Percorrer por autor "Silva, T."
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- DEMUX devices based on a-SiC:HPublication . Fantoni, Alessandro; Louro, Paula; Vieira, Manuel Augusto; Silva, T.; Vieira, ManuelaIn this paper we present results about the functioning of a multilayered a-SiC:H heterostructure as a device for wavelength-division demultiplexing of optical signals. The device is composed of two stacked p-i-n photodiodes, both optimized for the selective collection of photogenerated carriers. Band gap engineering was used to adjust the photogeneration and recombination rates profiles of the intrinsic absorber regions of each photodiode to short and long wavelength absorption and carrier collection in the visible spectrum. The photocurrent signal using different input optical channels was analyzed at reverse and forward bias and under steady state illumination. This photocurrent is used as an input for a demux algorithm based on the voltage controlled sensitivity of the device. The device functioning is explained with results obtained by numerical simulation of the device, which permit an insight to the internal electric configuration of the double heterojunction.These results address the explanation of the device functioning in the frequency domain to a wavelength tunable photocapacitance due to the accumulation of space charge localized at the internal junction. The existence of a direct relation between the experimentally observed capacitive effects of the double diode and the quality of the semiconductor materials used to form the internal junction is highlighted.
- Prediction of dynamic plasmid production by recombinant escherichia coli fed-batch cultivations with a generalized regression neural networkPublication . Silva, T.; Lima, P.; Roxo-Rosa, M.; Hageman, S.; Fonseca, L. P.; Calado, CecíliaA generalized regression neural network with external feedback was used to predict plasmid production in a fed-batch cultivation of recombinant Escherichia coli. The neural network was built out of the experimental data obtained on a few cultivations, of which the general strategy was based on an initial batch phase followed by an exponential feeding phase. The different cultivation conditions used resulted in significant differences in bacterial growth and plasmid production. The obtained model allows estimation of the experimental outputs (biomass, glucose, acetate and plasmid) based on the bioreactor starting conditions and the following on-line inputs: feeding rate, dissolved oxygen concentration and bioreactor stirring speed. Therefore, the proposed methodology presents a quick, simple and reliable way to perform on-line feedback prediction of the dynamic behaviour of the complex plasmid production process, based on simple on-line input data obtained directly from the bioreactor control unit and with few cultivation experiments for neural network learning.