Characterization Of The Electrodes Of DEP-Based Micro-Separator

In recent years, advances in lab-on-a-chip (LOC) devices has led to separation, sorting and manipulation of cells and particles on miniaturized devices. Among the different mechanisms that have been used in this regard, dielectrophoresis (DEP) offers high controllability on the particles, provides high throughput, and is tunable. Due to these advantages, DEP is used in this paper for the design of a micro-separator. To optimize the geometry of such a separator, COMSOL Multiphysics® is used to simulate the electric field with the goal of achieving the highest performance in cell separation. For a DEP-based micro-separator, two inclined rectangle planar electrodes are considered. The effect of the width of each one of these electrodes as well as the gap between them on the DEP force is investigated to find the optimum design

Integrated Decision Support System for Prognostic and Diagnostic Analyses of Water Distribution System Failures

This paper presents an innovative decision support system (DSS) for prognostic and diagnostic analyses of water distribution system (WDS) failures. The framework of the DSS is based on four novel models developed and published by the authors of this paper. The four models include reliability assessment model, leakage potential model, leakage detection model, and water quality failure potential model. Information obtained from these models together with external information such as customer complaints, lab test results (if any), and historical information are integrated using Dempster-Shafer (D-S) theory to evaluate prognostic and diagnostic capabilities of the DSS. The prognostic capabilities of the DSS provide hydraulic and water quality states of a WDS whereas the diagnostic capabilities of the DSS help to identify the failure location with minimal time after the occurrence and will help to reduce false positive and false negative predictions. The framework has ‘unique’ capacity to bring the modeling information (hydraulic and Quality), consumer complaints, historical failure data, and laboratory test information under a single platform to perform a prognostic and diagnostic investigation of WDS failures (hydraulic and Quality). The proof of concept of the DSS has been demonstrated using data used in published four articles. The outcomes of this research widely addressed the uncertainties associated with WDS which improves the efficiency and effectiveness of diagnosis and prognosis analyses of WDS. It is expected that the developed integrated framework will help municipalities to make informed decisions to increase the safety, reliability and the security of public health.Natural Sciences and Engineering Research Council of Canada (NSERC-SPG (Strategic Project Grants)

A Comparison Between Cyclone Separator Efficiency Enhancement Using Ferrous Powder And Additional Tangential Chamber

Paper presented at 2018 Canadian Society of Mechanical Engineers International Congress, 27-30 May 2018.This paper provides a comparison between the separation efficiency in cyclones separators using two optimization techniques. separation optimizations using ferrous powder, and additional tangential chamber were investigated. The proposed techniques show separation efficiency enhancements compared to the basic cyclone separator design. The separation ratio efficiency, Ԑsp, is evaluated by calculating the outlet to inlet count ratio. Similar to experimental studies in the literature, the inlet and outlet particle counts were evaluated using discretization techniques with the help of a microscope and a membrane collecting the dust at the outlet. It is observed from the two optimization techniques that the addition of ferrous powder while attracting it through magnetic forces provides an additional separation enhancement by 2% for 11 μm particles sizes. This advantage comes with a total of 25% efficiency enhancement for 4 μm particles compared to conventional designs. This study gives an insight on the different approaches proposed

Exploiting Microfluidics for Extracellular Vesicle Isolation and Characterization: Potential Use for Standardized Embryo Quality Assessment

Recent decades have seen a growing interest in the study of extracellular vesicles (EVs), driven by their role in cellular communication, and potential as biomarkers of health and disease. Although it is known that embryos secrete EVs, studies on the importance of embryonic EVs are still very limited. This limitation is due mainly to small sample volumes, with low EV concentrations available for analysis, and to laborious, costly and time-consuming procedures for isolating and evaluating EVs. In this respect, microfluidics technologies represent a promising avenue for optimizing the isolation and characterization of embryonic EVs. Despite significant improvements in microfluidics for EV isolation and characterization, the use of EVs as markers of embryo quality has been held back by two key challenges: (1) the lack of specific biomarkers of embryo quality, and (2) the limited number of studies evaluating the content of embryonic EVs across embryos with varying developmental competence. Our core aim in this review is to identify the critical challenges of EV isolation and to provide seeds for future studies to implement the profiling of embryonic EVs as a diagnostic test for embryo selection. We first summarize the conventional methods for isolating EVs and contrast these with the most promising microfluidics methods. We then discuss current knowledge of embryonic EVs and their potential role as biomarkers of embryo quality. Finally, we identify key ways in which microfluidics technologies could allow researchers to overcome the challenges of embryonic EV isolation and be used as a fast, user-friendly tool for non-invasive embryo selection

