Canan Özgen

MEMS biosensors for detection of methicillin resistant Staphylococcus aureus

This review presents the current state of the conventional methods, microfluidic based biosensors, and the commercial products used in the detection of methicillin resistant Staphylococcus aureus (MRSA), which is one of the most important threats of nosocomial infections in many parts of the world. The early detection of MRSA in the specimens of the patients is important to enable the appropriate treatment, to decrease morbidity and mortality rates, and to manage control actions in the healthcare units. Thus, rapid and inexpensive diagnostic systems with high sensitivity and specificity are essential to prevent MRSA to be an emerging public health threat. The design and fabrication of new diagnostic systems necessitates working in collaboration between different disciplines to make new challenges in the field of clinical diagnosis and to meet the demands of clinicians. It is certain that in the near future, MEMS and nanotechnology based detection methods will take the place of current methods in clinical diagnosis. The evaluation of new trends for specificity, sensitivity, cost effectiveness, disposability, low weight, ease of use, and facile access should be taken into consideration.

State estimation and inferential control for a reactive batch distillation column

An optimal reflux ratio profile is obtained for a reactive batch distillation system utilizing the capacity factor as the objective function in a nonlinear optimization problem. Then, an Artificial Neural Network (ANN) estimator system, which utilizes the use of several ANN estimators, is designed to predict the product composition values of the distillation column from temperature measurements inferentially. The network used is an Elman network with two hidden layers. The designed estimator system is used in the feedback inferential control algorithm, where the estimated compositions and the reflux ratio information are given as inputs to the controller to see the performance of the ANN. In the control law, a scheduling policy is used and the optimal reflux ratio profile is considered as pre-defined set-points. It is found that, it is possible to control the compositions in this dynamically complex system by using the designed ANN estimator system with error refinement whenever necessary.

Solvent Compatibility of Parylene C Film Layer

Parylene C has been preferred in various microfluidic and packaging applications as a chemical barrier; therefore, its durability in chemicals is critical to maintain functionality of the devices. In this paper, we investigated solvent compatibility of Parylene C in a range of solvents with regard to swelling of it and the change in its surface roughness at room temperature. The results of Parylene C swelling were associated with solubility parameter, δ (cal/cm3)1/2, which is predicted from the parameters of dispersion, polar, and hydrogen-bonding forces. Solvents that swelled Parylene C film layer mostly were benzene, chloroform, trichloroethylene, and toluene, while methanol, 2-propanol, ethylene glycol, and water did not cause any swelling. Subsequently, the adverse effects of diffusion of solvents through a Parylene C film layer were demonstrated by stripping of the encapsulated photoresist. In addition, a comparison was made between Parylene C and poly(dimethyl)siloxane (PDMS) considering the data of swelling ratios obtained from the experimental findings and the literature, respectively. Experimental findings showed that Parylene C is much more compatible to solvents than PDMS in high-throughput microfluidic and packaging applications. These results will be of great value to scientists for understanding compatibility of any selected solvent on Parylene C in the applications of micro devices.

A Fully Microfabricated Electrochemical Sensor and its Implementation for Detection of Methicillin Resistance in Staphylococcus Aureus

On-chip detection of biological analytes can enable diagnosis at the point of care. Combining the advantages of microelectromechanical system (MEMS) technology and molecular methods, we present the design of an integrated microfluidic platform, a microelectrochemical sensor (μECS), and its implementation for the detection of methicillin resistance in Staphylococcus aureus. This platform is capable of electrochemically sensing the target analyte in a microfluidic reactor without the usage of bulky electrodes, rendering it useful for in vitro diagnostics. In our experiments, the functionality of the sensor was tested for detecting specific DNA sequences of mecA gene (an indicator of methicillin resistance) over a range of concentrations of DNA (down to 10 pM). Synthetic oligonucleotides and bacterial PCR product were used as a target analyte in Hoechst 33258 marker-based detection and horseradish peroxidase-based detection, respectively. The results revealed that this platform has high sensitivity and selectivity. Also, its compatibility to MEMS processes enables its use with different applications ranging from detecting various types of cancers to endemics. The designed μECS can enable the detection of biological analytes of interest at low cost and high throughput.

