Srinivas Palanki

Heterogeneous immunoassays using magnetic beads on a digital microfluidic platform

A digital microfluidic platform for performing heterogeneous sandwich immunoassays based on efficient handling of magnetic beads is presented in this paper. This approach is based on manipulation of discrete droplets of samples and reagents using electrowetting without the need for channels where the droplets are free to move laterally. Droplet-based manipulation of magnetic beads therefore does not suffer from clogging of channels. Immunoassays on a digital microfluidic platform require the following basic operations: bead attraction, bead washing, bead retention, and bead resuspension. Several parameters such as magnetic field strength, pull force, position, and buffer composition were studied for effective bead operations. Dilution-based washing of magnetic beads was demonstrated by immobilizing the magnetic beads using a permanent magnet and splitting the excess supernatant using electrowetting. Almost 100% bead retention was achieved after 7776-fold dilution-based washing of the supernatant. Efficient resuspension of magnetic beads was achieved by transporting a droplet with magnetic beads across five electrodes on the platform and exploiting the flow patterns within the droplet to resuspend the beads. All the magnetic-bead droplet operations were integrated together to generate standard curves for sandwich heterogeneous immunoassays on human insulin and interleukin-6 (IL-6) with a total time to result of 7 min for each assay.

Dynamic optimization of batch processes: II. Role of measurements in handling uncertainty

Abstract The main bottleneck in using optimization at the industrial level is the presence of uncertainty in the form of model mismatch and disturbances. The way uncertainty can be handled constitutes the subject of this series of two papers. The first part dealt with the characterization of the nominal solution and proposed an approach to separate the constraint-seeking from the sensitivity-seeking components of the inputs. This second part reviews various strategies for optimization under uncertainty, namely the robust and measurement-based optimization schemes. A novel scheme is proposed, where optimality is achieved by tracking the necessary conditions of optimality. The different approaches are compared via the simulation of a bioreactor for penicillin production.

Synthesis of state feedback laws for end-point optimization in batch processes

nature of the feedback law (static or dynamic) are completely characterized in terms of the Lie bracket structure of the system dynamics. Explicit synthesis formulae for the state feedback laws are first obtained for time-invariant systems and then extended to time-varying systems. As illustrative examples of application of the proposed methodology, we consider several end-point optimization problems in batch chemical and biochemical reactors. INTRCJDUCTJON Batch and semi-batch processes are of great importance to the chemical industry. A wide variety of specialty chemicals such as antibiotics and polymers are produced in batch reactors; they are preferred due to their ease and flexibility of operation. Batch reactors are used when there are many processing steps in the chemical process, when isolation is required for reasons of sterility or safety and when the materials involved are hard to handle.

A feedback-based implementation scheme for batch process optimization

The terminal-cost optimization of a control–affine nonlinear system leads to a discontinuous solution that can be characterized in a piecewise manner. To implement such an optimal trajectory despite disturbances and parametric uncertainty, a cascade optimization scheme is proposed in this paper, where optimal reference signals are tracked. Optimality is achieved by the appropriate definition of reference signals (input bounds, state constraints, or switching functions) to track in various sub-intervals. Furthermore, conservatism is introduced into the optimization problem to ensure satisfaction of path constraints in the presence of uncertainty. Finally, the proposed cascade optimization scheme is illustrated on a simulation of a fed-batch penicillin fermentation plant.

A Lyapunov approach for robust nonlinear state feedback synthesis

The authors apply the input/output linearization approach for nonlinear state feedback synthesis. The model uncertainty under consideration is a class of state model perturbations that satisfy appropriate matching conditions. The controller design uses a Lyapunov-based approach to guarantee uniform ultimate boundedness of the states and the output. >

Controller synthesis for time-varying systems by input/output linearization

Recently, there have been major developments in the nonlinear systems theory for the regulation of nonlinear time-invariant systems. In this paper this methodology is extended to nonlinear time-varying systems. First, the relevance of modeling systems in chemical engineering with time-varying parameters is shown. Then, modified Lie derivatives are defined to account for the explicit time dependence of the system model on time. A time-dependent invertible coordinate transformation is derived to quantify the zero dynamics of the system. State feedback laws are synthesized, which provide a linear time-invariant input-output response. It is observed that the internal stability of this state feedback is not analogous to the results of the time-invariant case. A dynamic output feedback controller for the time-varying system is synthesized. This methodology is used to derive feedback laws for the regulation of linear time-varying systems. Finally, the proposed methodology is illustrated by a simulation example.

