16,867 research outputs found

    On A Simpler and Faster Derivation of Single Use Reliability Mean and Variance for Model-Based Statistical Testing

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    Markov chain usage-based statistical testing has proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-toend reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper continues our earlier work on a simpler and faster derivation of the single use reliability mean, and proposes a new derivation of the single use reliability variance by applying a well-known theorem and eliminating the need to compute the second moments of arc failure probabilities. Our new results complete a new analysis that could be shown to be simpler, faster, and more direct while also rendering a more intuitive explanation. Our new theory is illustrated with three simple Markov chain usage models with manual derivations and experimental results

    Field Effect Control of Electrokinetic Transport Phenomena in Nanofluidics

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    Naturally nanofluidics has applications demanding the samples to be handled in exceedingly small quantities due to the small size of the fluidic channels in nanofluidic devices, such as characterization of single biomolecules. Fluids confined in channels of nanometer characteristic dimensions exhibit physical behaviors not observed in large conduits. Charge properties of the nanochannel wall in contact with an aqueous solution play essential roles in the involved electrokinetic transport phenomena occurring in nanofluidic devices. In addition to tuning the charge properties of the nanofluidic channel wall by adjusting the solution properties such as pH and background salt concentration, field effect transistor (FET) with a gate electrode embedded beneath the nanochannel wall has been demonstrated to rapidly tune the surface charge condition and accordingly the electrokinetic transport phenomena in nanofluidic devices. The first part of the dissertation develops a mathematical model for the charge properties of the nanofluidic channel and the electroosmotic flow (EOF) in a nanoslit gated by a FET. In contrast to the previous studies, surface chemistry is considered for the first time. The obtained results demonstrated that the surface charge property as well as the direction and magnitude of the EOF can be actively tuned by the FET. The performance of FET control is more sensitive when the pH and/or the bulk electrolyte concentration is relatively low. Since the nanofluidics-based biosensing is based on discriminating the ionic current or conductance signal, active control of the surface charge property and accordingly the ionic current/conductance in nanofluidics is investigated in the second part of the dissertation. An analytical model for the surface charge property and the ionic conductance in a FET-gated silica nanochannel is developed considering practical effects of surface chemistry reactions, multiple ionic species, the Stern layer, and the EOF. The results show that the performance of the FET control on ionic conductance is more significant when the background salt concentration and pH are low. Experimental studies demonstrated that the streaming current in the nanochannel provides a simple and effective scenario for converting hydrodynamics to electrical power. The third part of the dissertation investigated streaming current in a pH-regulated nanochannel gated by FET. Analytical models for the streaming current/conductance with and without considering the electroviscous effects have been derived. The models take into account multiple ionic species, surface chemistry reactions, and the Stern layer effect. Results show the performance of the field effect modulation of the streaming conductance is significant for lower solution pH and salt concentration. The last part of the dissertation extends the previous studies by considering the overlapping of the EDLs inside the nanochannel. The model takes into account the surface chemistry, Stern layer, multiple ionic species and the EDL overlapping effect. The model is validated by the existing experimental data of the ionic conductance in the silica nanochannel with significant EDL overlapping effect. Results from the model with and without considering EDL overlapping are compared
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