Prof. Dr. Lital Alfonta Department of Life Sciences, Ilse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev
* Genetically Engineering of Enzymes and Electrode Modifications for Biosensing Applications
Biosensing efficiency, selectivity and sensitivity relies first and foremost on a successful interfacing between enzymes and sensing surfaces. An Interface that allows from one hand a specific analyte recognition and on the other hand an efficient signal transduction. Some of the challenges in biosensing stem from wrong orientation of the enzyme towards the sensing interface and from the need to use mediated electron transfer with a diffusional redox mediator due to a difficulty in relaying a signal from a redox center that is deeply buried inside the protein matrix. Using genetic code expansion tools, and genetic engineering approaches we were able to modify redox enzymes and surfaces for biosensing and biofuel cell applications so they could have superior properties over native enzymes. In my talk, I will demonstrate how does site specific wiring of redox enzymes which is genetically encoded, can improve electron transfer due to controlled and short electron transfer distances and due to proper enzyme orientation. I will also demonstrate how a rational genetic engineering of an enzyme gives it superior properties for biosensing purposes compared to those of the native enzyme.
*Nucleic Acid Analysis Using Multifunctional Hybridization Sensors
Hybridization of nucleic acid probes remains one of the most common strategy for sensing of specific DNA and RNA sequences. Formats that use hybridization probes include qualitative PCR, microarrays, fluorescent in situ hybridization (FISH), to name a few. Moreover, specific recognition of RNA sequences is on demand by gene silencing approaches, e.g. antisense and siRNA. Hybridization probes are nucleic acid oligomers of 15−25 nucleotides (or longer) designed to be complementary to targeted analytes. The formation of a probe-analyte hybrid testifies that the analyte contains a nucleotide sequence complementary to the probe. This approach suffers from low selectivity (especially for temperatures <40oC), high cost for fluorescent probes, and poor target accessibility if folded natural RNA are analyzed. Part of the sensing problem arises from the affinity/selectivity dilemma: the higher the probe-analyte affinity, the lower the selectivity. To address these and other problems, we design multicomponent hybridization probes (MHP) that consist of several oligonucleotide components, which associate with RNA/DNA target and produce a detectable signal. Each MHP component serves specific function, thus enabling simultaneous improvement of multiple key characteristics. My presentation will cover the design of a probe that can differentiate single nucleotide substitutions in DNA in the entire temperature interval of 5-40oC; a molecular machine that tightly binds RNA analyte while remains highly selectivity; a strategy for recognition of highly variable viral genomes with high selectivity.
*ATP Synthesis and Biosensing Coupled to the Electroenzymatic Activity of a Hydrogenase on an Electrode/Biomimetic Membrane Interface
Cells generate energy by coupling a proton gradient across a phospholipid bilayer membrane with the activity of a cross-membrane ATP synthase enzyme. In an effort to mimic this process in an artificial environment, we show that ATP can be efficiently produced starting from molecular hydrogen as a fuel.
The proton concentration in an electrode/phospholipid bilayer interface can be controlled and monitorised electrochemically by immobilizing the membrane-bound [NiFeSe]-hydrogenase from Desulfovibrio vulgaris Hildenborough. The electro-enzymatic oxidation of H2 generated a proton gradient across the supported biomimetic membrane that can be coupled to the in vitro synthesis of ATP by reconstituting ATP-synthase from E. coli on the biomimetic system.Such system is also suitable for developing an electrochemical biosensor of ATP.
Structural health monitoring of aerospace, civil and mechanical structures and components is becoming increasingly relevant to minimize maintenance costs and provide additional layers of safety for the users. The session will focus on the progress of existing or novel sensing concepts including synergies among sensing approaches. All sensing mechanisms including piezoelectrics, piezoresistive, electrostrictives, magnetostrictives, shape memory alloys or polymers and optical are included in this session. Experimental studies are preferred but computational approaches that include applications and experimental results are also welcome. Presentations on data fusion, networking and computing of large amounts of data that are geared towards structural health monitoring applications are also encouraged in this session.
Development of Self-Sensing Carbon Nanotube-Based Composites for Civil Infrastructure Applications
Worldwide, civil infrastructure systems are aging and deteriorating due to maintenance neglect, increasing traffic, and an environment that is becoming increasingly severer. In particular, bridges play a critical role in the transportation network. With limited monies available for maintenance and repair, a need exists for effective yet inexpensive solutions to strengthen and monitor bridges. This presentation provides an overview of the development of carbon nanotube (CNT)-based composites, which offer a means to strengthen and monitor a deteriorated bridge member simultaneously. CNT sensors are created by infusing a fabric, which can be structural or non-structural, with carbon nanotubes to form a piezo-resistive network. Changes in the measured resistance between electrodes, which are attached to the composite layer, have been found to directly correlate to deformations and the formation and accumulation of internal damage. The resulting novel self-sensing composites are sensitive, inexpensive, and able to adhere to almost any shape. Two particular civil infrastructure applications will be presented and discussed in detail. First, two large-scale reinforced concrete beams were strengthened with a composite layer that had an embedded sensing layer and then loaded to failure using load cycles of increasing amplitude. The objective of the second application was to increase the remaining fatigue-life of a cracked steel bridge member. For this application, ASTM E647 test specimens were rehabilitated with self-sensing composites and loaded cyclically to failure. Both applications highlight the potential of CNT-based composites in bridge rehabilitation and monitoring.
ProfessorTommy Chan School of Civil Engineering & Built Environment, Queensland University of Technology (QUT), Brisbane, Australia
Recent Advances in Using Sensors for Structural Health Monitoring for Structures
Structural Health Monitoring (SHM) involves the use of various sensing devices and ancillary systems to monitor the in-situ behaviour of a structure to assess the performance of the structure and evaluate its condition. Because of well demonstrating its effectiveness in helping reduce operational costs and increase safety and reliability, it has attracted numerous researchers working in the area for the last three decades. Structural Health Monitoring (SHM) research can be divided into three main categories: (i) system development, (ii) sensors / measurement and (iii) applications. This presentation will report the recent advances in SHM in these three categories. Under the first category, number of test-beds have been selected covering a range of civil structural systems from laboratory models (two large-scale bridge models) and four real structures (i.e. a highway bridge, one 5-star-green rated medium-rise building, and two footbridges at QUT). In the sensors/measurement category much of the recent work has been done to enhance the Fibre Bragg Grating (FBG) sensing technology. Recent developments include new FBG strain modulation methods and new FBG accelerometers using axial and/or transverse forces and vertical displacement measurements. The application category includes number of ongoing projects on developing various Damage Detection (DD) methods, e.g. correlation MSE with Multi-Layer Genetic Algorithm (ML-GA) based optimization, multi-criteria approach using combination of natural frequencies, Modal Frequencies (MF) and Modal Strain Energies (MSE) to detect damages in bridges, buildings and dams, Correlation based method using ratio of geometric MSE and natural frequency (GMSEF), Time Domain based methods based on Auto-Regressive (AR) and Auto-Regressive Moving Average (ARMA) models, Enhanced MF for locate damage in suspension bridge main cables and hangers. Apart from DD topics, a group has been involved in developing methods to identify the effective prestress force in prestressed concrete box girder bridges by combining various Moving Load Identification (MLI) methods and Electromagnetic Ultrasonic Transducers. Besides, the use of SHM for asset management will also be discussed.