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In this work, a novel microfluidic cell culture platform capable of automated electrical impedance measurements and immunofluorescence and brightfield microscopy was developed for further in-vitro cellular research intended to optimize cell culture conditions. The microfluidic system design, fabrication, automation, and design verification testing are described. Electrical and optical measurements of the 16 parallel cell culture chambers were automated via a custom LabView interface. A proposed design change will enable gas diffusion, removing the need for an environmental enclosure and allow long-term cell culture experiments. This "lab on a chip" system miniaturizes and automates experiments improving testing throughput and accuracy while creating a highly controllable microenvironment for cell culture. Such a system can be applied to drug development, bioassays, diagnostics, and animal testing alternatives. This work is part of a collaborative effort to define protocols for the electrical and optical characterization of cell culture within a novel microfluidic device with the intent of optimizing microenvironment conditions.

This dissertation investigates potential applications for Machine Learning (ML) and real-time fouling monitors in Reverse Osmosis (RO) desalination. The main objective was to develop a framework that minimizes the cost of membrane fouling by deploying AI-generated cleaning patterns and real-time fouling monitoring. Membrane manufacturers and researchers typically recommend cleaning (standard operating procedure – SOP) when normalized permeate flow, a performance metric tracking the decline of permeate flow/output from its initial baseline with respect to operating pressure, reaches 0.85-0.90 of baseline values. This study used estimates of production cost, internal profitability metrics, and permeate volume output to evaluate and compare the impact of time selection for cleaning intervention. The cleanings initiated when the normalized permeate flow reached 0.85 represented the control for cleaning intervention times. In deciding optimal times for cleaning intervention, a Deep Reinforcement Learning (RL) agent was trained to signal cleaning between 0.85-0.90 normalized with a cost-based reward system. A laboratory-scale RO flat membrane desalination system platform was developed as a model plant, and data from the platform and used to train the model and examine both simulated and actual control of when to trigger membrane cleaning, replacing the control operator's 0.85 cleaning threshold. Compared to SOP, the intelligent operator showed consistent savings in production costs at the expense of total permeate volume output. The simulated operation using the RL initiated yielded 9% less permeate water but reduced the cost per unit volume ($/m3) by 12.3%. When the RL agent was used to initiate cleaning on the laboratory-scale RO desalination system platform, the system produced 21% less permeate water but reduced production cost ($/m3) by 16.0%. These results are consistent with an RL agent that prioritizes production cost savings over product volume output. / Doctor of Philosophy / The decreasing supply of freshwater sources has made desalination technology an attractive solution. Desalination—or the removal of salt from water—provides an opportunity to produce more freshwater by treating saline sources and recycled water. One prominent form of desalination is Reverse Osmosis (RO), an energy intensive process in which freshwater is forced from a pressurized feed through a semipermeable membrane. A significant limiting cost factor for RO desalination is the maintenance and replacement of semipermeable RO membranes. Over time, unwanted particles accumulate on the membrane surface in a process known as membrane fouling. Significant levels of fouling can drive up costs, negatively affect product quality (permeate water), and decrease the useful lifetime of the membrane. As a result, operators employ various fouling control techniques, such as membrane cleaning, to mitigate its effects on production and minimize damage to the membrane. This dissertation investigates potential applications for Machine Learning (ML) and real-time fouling monitors in Reverse Osmosis (RO) desalination. The main objective was to develop a framework that minimizes the cost of membrane fouling by deploying AI-generated cleaning patterns and real-time fouling monitoring. Membrane manufacturers and researchers typically recommend cleaning (standard operating procedure – SOP) when normalized permeate flow, a performance metric tracking the decline of permeate flow/output from its initial baseline with respect to operating pressure, reaches 0.85-0.90 of baseline values. This study used estimates of production cost, internal profitability metrics, and permeate volume output to evaluate and compare the impact of time selection for cleaning intervention. The cleanings initiated when the normalized permeate flow reached 0.85 represented the control for cleaning intervention times. In deciding optimal times for cleaning intervention, a Deep Reinforcement Learning (RL) agent was trained to signal cleaning between 0.85-0.90 normalized with a cost-based reward system. A laboratory-scale RO flat membrane desalination system platform was developed as a model plant, and data from the platform and used to train the model and examine both simulated and actual control of when to trigger membrane cleaning, replacing the control operator's 0.85 cleaning threshold. Compared to SOP, the intelligent operator showed consistent savings in production costs at the expense of total permeate volume output. The simulated operation using the RL initiated yielded 9% less permeate water but reduced the cost per unit volume ($/m3) by 12.3%. When the RL agent was used to initiate cleaning on the laboratory-scale RO desalination system platform, the system produced 21% less permeate water but reduced production cost ($/m3) by 16.0%. These results are consistent with an RL agent that prioritizes production cost savings over product volume output.