Modeling of Gate Control Neuronal Circuitry Including Morphologies and Physiologies of Component Neurons and Fibres

Abstract In this work mathematical model of gate control theory, which explains the modulation of pain signals with tactile signals, is done. The difference of the current developed model from the previous modeling trials is that electrophysiological and morphological properties of component neurons and fibers that constitute the gate control structure are included to observe the structure-function relationship. Model of a single excitable cell is used as the main building block of the models of one unmyelinated fiber, one myelinated fiber, one interneuron and one projection neuron. The conduction velocities in the unmyelinated and myelinated fibers are found as 0.43m/s and 64.35 m/s, respectively. For both fibers input current intensity-frequency relationships are constructed. In addition, synapses between neurons are developed as two independent tanks and developed synapse model exhibits the summation and tetanization properties of real synapses while simulating the time dependency of neurotransmitter concentration in the synaptic cleft. All of the individual parts of the gate control system are connected and the whole system is simulated for different connection configurations.

State Estimation for a Reactive Batch Distillation Column

Abstract An Artificial Neural Network (ANN) estimator is designed to predict the composition values of a reactive batch distillation system inferentially. The estimator for the reactive batch distillation system, which is recently a preferred industrial operation for specialty chemicals production, is designed using temperature measurements throughout the column. The reflux ratio of the batch distillation column is also used as input to the ANN as well as temperature values. The ANN used is an Elman network with two hidden layers; having 20 neurons in the first hidden layer, three neurons in the second hidden layer, and four neurons in the output layer. The performance of the designed network is tested in open-loop and it is found that, it is possible to predict the product compositions by using the designed ANN estimator which can be used in the control of the product compositions.

Design of State Estimators for the Inferential Control of an Industrial Distillation Column

In the control of distillation columns, on-line composition measurements offer challenges. In this study, in order to predict the product compositions in an industrial multi-component distillation column from available on-line temperature measurements, two state estimators, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS), are developed and tested by using an unsteady-state column simulator. A model predictive controller (MPC) is used with the developed estimators individually for the dual composition control of the column. The performances of the developed inferential control system utilizing the estimators are found to be satisfactory considering both set-point tracking and disturbance rejection cases.

Hydrodynamics of condensate films on fluted tube surfaces. Part I

Abstract Hydrodynamic characteristics of liquid films in downward motion on the outer surface of fluted tubes have been investigated, both experimentally and theoretically. The solution of the Navier-Stokes equation is aimed at expressing velocity distribution, volumetric flow-rate, friction and cross-sectional area as a function of flute geometry and operating conditions. A general practical relationship has also been derived for the liquid carrying capacity of different flutes. The results provide sufficient information pertaining to the thickness of the condensate film and its variation, thus enabling the prediction of heat transfer coefficients in desalination.

SOME THEORETICAL INSIGHTS INTO RECENT CONTROL DESIGN ALGORITHMS

Abstract Internal Model Control (IMC) design procedure is known to offer valuable advantages over classical control methods, especially for robust controller design. The Perfect Controller is defined using the well-known classical PID algorithm and its equivalence to IMC. After the definition of the Perfect Controller the IMC filter tuning parameter, which is used to maintain robustness, is obtained as a result of reparametrization of the classical PID control algorithm and factorization of the model. Through factorization, invertible part of the model is used directly in defining the perfect controller and the noninvertibility effects of the model are lumped into the new IMC tuning parameter. Closed loop IMC controller design is obtained with appropriate plant parameter uncertainty representation. Theoretical foundation for the transition from classical PID design to IMC design is analyzed using simple common chemical process models.

Designing heat-exchanger networks for energy savings in chemical plants

The pinch-design method is applied to the evaluation of heat-exchanger networks for an existing monomer plant. The problem resulting from partially condensed multicomponent vapor streams possessing variable CP (mCp) values is solved by applying a novel technique. The rigorous design obtained is further revised by the consideration of plant constraints.