Model-based Processing of Micro-cantilever Sensor Arrays

Tringe, J W ; Clague, D S ; Candy, J V ; Lee, C L ; Rudd, R E ; Burnham, A K

Lawrence Livermore National Laboratory

关键词: Signal-To-Noise Ratio;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Processing;    Simulation;    Targets;   

DOI  :  10.2172/15011800
RP-ID  :  UCRL-TR-208164
RP-ID  :  W-7405-ENG-48
RP-ID  :  15011800

美国|英语

来源: UNT Digital Library

【 摘 要 】

We develop a model-based processor (MBP) for a micro-cantilever array sensor to detect target species in solution. After discussing the generalized framework for this problem, we develop the specific model used in this study. We perform a proof-of-concept experiment, fit the model parameters to the measured data and use them to develop a Gauss-Markov simulation. We then investigate two cases of interest: (1) averaged deflection data, and (2) multi-channel data. In both cases the evaluation proceeds by first performing a model-based parameter estimation to extract the model parameters, next performing a Gauss-Markov simulation, designing the optimal MBP and finally applying it to measured experimental data. The simulation is used to evaluate the performance of the MBP in the multi-channel case and compare it to a ''smoother'' (''averager'') typically used in this application. It was shown that the MBP not only provides a significant gain ({approx} 80dB) in signal-to-noise ratio (SNR), but also consistently outperforms the smoother by 40-60 dB. Finally, we apply the processor to the smoothed experimental data and demonstrate its capability for chemical detection. The MBP performs quite well, though it includes a correctable systematic bias error. The project's primary accomplishment was the successful application of model-based processing to signals from micro-cantilever arrays: 40-60 dB improvement vs. the smoother algorithm was demonstrated. This result was achieved through the development of appropriate mathematical descriptions for the chemical and mechanical phenomena, and incorporation of these descriptions directly into the model-based signal processor. A significant challenge was the development of the framework which would maximize the usefulness of the signal processing algorithms while ensuring the accuracy of the mathematical description of the chemical-mechanical signal. Experimentally, the difficulty was to identify and characterize the non-target signals present in the measurement system. In the future, these signals will limit the ability of the sensor to detect very small quantities of chemicals generated by nuclear processing. In this project, it became necessary to make use of a model system, mercaptoethanol, which created a large, reproducible signal that could be readily analyzed with the model-based processor. Further, redundant cantilevers were examined exclusively: all levers were nominally identically functionalized, and no ''control'' levers were used that did not react to the mercaptoethanol signal. To demonstrate the full utility of the MBP for chemical sensing, the logical and necessary next steps are (1) verify the physical models used in this study for a variety of solvents and target molecules (this data has already been obtained as part of this study) (2) make use of control levers, and (3) extend the experimental library to include low concentrations of chemical targets of practical interest for sensing nuclear processes.