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This thesis consists of three sections, the first two deal with the development and testing of an electrical impedance tomography prototype system for imaging breast cancer. The third section sues mutlivariate statistical methods to improve EIT image quality. The McMaster EIT System Mk1.0 is the resultant system of the system development. The EIT system is a 48 electrode, single current source, serial acquisition device with an operational frequency between 100Hz to 125kHz. The device is able to inject current between any two electrodes and is able to perform single or differential measurements on any two electrode pairs. The system is equipped with a virtual phase-lock loop and is capable of paramatic imaging. The system was tested using tests common to most electrical devices and specifically designed for EIT systems, to both benchmark the system and detect any errors. The testing revealed the device while able to produce viable EIT images; system suffers from a large stray capacitance. Due to stray capacitance the system injection amplitude accuracy varies with frequency and load. The system SNR is over 100dB with a 125kHz signal with a 5mA signal and compares favourably with existing EIT systems. The CMRR of the system closely tracked the published CMRR of the underlying commercial components and is comparable to existing systems. A second source of error which needs to be rectified in future deigns is the high contact impedence; which causes high direct current offset. Multivariate testing was used to detect errors which could not be easily discovered using conventional testing. The testing, performed iteratively detected several electronic errors which were fixed during development of the device. Six related models were developed for system noise, each with a different set of underlying assumptions about the source of noise. Of the models only one model proved to be a success on both qualitative and quantitative analysis of sample data sets. Finally an alternate model to the Cole-Cole parametric imaging based on PCA was proposed. The model proved to be better at modeling the underlying tissue variations in the presence of noise than Cole-Cole based models. The prototype EIT system presented in this thesis is a viable EIT system, but is in need of improvements to shielding to improve system performance. Also in need of improvement is the operational frequency and modifications toward a distributed architecture. The multivariate methods used for modelling system noise and tissue should be combined into one method for maximum benefit. / Thesis / Master of Applied Science (MASc)

The purpose of this work is to develop and evaluate electroding methods for a reverse osmosis (RO) membrane that results in an in situ sensor able to detect RO membrane protein fouling. Four electroding techniques were explored: i) gold exchange-reduction, ii) encapsulated carbon grease, iii) "direct assembly process" (DAP), and iv) platinized polymer graft. The novel platinized polymer graft method involves chemically modifying the RO membrane surface to facilitate platinization based on the hypothesis that deposition of foulant on the platinized surface will affect platinum/foulant/solution interfacial regions, thus sensor impedance. Platinized polymer graft sensors were shown to be sensitive to protein fouling. Electrodes were characterized by their electrical properties, SEM and XPS. Assembled sensors were evaluated for sensitivity to electrolyte concentration and protein fouling. Micrographs showed coating layers and pre-soak solution influence gold exchange-reduction electrode formation. High surface resistance makes gold exchange-reduction an unsuitable method. Concentration sensitivity experiments showed carbon grease and DAP electroding methods produce unusable sensors. Carbon grease sensors have time-dependent impedance response due to electrolyte diffusion within the micro-porous polysulfone support. DAP electroded sensors proved quite fragile upon hydration; their impedance response is transient and lacks predictable trends with changes in concentration. A parametric study of the platinized polymer graft method shows amount of grafted monomer correlates to grafting time, and deposited platinum is a function of exchange-reduction repetitions and amount of grafted monomer. Platinized polymer graft sensors were fouled in both dead-end and cross-flow RO systems, and their impedance trends, while varying between sensors, indicate protein-fouling sensitivity. / Master of Science

