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Author

Mina Hoorfar

Bio: Mina Hoorfar is an academic researcher from University of British Columbia. The author has contributed to research in topics: Proton exchange membrane fuel cell & Materials science. The author has an hindex of 31, co-authored 185 publications receiving 3669 citations. Previous affiliations of Mina Hoorfar include University of Toronto & University of Victoria.


Papers
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Journal ArticleDOI
TL;DR: The most recent advances in the DMF platforms are discussed, and the feasibility of developing multifunctional packages for performing complete sets of processes of biochemical assays, particularly for point-of-care applications is evaluated.
Abstract: Following the development of microfluidic systems, there has been a high tendency towards developing lab-on-a-chip devices for biochemical applications. A great deal of effort has been devoted to improve and advance these devices with the goal of performing complete sets of biochemical assays on the device and possibly developing portable platforms for point of care applications. Among the different microfluidic systems used for such a purpose, digital microfluidics (DMF) shows high flexibility and capability of performing multiplex and parallel biochemical operations, and hence, has been considered as a suitable candidate for lab-on-a-chip applications. In this review, we discuss the most recent advances in the DMF platforms, and evaluate the feasibility of developing multifunctional packages for performing complete sets of processes of biochemical assays, particularly for point-of-care applications. The progress in the development of DMF systems is reviewed from eight different aspects, including device fabrication, basic fluidic operations, automation, manipulation of biological samples, advanced operations, detection, biological applications, and finally, packaging and portability of the DMF devices. Success in developing the lab-on-a-chip DMF devices will be concluded based on the advances achieved in each of these aspects.

337 citations

Journal ArticleDOI
01 Dec 2015-Sensors
TL;DR: The integration of microfluidic and biosensor technologies provides the ability to merge chemical and biological components into a single platform and offers new opportunities for future biosensing applications including portability, disposability, real-time detection, unprecedented accuracies, and simultaneous analysis of different analytes in a single device.
Abstract: A biosensor can be defined as a compact analytical device or unit incorporating a biological or biologically derived sensitive recognition element immobilized on a physicochemical transducer to measure one or more analytes. Microfluidic systems, on the other hand, provide throughput processing, enhance transport for controlling the flow conditions, increase the mixing rate of different reagents, reduce sample and reagents volume (down to nanoliter), increase sensitivity of detection, and utilize the same platform for both sample preparation and detection. In view of these advantages, the integration of microfluidic and biosensor technologies provides the ability to merge chemical and biological components into a single platform and offers new opportunities for future biosensing applications including portability, disposability, real-time detection, unprecedented accuracies, and simultaneous analysis of different analytes in a single device. This review aims at representing advances and achievements in the field of microfluidic-based biosensing. The review also presents examples extracted from the literature to demonstrate the advantages of merging microfluidic and biosensing technologies and illustrate the versatility that such integration promises in the future biosensing for emerging areas of biological engineering, biomedical studies, point-of-care diagnostics, environmental monitoring, and precision agriculture.

319 citations

Journal ArticleDOI
27 Mar 2019
TL;DR: The current knowledge in exosome biogenesis, their roles in disease progression, and therapeutic applications and opportunities in bioengineering are summarized and the established and emerging technological developments inExosome isolation and characterization are highlighted.
Abstract: Exosomes are small (∼30–140 nm) lipid bilayer-enclosed particles of endosomal origin. They are a subset of extracellular vesicles (EVs) that are secreted by most cell types. There has been growing interest in exosome research in the last decade due to their emerging role as intercellular messengers and their potential in disease diagnosis. Indeed, exosomes contain proteins, lipids, and RNAs that are specific to their cell origin and could deliver cargo to both nearby and distant cells. As a result, investigation of exosome cargo contents could offer opportunities for disease detection and treatment. Moreover, exosomes have been explored as natural drug delivery vehicles since they can travel safely in extracellular fluids and deliver cargo to destined cells with high specificity and efficiency. Despite significant efforts made in this relatively new field of research, progress has been held back by challenges such as inefficient separation methods, difficulties in characterization, and lack of specific biomarkers. In this review, we summarize the current knowledge in exosome biogenesis, their roles in disease progression, and therapeutic applications and opportunities in bioengineering. Furthermore, we highlight the established and emerging technological developments in exosome isolation and characterization. We aim to consider critical challenges in exosome research and provide directions for future studies.

281 citations

Journal ArticleDOI
TL;DR: In this article, a review of the recent published research results in this field is reviewed and several applications of EIS in PEM fuel cell modeling and diagnosis are thoroughly reviewed, including outstanding improvements conducted in EIS measurement and hardware.

274 citations

Journal ArticleDOI
TL;DR: The improved version of ADSA not only determines the interfacial properties more accurately but, through the shape parameter, also provides an a priori knowledge of the accuracy of the results.

253 citations


Cited by
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Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Book ChapterDOI
01 Jan 2010

5,842 citations

Journal ArticleDOI
TL;DR: In this article, the authors present the latest status of PEM fuel cell technology development and applications in the transportation, stationary, and portable/micro power generation sectors through an overview of the state-of-the-art and most recent technical progress.

2,687 citations

Book
01 Jan 1971
TL;DR: In this paper, Ozaki et al. describe the dynamics of adsorption and Oxidation of organic Molecules on Illuminated Titanium Dioxide Particles Immersed in Water.
Abstract: 1: Magnetic Particles: Preparation, Properties and Applications: M. Ozaki. 2: Maghemite (gamma-Fe2O3): A Versatile Magnetic Colloidal Material C.J. Serna, M.P. Morales. 3: Dynamics of Adsorption and Oxidation of Organic Molecules on Illuminated Titanium Dioxide Particles Immersed in Water M.A. Blesa, R.J. Candal, S.A. Bilmes. 4: Colloidal Aggregation in Two-Dimensions A. Moncho-Jorda, F. Martinez-Lopez, M.A. Cabrerizo-Vilchez, R. Hidalgo Alvarez, M. Quesada-PMerez. 5: Kinetics of Particle and Protein Adsorption Z. Adamczyk.

1,870 citations