of two capacitive porous electrodes and several membrane-like electrodes between them, referred to as capacitive membrane electrodes (CMEs). To maximise the performance of the multichannel deionisation system, the performance of the capacitive membrane electrodes should be enhanced. Therefore, the aim of his thesis is to optimise the physical properties of the capacitive membrane electrodes that affect the performance of the capacitive membrane electrodes and to gain a better understanding of how the microscopic properties of the CMEs should be carefully tailored to improve the performance.
To address these questions we can turn to Electrochemical Impedance Spectroscopy (EIS). EIS is a non-invasive measurement technique that may be regarded as a much more sophisticated resistance measurement compared to, for example, a multimeter. In contrast to the latter device, EIS measures the impedance, which is a combination of the resistance and the reactance, at a wide range of frequencies. The frequency dependency of the measured impedance can be used to find a so-called equivalent electrical circuit model (EECM). For EIS measurements on electrochemical cells, such as batteries, fuel cells, and electrolysers, the EECM elucidates the different electrochemical processes that occur at different timescales and impedance plots are used to analyse and compare these electrochemical processes. EIS has been successfully used to analyse the electrochemical processes within CDI electrodes and ion exchange membranes, but capacitive membrane electrodes used in an MC-CDI system have never been studied with an accurate and fast measurement technique such as EIS. Because CMEs do not have a solid support structure, in contrast to most CDI electrodes, ions are free to migrate through the electrodes. Therefore, the alternating current that is needed in EIS measurements can be applied on an external set of electrodes, while the response alternating voltage can be picked up at the CMEs. This 4-point impedance measurement configuration enables precise determination of the ionic resistance and capacitance of the electrode material. Therefore, an electrochemical impedance spectroscopy setup is built to determine the performance indicators of the CMEs and to gain a better understanding of the electrochemical processes taking place at the interface of the CMEs. The resulting Nyquist and Bode plots are used to analyse the ion diffusion and capacitive behaviour of the electrodes. To enable the analysis, first, an equivalent electrical circuit is determined for these freestanding electrodes. It was found that the CME can be represented by the Transmission-line model, which in the equivalent electrical circuit takes the form of a junction of three complex impedances. From the values of the electrochemical parameters, the performance indicators of the CME, membrane conductivity and permselectivity, were evaluated.
The CME meter presents itself as an accurate measurement system to quickly evaluate the performance indicators of the CME, with the aim of efficiently searching for the CME that will show the best performance within the MC-CDI system, without placing it within the MC-CDI system. Further research should be conducted to investigate the extent to which EIS could be used to estimate the permselectivity of the CME and whether the total measurement time to predict permselectivity is still short enough to propose EIS as an alternative measurement technique.

In this thesis, a proof of concept was established for the use of a novel coupled QM-MD approach to modelling metallic (copper) electrode-electrolyte interfaces. SCC-DFTB calculations of the instantaneous electronic structure of a copper electrode were coupled to a classical MD simulation of an electrode-electrolyte interface. The applied QM-MD method was described rigorously, and used to investigate the compound distribution and dynamics at the interface, relative to a fully classical MD simulation. Polarisation effects were observed to bring about a significant increase in the attraction between cations and the cathode. Moreover, local polarisation of the cathode was found to immobilise adsorbed cations, and induce an increased orientational preference of the nearby water dipoles. The secondary goal of this thesis was to explore to what extent neural networks are able to replicate SCC-DFTB calculations of the electronic charge density on a metallic electrode. Using a computer vision approach, qualitative evidence was obtained indicating that neural networks can be used to replicate SCC-DFTB predictions on periodic metallic surfaces.

This thesis aims to find out whether electrokinetic transport of a calcium chloride solution can be controlled by adding sodium ions by performing molecular dynamics simulations. The idea that electrokinetic transport control is a possibility originates from research that found that charge inversion is reduced when monovalent ions are added to a multivalent solution. Consequently, flow reversal suppression is expected. Many mechanisms are known to contribute to charge inversion and ion competition, but it is unclear how they exactly relate to charge inversion reduction and flow reversal suppression. This work aims to replicate charge inversion reduction and flow reversal suppression in a system with an amorphous silica interface for mixed electrolytes with calcium, sodium and chloride. To allow insight into the structural behaviour of the electrical double layer and the electrokinetic properties for varying concentrations, molecular dynamics simula- tions are used. No variation in charge inversion and flow reversal reduction was found for the simulated concentrations. However, the adsorption behaviour of ions in the electrical double layer changed due to ion competition between sodium and calcium, where sodium outcompetes calcium for inner sphere surface complex adsorption. This work also shows that the electroosmotic flow behaviour for mixtures is sensitive to the dynamic adsorption behaviour of ions, emphasizing the importance of correctly tuned force field parameters between surface and ions.