Structural Health Monitoring (SHM) is the implementation of damage detection and characterization algorithms using in vitro sensing and actuation for rapidly determining faults in structural systems before the damage leads to catastrophic failure. SHM systems provide near real time information on the state of the integrity of civil, mechanical and aerospace structures. A roadblock in implementing SHM systems in practice is the possibility of false positives introduced by environmental changes. In particular, temperature changes can cause many SHM algorithms to indicate damage when no damage exists. While several experimentally based efforts have been attempted to alleviate temperature effects on SHM algorithms, fundamental research on the effects of temperature on SHM has not been investigated. The work presented in this dissertation composes of two main parts: the first part focuses on the experimental studies of different mechanical structures of aluminum beams, lug samples and railroad switch bolts. The experimental study of the aluminum lug samples and beams is done to propose and examine methods and models for in situ interrogation and detection of damage (in the form of a fatigue crack) in these specimen and to quantify the smallest detectable crack size in aluminum structures. This is done by applying the electrical impedance-based SHM method and using piezoceramic sensors and actuators. Moreover, in order to better extract the damage features from the measured electrical impedance, the ARX non-linear feature extraction is employed. This non-linear feature extraction, compared to the linear one, results in detection of damages in the micro-level size and improves the early detection of fatigue cracks in structures. Experimental results also show that the temperature variation is an important factor in the structural health monitoring applications and its effect on the impedance-based monitoring of the initiation and growth of fatigue cracks in the lug samples is experimentally investigated. The electrical impedance-based SHM technique is also applied in monitoring the loosening of bolted joints in a full-scale railroad switch and the sensitivity of this technique to different levels of loosening of the bolts is investigated. The second part of the work presented here focuses on the analytical study and better understanding of the effect of temperature on the vibration-based SHM. This is done by analytical modeling of the vibratory response of an Euler-Bernoulli beam with two different support conditions of simply supported and clamped-clamped and with a single, non-breathing fatigue crack at different locations along the length of the beam. The effect of temperature variations on the vibratory response of the beam structure is modeled by considering the two effects of temperature-dependent material properties and thermal stress formations inside the structure. The inclusion of thermal effects from both of these points of view (i.e. material properties variations and generation of thermal stresses) as independent factors is investigated and justified by studying the formulations of Helmholtz free energy and stresses inside a body. The effect of temperature variations on the vibratory response of the cracked beam are then studied by integrating these two temperature-related effects into the analytical modeling. The effect of a growing fatigue crack as well as temperature variations and thermal loadings is then numerically studied on the deflection of the beam and the output voltage of a surface-bonded piezoceramic sensor. / Ph. D.

The human skin is the largest and most complex body organ but accessible and attractive for biomarker sampling and transdermal drug delivery. The two procedures are significantly impacted by several biophysical properties of the skin, especially the pH and stratum corneum (SC)hydration. The varying levels of pH on the skin surface usually impact the permeability barrier function of the SC, contributing to the onset of dermatological disorders such as incontinence-associated dermatitis (IAD). Consequently, this scholarly work provides a comprehensive in vitro investigation of the effect of pH on the skin including the effect of artificial urine. The pig skin membranes were used to conduct electrical impedance spectroscopy (EIS)experiments using a four-electrode Franz cell set-up. Artificial urine and buffered solution with varying pH gradients were utilized to induce reversible changes in effective membrane capacitance (Ceff) and membrane resistance (Rmem). The in vitro investigation revealed that exposure to urine changed the electrical impedance properties of the skin. Specifically, we found that the application of artificial urine to the skin reduced skin resistance. At the same time, we also find systematic changes in skin capacitance. Skin capacitance increased with increased pH. Hence the two skin impedance parameters showed a clear effect of artificial urine on the skin. These changes, i.e., the decrease of Rmem and increase of Ceff of skin membranes when they are exposed to artificial urine, can be interpreted as skin barrier deterioration The information provided herein is relevant in describing the detrimental effect of urine on the skin, which probably makes skin barrier more permeable.