Microfluidic integrated gas sensors for smart analyte detection: a comprehensive review

The utilization of gas sensors has the potential to enhance worker safety, mitigate environmental issues, and enable early diagnosis of chronic diseases. However, traditional sensors designed for such applications are often bulky, expensive, difficult to operate, and require large sample volumes. By employing microfluidic technology to miniaturize gas sensors, we can address these challenges and usher in a new era of gas sensors suitable for point-of-care and point-of-use applications. In this review paper, we systematically categorize microfluidic gas sensors according to their applications in safety, biomedical, and environmental contexts. Furthermore, we delve into the integration of various types of gas sensors, such as optical, chemical, and physical sensors, within microfluidic platforms, highlighting the resultant enhancements in performance within these domains

Quantitative Natural Gas Discrimination For Pipeline Leak Detection Through Time-Series Analysis of an MOS Sensor Response

Paper presented at 2018 Canadian Society of Mechanical Engineers International Congress, 27-30 May 2018.In order to detect natural gas pipeline leaks, ethane in the natural gas must be discriminated from background methane emissions. Our gas detection apparatus is well-suited for this application due to its flexibility and low cost. We present a comparison of machine learning models for quantitative estimation of concentrations of both methane and ethane in a target gas sample, using a response over time from a single sensor in our apparatus. We also demonstrate that the use of synthetic data is very effective for training a model to discriminate between methane and ethane

Smelling Through Microfluidic Olfaction Technology

Detection of volatile organic compounds (VOCs) in the exhaled breath is found to be a promising method to diagnose different diseases. The amount of alcohol or drugs absorbed/inhaled in the body can also be measured using gas sensors. Oral habits can affect the composition and also concentration of VOCs produced as a result of cellular metabolic reactions inside our body. Recognition of exhaled breath patterns including composition and concentration of VOCs provides useful information regarding how the breath affects the artificial olfaction systems. This can provide a powerful tool to calibrate gas sensors and detect VOCs associated with different diseases. In the following study, the breath signatures are extracted after different activities including fasting, brushing teeth, and drinking coffee. The results are normalized and implemented into a feature extraction model that extracts principal features for each regime. This will determine the breath signature of each regime. The results show that the effect of these activities on the breath is consistent between different subjects. This study provides the base signature of the exhaled breath which can be used in a clinical setting to identify other target VOCs that are considered the biomarkers of diseases

Development of a PC version for axisymmetric drop shape analysis (ADSA)

grantor: University of TorontoMany areas of research in surface phenomena require accurate measurements of interfacial properties. Axisymmetric drop shape analysis (ADSA) has been applied to measure contact angles and liquid-fluid interfacial tensions by fitting the Laplace equation of capillarity to the shape of the sessile and pendant drops. However, ADSA originally developed for a UNIX platform requires special training of personnel for working with the UNIX system, a fact which has limited wider use of ADSA. In addition, lacking peripheral software for the UNIX systems limits the effectiveness and usefulness of the ADSA program. This research develops a PC version of ADSA, a user-friendly program employing standard software controls. Additional features include: Microsoft Foundation Class (MFC), Object Linking and Embedding (OLE), and Multi-Document Interface (MDI), which are implemented using Visual C++, a highly enhanced programming environment. Finally, several experiments were conducted to verify the performance and accuracy of the ADSA PC-version.M.A.Sc

Different Methods for Determining Porosity of Gas Diffusion Layer using X-ray Microtomography

Gas diffusion layer (GDL) is a crucial component in polymer electrode membrane fuel cells. Being highly porous, this layer facilitates transport of species from the flow field to the reaction sites and vice versa. One of the main characteristics of GDLs is porosity, which has been measured using a number of different methods including 3D X-ray microtomography (μ XCT). Despite the extensive use of this technique in investigating the properties of GDLs, there are variations in the results since the surface of the three dimensional volume used to obtain the bulk porosity of GDLs is difficult to quantify. In this paper, a robust surface identification method, referred to as "Rolling Ball", is introduced to identify systematically the surface and hence porosity of GDLs from μ XCT datasets. In this method, the diameter of the GDL carbon fiber is used as the characteristic length in combination with a Distance Transform (DT) to robustly identify the surface topology. This method is then used to estimate porosity of four different samples of a highly porous GDL, SGL 25BA. The results between different samples show great consistency. A comparison with other methods is also performed, and variations in the bulk and in-plane porosity are observed. The main advantage of the proposed Rolling Ball method compared to prior methods used in the literature is that it uses the carbon fiber diameter to identify the surface results in a systematic fashion. This methodology can be easily applied to other highly porous media.Applied Science, Faculty ofEngineering, School of (Okanagan)ReviewedFacultyGraduat