Silver nanoparticles protect human keratinocytes against UVB radiation-induced DNA damage and apoptosis: potential for prevention of skin carcinogenesis.

UNLABELLED Ultraviolet (UV)-B radiation from the sun is an established etiological cause of skin cancer, which afflicts more than a million lives each year in the United States alone. Here, we tested the chemopreventive efficacy of silver-nanoparticles (AgNPs) against UVB-irradiation-induced DNA damage and apoptosis in human immortalized keratinocytes (HaCaT). AgNPs were synthesized by reduction-chemistry and characterized for their physicochemical properties. AgNPs were well tolerated by HaCaT cells and their pretreatment protected them from UVB-irradiation-induced apoptosis along with significant reduction in cyclobutane-pyrimidine-dimer formation. Moreover, AgNPs pre-treatment led to G1-phase cell-cycle arrest in UVB-irradiated HaCaT cells. AgNPs were efficiently internalized in UVB-irradiated cells and localized into cytoplasmic and nuclear compartments. Furthermore, we observed an altered expression of various genes involved in cell-cycle, apoptosis and nucleotide-excision repair in HaCaT cells treated with AgNPs prior to UVB-irradiation. Together, these findings provide support for potential utility of AgNPs as novel chemopreventive agents against UVB-irradiation-induced skin carcinogenesis. FROM THE CLINICAL EDITOR Excessive exposure to the sun is known to increase the risk of skin cancer due to DNA damage. In this work, the authors tested the use of silver nanoparticles as protective agents against ultraviolet radiation. The positive results may open a door for the use of silver nanoparticle as novel agents in the future.

A neural network strategy for batch process optimization

Abstract The traditional way of operating a batch reactor is to use a pre-determined input trajectory. However, due to batch-to-batch variation in process conditions, this can lead to sub-optimal operation. In this paper, a novel neural network strategy is developed for calculating the optimal operating trajectory based on initial loading conditions and process parameters. The efficacy of this methodology is demonstrated via application to two industrially relevant batch polymerization processes — (1) batch styrene polymerization and (2) batch methyl methacrylate polymerization.

Nonlinear control of nonsquare multivariable systems

This paper is concerned with the synthesis of a nonlinear state feedback law for nonsquare multivariable nonlinear systems. Previous approaches in the literature have solved this problem by (1) squaring the system by discarding some inputs or by adding new outputs, or (2) by utilizing some inputs for input/output (I/O) linearization and the remaining inputs for minimizing cost. In this paper, a nonlinear feedback law is synthesized which utilizes all the available inputs to I/O linearize the system and minimize the cost of the control effort by solving a convex optimization problem on-line. This procedure is illustrated via simulation of a regulation problem in a nonlinear continuous stirred tank reactor with three inputs and two outputs.

Optimal feedback control of batch reactors with a state inequality constraint and free terminal time

Abstract In this paper we derive optimal state feedback laws for end-point optimization of a dynamic system where the final time is free and the system has a scalar inequality constraint. The existence of a singular region as well as the nature of the state feedback law (static or dynamic) is completely characterized in terms of the system dynamics. Explicit synthesis formulae for the state feedback laws are presented. Once the state feedback laws for end-point optimization have been derived, issues on how these laws can be implemented as part of a closed-loop scheme are discussed. As illustrative examples of application of the proposed methodology, several end-point optimization problems in batch chemical reactors are considered.