AdvisorsSalama, Khaled N.
Embargo End Date2016-04-30
Permanent link to this recordhttp://hdl.handle.net/10754/550470
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Access RestrictionsAt the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2016-04-30.
AbstractThe semiconductor industry was benefited by the advances in technology in the last decades. This fact has an impact on the sensors field, where the simple transducer was evolved into smart miniaturized multi-functional microsystems. However, commercially available gas and biological sensors are mostly bulky, expensive, and power-hungry, which act as obstacles to mass use. The aim of this work is gas and biological sensing using capacitive structures. Capacitive sensors were selected due to its design simplicity, low fabrication cost, and no DC power consumption. In the first part, the dominant structure among interdigitated electrodes (IDEs), fractal curves (Peano and Hilbert) and Archimedean spiral was investigated from capacitance density perspective. The investigation consists of geometrical formula calculations, COMSOL Multiphysics simulations and cleanroom fabrication of the capacitors on a silicon substrate. Moreover, low-cost fabrication on flexible plastic PET substrate was conducted outside cleanroom with rapid prototyping using a maskless laser etching. The second part contains the humidity, Volatile Organic compounds (VOCs) and Ammonia sensing of polymers, Polyimide and Nafion, and metal-organic framework (MOF), Cu(bdc)2.xH2O using IDEs and tested in an automated gas setup for experiment control and data extraction. The last part includes the biological sensing of C - reactive protein (CRP) quantification, which is considered as a biomarker of being prone to cardiac diseases and Bovine serum albumin (BSA) protein quantification, which is used as a reference for quantifying unknown proteins.
CitationSapsanis, C. (2015). Capacitive Structures for Gas and Biological Sensing. KAUST Research Repository. https://doi.org/10.25781/KAUST-C66Q6