Advanced schemes for surface plasmon resonance and plasmon-enhanced fluorescence biosensors

Advanced optical biosensor platforms exploiting long range surface plasmons (LRSPs) and responsive N-isopropylacrylamide (NIPAAm) hydrogel binding matrix for the detection of protein and bacterial pathogen analytes were carried out. LRSPs are optical waves that originate from coupling of surface plasmons on the opposite sites of a thin metallic film embedded between two dielectrics with similar refractive indices. LRSPs exhibit orders of magnitude lower damping and more extended profile of field compared to regular surface plasmons (SPs). Their excitation is accompanied with narrow resonance and provides stronger enhancement of electromagnetic field intensity that can advance the sensitivity of surface plasmon resonance (SPR) and surface plasmon-enhanced fluorescence spectroscopy (SPFS) biosensors. Firstly, we investigated thin gold layers deposited on fluoropolymer surface for the excitation of LRSPs. The study indicates that the morphological, optical and electrical properties of gold film can be changed by the surface energy of fluoropolymer and affect the performance of a SPFS biosensor. A photo-crosslinkable NIPAAm hydrogel was grafted to the sensor surface in order to serve as a binding matrix. It was modified with bio-recognition elements (BREs) via amine coupling chemistry and offered the advantage of large binding capacity, stimuli responsive properties and good biocompatibility. Through experimental observations supported by numerical simulations describing diffusion mass transfer and affinity binding of target molecules in the hydrogel, the hydrogel binding matrix thickness, concentration of BREs and the profile of the probing evanescent field was optimized. Hydrogel with a up to micrometer thickness was shown to support additional hydrogel optical waveguide (HOW) mode which was employed for probing affinity binding events in the gel by means of refractometric and fluorescence measurements. These schemes allow to reach limits of detection (LODs) at picomolar and femtomolar levels, respectively. Besides hydrogel based experiments for detection of molecular analytes, long range surface plasmon-enhanced fluorescence spectroscopy (LRSP-FS) was employed for detection of bacterial pathogens. The influence of capture efficiency of bacteria on surfaces and the profile of the probing field on sensor response were investigated. The potential of LRSP-FS with extended evanescent field is demonstrated for detection of pathogenic E. coli O157:H7 on sandwich immunoassays . LOD as low as 6 cfu mL-1 with a detection time of 40 minutes was achieved.rn

Efficient certification of feasibility and objective value of linear programs and its applications

The use of linear programming in various areas has increased with the significant improvement of specialized solvers. Linear programs are used as such to model practical problems, or as subroutines in algorithms such as formal proofs or branch-and-cut frameworks. In many situations a certified answer is needed, for example the guarantee that the linear program is feasible or infeasible, or a provably safe bound on its objective value. Most of the available solvers work with floating-point arithmetic and are thus subject to its shortcomings such as rounding errors or underflow, therefore they can deliver incorrect answers. While adequate for some applications, this is unacceptable for critical applications like flight controlling or nuclear plant management due to the potential catastrophic consequences. We propose a method that gives a certified answer whether a linear program is feasible or infeasible, or returns unknown'. The advantage of our method is that it is reasonably fast and rarely answers unknown'. It works by computing a safe solution that is in some way the best possible in the relative interior of the feasible set. To certify the relative interior, we employ exact arithmetic, whose use is nevertheless limited in general to critical places, allowing us to rnremain computationally efficient. Moreover, when certain conditions are fulfilled, our method is able to deliver a provable bound on the objective value of the linear program. We test our algorithm on typical benchmark sets and obtain higher rates of success compared to previous approaches for this problem, while keeping the running times acceptably small. The computed objective value bounds are in most of the cases very close to the known exact objective values. We prove the usability of the method we developed by additionally employing a variant of it in a different scenario, namely to improve the results of a Satisfiability Modulo Theories solver. Our method is used as a black box in the nodes of a branch-and-bound tree to implement conflict learning based on the certificate of infeasibility for linear programs consisting of subsets of linear constraints. The generated conflict clauses are in general small and give good rnprospects for reducing the search space. Compared to other methods we obtain significant improvements in the running time, especially on the large instances.