Microcantilever sensors for biochemical detection and analysis

Biochemical agents, including bacteria and toxins, are potentially dangerous and responsible for a wide variety of diseases. Reliable detection and characterization of small samples is necessary in order to reduce and eliminate their harmful consequences. Microcantilever sensors offer a potential alternative to the state of the art due to their small size, fast response time, and the ability to operate in air and liquid environments. At present, there are several technology limitations that inhibit application of microcantilever to biochemical detection and analysis, including difficulties in conducting temperature-sensitive experiments, material inadequacy resulting in insufficient cell capture, and poor selectivity of multiple analytes. This work aims to address several of these issues by introducing microcantilevers having integrated thermal functionality and by introducing nanocrystalline diamond as new material for microcantilevers. Microcantilevers are designed, fabricated, characterized, and used for capture and detection of cells and bacteria. The first microcantilever type described in this work is a silicon cantilever having highly uniform in-plane temperature distribution. The goal is to have 100 μm square uniformly heated area that can be used for thermal characterization of films as well as to conduct chemical reactions with small amounts of material. Fabricated cantilevers can reach above 300C while maintaining temperature uniformity of 2−4%. This is an improvement of over one order of magnitude over currently available cantilevers. The second microcantilever type is a doped single crystal silicon cantilever having a thin coating of ultrananocrystalline diamond (UNCD). The primary application of such a device is in biological testing, where diamond acts as a stable, electrically isolated reaction surface while silicon layer provides controlled heating with minimum variations in temperature. This work shows that composite cantilevers of this kind are an effective platform for temperature-sensitive biological experiments, such as heat lysing and polymerase chain reaction. The rapid heat-transfer of Si-UNCD cantilever compromised the membrane of NIH 3T3 fibroblast and lysed the cell nucleus within 30 seconds. Bacteria cells, Listeria monocytogenes V7, were shown to be captured with biotinylated heat-shock protein on UNCD surface and 90% of all viable cells exhibit membrane porosity due to high heat in 15 seconds. Lastly, a sensor made solely from UNCD diamond is fabricated with the intention of being used to detect the presence of biological species by means of an integrated piezoresistor or through frequency change monitoring. Since UNCD diamond has not been previously used in piezoresistive applications, temperature-denpendent piezoresistive coefficients and gage factors are determined first. The doped UNCD exhibits a significant piezoresistive effect with gauge factor of 7.53±0.32 and a piezoresistive coefficient of 8.12×10^−12 Pa^−1 at room temperature. The piezoresistive properties of UNCD are constant over the temperature range of 25−200C. 300 μm long cantilevers have the highest sensitivity of 0.186 m-Ohm/Ohm per μm of cantilever end deflection, which is approximately half that of similarly sized silicon cantilevers. UNCD cantilever arrays were fabricated consisting of four sixteen-cantilever arrays of length 20–90 μm in addition to an eight-cantilever array of length 120 μm. Laser doppler vibrometry (LDV) measured the cantilever resonant frequency, which ranged as 218 kHz−5.14 MHz in air and 73 kHz−3.68 MHz in water. The quality factor of the cantilever was 47−151 in air and 18−45 in water. The ability to measure frequencies of the cantilever arrays opens the possibility for detection of individual bacteria by monitoring frequency shift after cell capture.

Thermomechanical sensitivity of microcantilever in the mid-infrared spectral region

This paper reports the thermomechanical sensitivity of bimaterial cantilevers over a mid-infrared (IR) spectral range (5-10 µm) that is critical both for chemical analysis via vibrational spectroscopy and for direct thermal detection in the 300-700 K range. Mechanical bending sensitivity and noise were measured and modeled for six commercially available microcantilevers, which consist of either an aluminum film on a silicon cantilever or a gold film on a silicon nitride cantilever. The spectral sensitivity of each cantilever was determined by recording cantilever deflection when illuminated with IR light from a monochromator. Rigorous modeling and systematic characterization of the optical system allowed for a quantitative estimate of IR energy incident upon the cantilever. Separately, spectral absorptance of the cantilever was measured using Fourier transform infrared (FT-IR) microscopy, which was compared with analytical models of radiation onto the cantilever and heat flow within the cantilever. The predictions of microcantilever thermomechanical bending sensitivity and noise agree well with measurements, resulting in a ranking of these cantilevers for their potential use in IR measurements.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.