Thesis (MScPhysio)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Question: What are the effects of Intermittent Positive Pressure Breathing (with and without a Positive End Expiratory Pressure [PEEP] Valve), compared to deep breathing exercises, on ventilation distribution in high-risk adults following open upper abdominal surgery (UAS)? Design: This study comprised an observational descriptive component as well as a prospective triple blind randomised controlled crossover trial with concealed allocation and patient, assessor and statistician blinding Participants: Seven patients at high risk for postoperative pulmonary complications following UAS Intervention: Deep breathing exercises (DBExs) were compared to Intermittent Positive Pressure Breathing (IPPB), with IPPB further applied with and without a PEEP Valve, using a randomised cross-over design with 30 minute washout duration between periods. Outcome measures: Global and regional impedance changes in the lungs were measured using Electrical Impedance Tomography. Vital signs, visual analogue pain scale (VAS) and modified Borg scale (MBS) were measured pre– and post-intervention. Results: A greater mean global lung impedance change ( Z) was detected with IPPB compared to DBExs (mean difference in Z 2803.8; 95% CI 5189.9 to 8512.5 and 2046 to 96047.9; P<0.01). These changes in lung impedance lasted 30 minutes before returning to baseline. There was no difference in Z when patients received IPPB with 5cmH20 PEEP compared to IPPB with no PEEP. No specific regional ventilation changes were noted. IPPB did not increase VAS, MBS scores or adversely affect vital signs. Conclusion: IPPB is an effective technique to improve lung volumes compared to deep breathing exercises. Further studies are required to investigate the effect of IPPB on clinical outcome. / AFRIKAANSE OPSOMMING: Vraag: Watter uitwerking het onderbroke positiewe-drukasemhaling (met én sonder ’n positiewe-endekspiratoriesedruk-[PEEP-]klep) in vergelyking met diepasemhalings-oefeninge op ventilasieverspreiding by hoërisikovolwassenes ná bo-buikchirurgie? Ontwerp: ’n Waarnemingsgegronde, vergelykende en ondersoekende, driedubbelblinde, verewekansigde gekontroleerde oorskakelproef, met verborge toewysing en blinding van pasiënte, die assesseerder en statistikus Deelnemers: Sewe pasiënte met ’n hoë risiko vir post-operatiewe pulmonêre komplikasies na bo-buikchirurgie Intervensie: Diepasemhalingsoefeninge (DBEx) is vergelyk met onderbroke positiewedrukasemhaling (IPPB), wat op sy beurt met én sonder ’n PEEP-klep toegepas is, met behulp van ’n verewekansigde oorskakelstudie met ’n halfuur lange uitspoeling tussen oorskakelings. Uitkomsmetings: Algehele en regionale impedansieveranderinge in die longe is met behulp van elektriese impedansietomografie gemeet. Vitale tekens, die visuele analoogskaal (VAS) en die aangepaste Borg-skaal (MBS) is voor, sowel as na die intervensie afgeneem. Resultate: ’n Groter gemiddelde algehele impedansieverandering ( Z) is opgemerk met IPPB in vergelyking met DBEx (gemiddelde verskil 2803.8; 95% CI 5189.9: 8512.5 en 2046: 96047.9; P<0.01). Hierdie veranderinge in longimpedansie het ’n halfuur of langer geduur voordat dit na die basislyn teruggekeer het. Daar was geen verskil in Z toe pasiënte IPPB met ’n PEEP-klep van 5cmH20 ontvang het teenoor IPPB sonder ’n PEEP-klep nie. Geen spesifieke regionale voorkeure is opgemerk nie. IPPB het nie die VAS- of MBS-tellings verhoog of vitale tekens verswak nie. Stellenbosch University https://scholar.sun.ac.za iv Gevolgtrekking: In vergelyking met DBEx, is IPPB ’n doeltreffende tegniek om longvolumes te verbeter. Verdere studies word vereis om die uitwerking van IPPB op kliniese uitkomste te ondersoek.