Chemical and Bioprocess Engineering Theses

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This collection is made up of doctoral and master theses by research, which have been received in accordance with university regulations.

For more information, please visit the UCD Library Theses Information guide.


Recent Submissions

Now showing 1 - 5 of 11
  • Publication
    Atomistic Simulations of Metal-Oxide Interface with Water: Theoretical studies on systems of TiO2 and Fe2O3
    (University College Dublin. School of Chemical and Bioprocess Engineering, 2022) ;
    In this thesis, various systems containing interfaces of titanium dioxide (TiO2) and haematite (a-Fe2O3) with water are examined using a number of atomistic simulation methodologies. These systems include large-scale anatase (101) and rutile (110) surface slabs modelled using force-field molecular dynamics (MD); smaller haematite (001) and rutile (110) surface slabs modelled using density functional theory MD; and a large scale anatase nanoparticle and smaller anatase (101) and rutile (110) surface slabs, modelled using density functional tight-binding MD. As part of these studies a variety of analyses are presented, aimed at providing a quantitative understanding of the effects that each surface or nanoparticle has on the properties of water molecules near the interface; and thereby assessing, in a qualitative way, how these effects are manifested using the different methodologies. These analyses include established techniques in the field of atomistic simulations, such as hydrogen bond analysis and electronic density of states calculations. Also employed are techniques novel to the field of atomistic simulation, such as the coherence spectrum. Two points of emphasis are present throughout this thesis: firstly, to improve the understanding of the materials examined towards the development of photoelectrochemical catalysts; and secondly, to explore the current state-of-the-art in atomistic simulations, and "push the boundaries" of the available techniques.
  • Publication
    The use of Process Analytical Technologies to examine the viability of CHO cells
    (University College Dublin. School of Chemical and Bioprocess Engineering, 2022) ;
    The viability of mammalian cells is primarily tested by dye exclusion assays to examine the integrity of the outer membrane. Precursor events to the onset of cell death are detectable using a combination of online and offline technologies. This work explores the use of dielectric spectroscopy and impedance flow cytometry to characterize changes in the biophysical properties of cells as they progress through batch cultures. At-line single cell imaging was examined in tandem with these methods to prove further insight into the identification of morphological changes in the cell culture. This information was collated to better understand at what point cells can no longer be classified as recoverable prior to the loss in membrane integrity. Autophagic activity such as the increased presence of lysosomes was identified using digital holographic imaging. An earlier decline in the online capacitance signal relative to offline counts occurred in tandem with the onset of autophagy due the shifting dynamics of the cell population. Schwan modelling gave insight on the changes in the bulk membrane capacitance and intracellular conductivity of the cells during this period. Single cell impedance measurements were used to examine the population dynamics with greater accuracy. Opacity and phase parameters were derived at suitable frequencies and compared to the online models. Multifrequency data from the capacitance probe proved useful in the identification of apoptotic activity which followed autophagy. The Cole-Cole a and critical frequency of the changing ß-dispersion curve properties were examined relative to these starvation events. A feeding strategy was employed to delay the onset of autophagy in batch cultures, through the introduction of amino acids. Controlled refeeding experiments were shown to affect both the presence of lysosomes and shifts in opacity trends, suggesting that cells could be recovered during autophagy. The effects of such a feed on the online modelling data was examined to see if a real time parameter from the multifrequency trends could be used as an indicator for culture refeeding.
  • Publication
    Machine-learning for force-fields in molecular simulation: Water, Metal Oxides and their Interfaces
    (University College Dublin. School of Chemical and Bioprocess Engineering, 2022) ;
    Photoelectrochemical (PEC) water splitting cells, used to create hydrogen from solar energy, are crucial to the implementation of solar-to-fuel technology. Research in science and technology is focused on improving the functionality and efficiency of these devices while also ensuring that they will last for a long time and will be affordable. Machine-learning based computer-aided simulation is one of the methods exploited to design the next generation of photoelectrochemical cells. Such simulation techniques have filled the gap between conventional force fields, which are fast and inaccurate, and electronic structure simulations, which are slow and accurate. In this thesis, we developed and/or improved machine-learning interatomic potentials (MLPs) for all the chemical systems involved in the design of PEC cells. Having established the reliability of such potentials in describing the many-body share of energy for argon clusters, we then utilize neural networks as a powerful machine-learning method to construct potential energy surfaces for the other studied systems. High-dimensional neural-network (HDNN) model developed by Behler (Behler, 2007) was used to obtain the coordinates of a chemical system as input and output its total energy. We used atom-centered symmetry functions (Behler, 2011) to encode the chemical environment into a fixed-length input vector. As part of this thesis, various sampling schemes are examined and improved to construct the training set needed for model development, as well as a new technique to manipulate publicly available data for model development is implemented using machine-learning. In addition, the network's weights and biases are adjusted using different optimization techniques. We also implemented a molecular dynamics code that includes long-range electrostatic energy via Ewald sums and delta-machine-learning, and we proposed and developed a technique based on checkpoint ensembles in machine-learning to improve the accuracy of a neural network model for total energy prediction.
