Studentská vědecká konference 2017

The importance and awareness of lipid nanoparticles (LNP’s) have significantly increased with the COVID-19 outbreak. During this pandemic, LNP’s demonstrated their effectiveness as a practical drug delivery system. However, for the m-RNA to be effectively delivered to a human body, a compatible lipidic composition had to be found.  Ensuring the compatibility between the drug and the phospholipid 'barrier' is generally essential and given the vast array of drugs available, the discovery of new phospholipid compositions remains a continual necessity.   Our research explores unconventional phospholipid bilayers containing the ladderane motive. This unique structural feature, formed by conjugated cyclobutenes or cyclohexanes, is naturally found in the organelles of ammonium-oxidizing bacteria, though its exact purpose within these organisms remains a mystery. Thus, we decided to conduct molecular dynamics simulations on bilayers containing ladderane phospholipids or alcohols. This theoretical exploration aims to provide new insights into their properties, broadening our knowledge in both bacterial and pharmaceutical perspective. 

Cell culture—the process of growing cells in physiological conditions outside their natural surroundings, has been a valuable technique for studying the biology of cells from multicellular organisms by providing an in vitro model of the tissue in a defined environment that can be readily modified and analysed. Currently, research in tissue engineering, stem cells, and cell biology in general relies mostly on culture of cells grown on flat plastic surfaces. Surface patterning has become increasingly useful in biomedical research and applications. It offers a way to study cell behaviour within an environment that more closely mirrors the complex cellular microenvironment, rather than relying on cell culture on a flat surface. This has opened new avenues for exploring cell behaviour in a more accessible and accurate manner. The aim of this work is to fabricate periodical patterned surfaces, by several methods of preparation, and study the effects of obtained surface patterning on cell proliferation, adhesion, morphology, and viability.  

The subnanometer size of the Cu_5 – nPd_n (0 ≤ n ≤ 5) clusters grants them different behaviour compared to the bulk material. The properties of said clusters are not only composition-dependent, but also size-dependent, meaning that their properties can be greatly altered by even one atom change in their structure. Our main goal was to see how the swapping of one atom in a Cu-Pd pentamer cluster affects its activity and selectivity. We tested each Cu-Pd pentamer during oxidative dehydrogenation of cyclohexene. From our six pentamers (Cu₅, Cu₄Pd₁, Cu₃Pd₂, Cu₂Pd₃, Cu₁Pd₄, Pd₅) were active 5 clusters containing palladium, cluster Cu₅ was inactive throughout testing. We observed a major difference in the activity of mixed clusters during the first and second ramp (lower activity during the second ramp). There was no significant change in the activity of the cluster Pd₅. Even with the change in activity, there was no notable change in selectivity. The selectivity of the bimetallic clusters was 100% toward benzene and the selectivity of Pd₅ was over 98%, with produced cyclohexadiene and CO₂ less than 1% each. The findings from this study show the difference in the properties of each cluster (exact composition plays a key role). It also shows the potential of mixed clusters in catalysis.  

The surge in renewable energy and weather-of-day dependency emphasize a need for safe and reliable stationary energy storage. Redox flow batteries (RFBs) presently offer the perspective storage solution. Most RFBs rely on vanadium salts, yet the fluctuating vanadium cost motivates research for alternatives. Organic electrolyte-based RFBs (ORFBs) can be significantly cheaper and modified to meet desired requirements. In this work, we developed a mathematical model to study and enhance the performance of organic redox flow battery. We have separated the battery into four compartments: positive and negative half-cells, and two tanks. The model describes the change in concentration of each chemical component and the change in volume of electrolytes. The membrane transport of active species is described by the Nernst-Planck equation and the transport of water is driven by osmotic pressure difference. To describe electrochemical properties, we use the Tafel equation to calculate overpotential at the electrodes, Ohm’s law to determine voltage drop over the membrane, and the Nernst equation for equilibrium potential. The model results are validated against experimental measurements. Using the model results, we can increase the battery performance and efficiency for real-world use.  

The most widely produced renewable and biodegradable polymer is polylactic acid (PLA). A multiscale mathematical model has been built to determine rheological properties of PLA melt during production with the potential to become a soft sensor in on-line optimization and control of a  polymerization reactor. Unfortunately, prediction of detailed polymer molecular architecture using the Monte Carlo method (which is incorporated in the developed model) is computationally demanding, thereby limiting use of the model in real time during production. Therefore, the goal of this work is to develop an artificial neural network (ANN) as a surrogate model to be used in real-time PLA production. The structure of the employed ANN is presented along with a description of the process of deep learning as well as sampling points generation in the model parametric space using the Latin hypercube sampling method.  

