Biosensors: Recent advances and mathematical challenges
March, 2014 –
Language: English
– Open Access
DOI: 10.3926/oms.98
/ ISBN: 978-84-941872-0-9
Categories:
Biology and Life Sciences
Many researchers and research groups around the globe are dealing with interdisciplinary problems and multidisciplinary groups; but especially those that deal everyday with biosensors have the feeling of not having enough personnel with the sufficient background to cover all topics. With this feeling in our minds, we decided to invite wonderful researchers from different parts to design this book, not as a conventional biosensor’s book, but as an interesting journey in the complex world of biosensors. A close look to some recent and key advances on the topic, but at the same time tips through novel mathematical modelling to improve our work, and last but not least, tricks to fool those undesired electrical disturbances that commonly messes our daily work can be found in these pages.
Feel free to navigate the book through its chapters, once in your hands you will feel eager to learn more about biosensors. It has been an experience to put all this together; but now, with the book ready, we can say that this trip has being a wonderful adventure. That is why want to thank all the authors for their great contributions.
Chapters
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Prologue and Introduction
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Use of functional metalic nanostructures in biosensors
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Bacterial membrane formation monitored with atomic force microscopy and quartz crystal microbalance
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Whole cell biosensors
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Electrochemical biosensors for the detection of microcystinsRecent advances and perspectives
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Phenolic compounds determination in water by enzymatic-based electrochemical biosensors
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Electrochemical detection of dopamine using graphite electrodes modified with PAMAM G4.0-64OH dendrimers in synthetic cerebrospinal fluids
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Continuous monitoring based on biosensors coupled with artificial intelligence
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Modeling a second-generation glucose oxidase biosensor with statistical machine learning methods
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Modeling the influence of pH and temperature on the response of an acetylcholinesterase biosensor using machine learning methods
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Digital analysis and treatment for electromagnetic disturbances in biomedical instrumentation