  • Publication
    Electric field Phenomena at Water/Metal-Oxide Interfaces
    (University College Dublin. School of Chemical and Bioprocess Engineering, 2022) ;
    Understanding effective energy-conversion systems and dealing with the problem of intermittency through scalable energy-storage systems are the two major difficulties in renewable energy. At the Grid size, relatively little progress has been done, and two considerable issues remain: (i) minimizing environmental harm, and (ii) the issue of ecologically friendly energy conversion. Light-driven photoelectrochemical (PEC) water-splitting can create hydrogen, but it is inefficient; instead, we focus on how electric fields can be applied to metal-oxide/water systems to adjust the interplay with their intrinsic electric fields, and how this can change and increase PEC activity, drawing both on experiment and non-equilibrium molecular simulation. Non-equilibrium molecular-dynamics simulations of liquid water were carried out in the canonical ensemble in the presence of both external static and oscillating electric fields of(r.m.s.) intensities 0.05 V/Å and 0.10 V/Å, with oscillating-field frequencies 50, 100 and 200 GHz. The rigid potential model TIP4P/2005 was used, and NEMD simulations were performed, including in the supercooled region, at temperatures ranging from 200 to 310 K. Significant changes in the percentage dipole alignment and self-diffusion constant were found vis-à-vis zero-field conditions, as well as shifting of the probability distribution of individual molecular self- diffusivities. The application of static fields was typically found to reduce the self-diffusion of liquid water, effectively due to some extent of "dipole-locking", or suppression of rotational motion, whereas diffusivity was found to be enhanced in oscillating fields, especially at high frequencies and outside the supercooled region. Classical molecular-dynamics techniques were used to evaluate the distribution of individual water molecules’ self-diffusivities in adsorbed layers at TiO2 surfaces anatase (101) and rutile (110) at 300 K for inner and outer adsorbed layers. Using local order parameters, the layered-water structure was identified and classed in layers, which proved to be an equally viable way of "self-ordering" molecules in layers. Anatase and rutile differed significantly in disrupting these molecular distributions, particularly in the adsorbed outer layer. Anatase (101) had much greater self-diffusivity values, owing to its "corrugated" structure, which allows for increased hydrogen bonding interaction with adsorbed molecules beyond the initial hydration layer. On the contrary, rutile (110) has more securely "trapped" water molecules in the region between Ob atoms, resulting in less mobile adsorbed layers. Finally, the dynamical properties of physically and chemically adsorbed water molecules on pristine hematite-(001) surfaces were investigated using non-equilibrium ab-initio molecular dynamics (NE -AIMD) in the NV T ensemble at room temperature, in the presence of externally applied, uniform static electric fields of increasing intensity. Significant changes in the dipole moment and self-diffusion constant were observed in comparison to zero-field circumstances, as well as a shift in the probability distribution of individual molecule self-diffusivities. For example, static fields were shown to promote the self-diffusion of water molecules at the a-Fe2O3 surface, owing to some degree of ’dipole-locking’ in the applied direction of the field.
  • Publication
    Understanding Chinese hamster ovary cell translation at sub-codon resolution
    (University College Dublin. School of Chemical and Bioprocess Engineering, 2022) ;
    Chinese hamster ovary (CHO) cells are the dominant mammalian expression host for recombinant therapeutic protein production. In terms of manufacturing efficiency, much has been accomplished in areas such as optimised transgene design and cell line development. Since the publication of the Chinese hamster genome the field has gained a more refined understanding of the relationship between the CHO biological system and desirable bioprocess traits. Despite the central importance of protein synthesis, few studies to date have focussed on characterising translation in CHO cells. The goal of this thesis is to evaluate the utility of ribosome footprint profiling (Ribo-seq) to further improve our understanding of CHO cell biology and highlight routes towards enhanced biopharmaceutical manufacturing. A key aspect of this work is the combination of multiple translation inhibitors for Ribo-seq to enable the simultaneous analysis of translation initiation and elongation for the first time. The availability of these data enabled the identification of previously uncharacterised open reading frames (ORFs) including those non-AUG start codons. Novel ORFs comprised of N-terminal extensions of canonical proteins, ORFs found in genes previously thought to be non-coding and those found in the 5’ leader sequence of mRNAs (i.e. upstream ORFs). Through the use of Ribo-seq and RNA-seq data, these upstream ORFs were found to have a repressive effect on the translation efficiency of the main ORF. In addition, following comparison of CHO cells at day 4 and day 7 of cell culture as well upon a reduction of cell culture temperature, genes undergoing differential translation were identified. A number of these genes did not have a corresponding change in gene expression, confirming that Ribo-seq can provide an additional dimension compared to using RNA-seq in isolation. Ribosome profiling has further enabled the computation of transcriptome wide decoding times for each codon, and revealed influence of codon context on translational rate. These data provide a potential route towards more efficient codon optimised transgene sequences. Perhaps the most striking finding of this work is the identification of thousands of novel small open reading frames (sORFs) predicted to encode microproteins (i.e. proteins < 100aa). Host cell protein analysis, revealed that 8 microproteins were present in adalimumab, confirming that microproteins are a novel class of potential process related impurity. In summary, ribosome footprint profiling is a powerful analytical method for improving the annotation of the CHO cell genome, understanding CHO cell biology and identification of routes to improve not only the upstream process but also enhance the characterisation of the final drug product.