With increasing population growth and ever-more-threatening climate changes, freshwater accessibility is becoming one of the biggest concerns of modern society. Since the predominant portion of Earth's water is saline, harnessing freshwater from saltwater reservoirs is a direct and viable solution. Among the various water desalination technologies (e.g. reverse osmosis, electrodialysis), capacitive deionization (CDI) stands out as a great alternative for the desalination of low-salinity water. CDI is a groundbreaking electrokinetic water purification technology which holds immense potential to address these global freshwater scarcity challenges, due to its energy efficiency and versatility. The CDI process resides in applying an external voltage across two parallel electrodes, between which a concentrated solution is directed. As the solution flows alongside these electrodes, dissolved ions are carried by the direct electrical current to the charged electrodes, where they are temporarily stored. Thus, a deionized solution exits the system. The main goals of this project are to (i) employ nanostructured materials as electrodes in CDI systems,  (ii) characterize the CDI process in such systems, and (iii) determine the influence of process conditions.  

Recovering from a bone fracture is a very complex and strenuous process. In the cases of especially complex fractures with bone loss, the human body isn´t capable of fully recovering on its own.  In these situations, where surgical intervention is required, bone tissue engineering can be used to create bone implants, providing structural support for the growth of a new bone. Hydroxyapatite (HA) has been widely used for these purposes due to its natural bioactive properties. However, to ensure favorable conditions for the growth of the bone tissue on the prepared scaffolds in addition to being biocompatible the scaffold's porosity needs to be within a specific size range. Standard methods of scaffold preparation are not able to guarantee the desired porosity and thus, there has been a continuous search for novel means of HA scaffold production. 3D printing is emerging as one of the most promising methods, allowing for the material shaping of very complex scaffolds. This technique meets the individual patients' requirements while being able to produce the desired porosity distribution within the implant. In this work, we have explored the possibility of using commercial 3D printers to produce sintered HA scaffolds and evaluated the scaffold's mechanical and biological properties.

Transforming raw pharmaceutical materials into a desired product is a demanding and expensive process including many unit operations such as milling, granulation, coating and tablet pressing.  Spray drying is a method of transforming liquid feed into a fine powder, which serves as a base for said procedures. Lowering the size of drug particles greatly improves their overall solubility, since the surface to volume ratio also increases. To produce nano-sized drug particles using spray drying, we can use anti-solvent precipitation creating suspension, which serves as a stock feed. The issue of suspension instability is solved by immediately feeding the suspension into the spray drier, interrupting further crystal growth. By adding hydrophilic excipient into the anti-solvent (aqueous) phase, the produced powder forms an amorphous excipient matrix containing crystalline drug with improved water solubility and powder flow properties.   The aim of my work is finding a method incorporating spray drying and anti-solvent precipitation of producing powder that would be ready for direct tablet pressing directly after the spray drying, condensing many production steps into one continuous process, while improving bioavailability of the drug itself.  

Biological medical products are gaining share in the pharmaceutical industry with 50% of newly approved drugs in 2022 being biologics. The development of new cell lines is performed at lab scale with later expansion of production to the pilot scale and then to the final manufacturing scale. To avoid cost- and time-demanding experiments to estimate the required operating parameters for different scales, the application for accelerated tech transfer and scale-up for industrial Single-use bioreactors (200-2000L) used at Takeda Pharmaceutical Company production and R&D sites is being developed. The operation of the bioreactors is modeled using Computational Fluid Dynamics to obtain data for critical process parameters inside the bioreactors with focus on oxygen transfer rate. The established CFD model is used for parametric study, employing the Design of Experiment approach to reduce the computation time. A mathematical model for the relationship between critical process parameters and input parameters, i.e., working volume, agitation rate and aeration, will be created. Finally, the mathematical model will be implemented in the graphical user interface to offer the production and R&D sites easy-to-use application to accelerate the development of new biological medical products.

Exploring bimetallic patchy nanoparticles, this work aims to transform dendritic gold-only patches into more stable and dense structures by the incorporation of silver. The change in patch morphology induces plasmonic resonance, a phenomenon where the nanoparticles resonate with incident light. This enhances light absorption in the visible or near-infrared range which is crucial for optical sensing. The main focus of the work was on the reaction kinetics as a fundamental element in understanding the mechanism of patch formation. It was discovered that a slower kinetics rate favors the formation of dense patches. Consequently, the challenge became to establish a reproducible method to intentionally slow down the kinetics, enabling precise control over the patch morphology, which was analyzed using UV-VIS-NIR spectroscopy and SEM. Thanks to their multifunctional nature, patchy particles offer a world of (almost) endless possibilities - their patches can be functionalized to serve as optical sensors for chemical sensing, environment monitoring, and in the medical field, even for disease detection and diagnostics or cancer treatment through localized hyperthermia therapy. Furthermore, their applications extend to vibrant pigments or catalysts, optimizing surface area to volume ratio.