Force field is widely used to model the potential energy in atomistic simulation systems. Despite force fields have a concise mathematical form, a good set of force field parameters usually requires extra care of calibration. Besides, numerous ionic force field parameters are reported from various sources as researchers have specific target properties for their interests. Previous studies mainly used brute force optimization to find the most desired set of parameters in ionic solution. However, these methods are not efficient since the evaluation of the performance of a parameter set is time-consuming. This work used a stochastic optimization routine in machine learning to tackle the problem of black-box function optimization. This method shows excellent performance of locating the optimum regions of the black-box cost function in only a few iterations. To evaluate the performance of a set of ionic force field parameters, MD simulations are carried out in LAMMPS to compute ionic properties. The solvation free energy and ion oxygen distance are selected as the primary targets while the self-diffusion coefficient and contact ion pairs are regarded as the secondary targets. The optimum region of primary targets are found by direct optimization, then secondary targets are studied with optimized parameters of the primary targets. There have been found discrepancies between the optimum regions of different targeted properties. The dependence studies of individual ionic force field parameters ($\epsilon, \sigma, q$) are analyzed and parameterization trends are found out. Base on these trends, the final calibration model is proposed.

In the pursuit of gaining a better understanding of the mechanisms of oxide-electrolyte interfaces, this thesis presents a working model that mimics a dynamic surface charge distribution by introducing protonation and deprotonation events using MD simulation. Due to the limitations of measurement equipment that operate on an atomic scale, literature cannot provide us with exact time scales for protonation and deprotonation events. Consequently, previous research simulated the surface charge distribution of oxide surfaces as being static and assumed the effect of local protonation and deprotonation to be negligible. This work shows that varying the (de)protonation event period τ significantly influences the characteristics of the electric double layer (EDL). Continuous protonation and deprotonation changes the diffusion coefficient and subsequently alters the structure of the Stern layer, screening function, and preferential adsorption type. As a whole, dynamic surface charge distribution has a considerable impact on the characteristics of the electric double layer depending on τ and should be considered in future MD simulations.

Crystallization is employed in a wide range of industries but our ability to control it remains far from perfect. New methods are being continuously developed and improved to provide enhanced kinetics and control. Non-photochemical laser induced nucleation (NPLIN) is one of the avenues being looked into extensively since its accidental discovery about two decades ago. Despite providing improved nucleation kinetics and potential polymorph control, the mechanism behind NPLIN is still unknown. Four different theories have been proposed in the literature with varying amounts of agreement between
different research groups.
The main aim of this thesis is to try to determine the mechanism behind NPLIN. This report can be divided into two parts, each focusing on a possible mechanism. The first is the optical Kerr effect,
which involves investigating the effect of polarization of light on glycine polymorph formed. This is achieved by varying the laser light polarisation and number of pulses for a range of glycine
supersaturation. The second part deals with an experimental setup designed to work with microscale volumes. This will give us the capability to isolate the nuclei and observe the events leading up to their formation.
For studying the optical Kerr effect, the experiment performed by Sun et al. was repeated. A significant temperature increase inside the solution was obtained because of exposure to a high number
of pulses (600) of infrared light. No dependence of laser light polarization on polymorph formation was found. The polymorph formed by laser is different than that obtained by crash cooling. In the
second part of the thesis, the attention is shifted towards the role of impurities present in the solution which can also absorb the laser light leading to formation of a cavitation bubble. This possibility was
examined with the help of the setup mentioned above. It was noted that the crystals were nucleating at multiple points around the laser focus at a distance which is similar to the size of the cavitation bubble previously reported in literature. These observations made can be attributed towards the presence of a bubble.

For the design and optimization of different processes and technologies in the chemical and petrochemical industry, the knowledge of the accurate vapor-liquid phase equilibrium of hydrocarbons and their binary mixtures is fundamental. The main focus of this thesis is the binary mixtures of methane with various n-alkanes and the binary mixture of methane and toluene, at temperatures ranging from 400 to 650 K and pressures ranging from 2 MPa to 50 MPa. Despite the currently increased importance of these asymmetric binary mixtures of methane and long n-alkanes due to Enhanced Oil Recovery technologies and depletion of old, easily accessible and highly profitable hydrocarbon reservoirs, the available vapor-liquid equilibrium (VLE) data from experiments are scarce or unknown.Currently, the most common practice for volumetric and phase behavior calculations in the industry is based on different cubic, such as Peng-Robinson, SRK or on higher order equations of state like PC-SAFT . However, the predicted data by equations of state are not accurate enough, due to the lack of experimental data. This is more pronounced for high temperature and pressure conditions or in the vicinity of the critical point. This thesis aims to produce vapor-liquid phase equilibrium data for binary mixtures of methane with various long n-alkanes by performing Monte Carlo molecular simulations in the Gibbs ensemble with TraPPE force field. The simulation results are used to validate the applied TraPPE force field by comparing its results to available experimental data. At extrapolated conditions, the new data are compared to PC-SAFT predictions in order to assess the performance of the CBMC technique and to highlight the deviations between the CBMC and PC-SAFT results. Additionally, this new data could be used to adjust the parameters of the applied PC-SAFT equation of state to achieve better predictions at extrapolated conditions.