Fluorescence optofluidic microscopy and fluorescence microscopy based on the Talbot effect

Light microscopy has been one of the most common tools in biological research, because of its high resolution and non-invasive nature of the light. Due to its high sensitivity and specificity, fluorescence is one of the most important readout modes of light microscopy. This thesis presents two new fluorescence microscopic imaging techniques: fluorescence optofluidic microscopy and fluorescent Talbot microscopy. The designs of the two systems are fundamentally different from conventional microscopy, which makes compact and portable devices possible. The components of the devices are suitable for mass-production, making the microscopic imaging system more affordable for biological research and clinical diagnostics.

Fluorescence optofluidic microscopy (FOFM) is capable of imaging fluorescent samples in fluid media. The FOFM employs an array of Fresnel zone plates (FZP) to generate an array of focused light spots within a microfluidic channel. As a sample flows through the channel and across the array of focused light spots, a filter-coated CMOS sensor collects the fluorescence emissions. The collected data can then be processed to render a fluorescence microscopic image. The resolution, which is determined by the focused light spot size, is experimentally measured to be 0.65 μm.

Fluorescence Talbot microscopy (FTM) is a fluorescence chip-scale microscopy technique that enables large field-of-view (FOV) and high-resolution imaging. The FTM method utilizes the Talbot effect to project a grid of focused excitation light spots onto the sample. The sample is placed on a filter-coated CMOS sensor chip. The fluorescence emissions associated with each focal spot are collected by the sensor chip and are composed into a sparsely sampled fluorescence image. By raster scanning the Talbot focal spot grid across the sample and collecting a sequence of sparse images, a filled-in high-resolution fluorescence image can be reconstructed. In contrast to a conventional microscope, a collection efficiency, resolution, and FOV are not tied to each other for this technique. The FOV of FTM is directly scalable. Our FTM prototype has demonstrated a resolution of 1.2 μm, and the collection efficiency equivalent to a conventional microscope objective with a 0.70 N.A. The FOV is 3.9 mm × 3.5 mm, which is 100 times larger than that of a 20X/0.40 N.A. conventional microscope objective. Due to its large FOV, high collection efficiency, compactness, and its potential for integration with other on-chip devices, FTM is suitable for diverse applications, such as point-of-care diagnostics, large-scale functional screens, and long-term automated imaging.

On-chip integrated label-free optical biosensing

This thesis investigates the design and implementation of a label-free optical biosensing system utilizing a robust on-chip integrated platform. The goal has been to transition optical micro-resonator based label-free biosensing from a laborious and delicate laboratory demonstration to a tool for the analytical life scientist. This has been pursued along four avenues: (1) the design and fabrication of high-$Q$ integrated planar microdisk optical resonators in silicon nitride on silica, (2) the demonstration of a high speed optoelectronic swept frequency laser source, (3) the development and integration of a microfluidic analyte delivery system, and (4) the introduction of a novel differential measurement technique for the reduction of environmental noise.

The optical part of this system combines the results of two major recent developments in the field of optical and laser physics: the high-$Q$ optical resonator and the phase-locked electronically controlled swept-frequency semiconductor laser. The laser operates at a wavelength relevant for aqueous sensing, and replaces expensive and fragile mechanically-tuned laser sources whose frequency sweeps have limited speed, accuracy and reliability. The high-$Q$ optical resonator is part of a monolithic unit with an integrated optical waveguide, and is fabricated using standard semiconductor lithography methods. Monolithic integration makes the system significantly more robust and flexible compared to current, fragile embodiments that rely on the precarious coupling of fragile optical fibers to resonators. The silicon nitride on silica material system allows for future manifestations at shorter wavelengths. The sensor also includes an integrated microfluidic flow cell for precise and low volume delivery of analytes to the resonator surface. We demonstrate the refractive index sensing action of the system as well as the specific and nonspecific adsorption of proteins onto the resonator surface with high sensitivity. Measurement challenges due to environmental noise that hamper system performance are discussed and a differential sensing measurement is proposed, implemented, and demonstrated resulting in the restoration of a high performance sensing measurement.

The instrument developed in this work represents an adaptable and cost-effective platform capable of various sensitive, label-free measurements relevant to the study of biophysics, biomolecular interactions, cell signaling, and a wide range of other life science fields. Further development is necessary for it to be capable of binding assays, or thermodynamic and kinetics measurements; however, this work has laid the foundation for the demonstration of these applications.

Sensor networks for geospatial event detection - theory and applications

This thesis presents theories, analyses, and algorithms for detecting and estimating parameters of geospatial events with today's large, noisy sensor networks. A geospatial event is initiated by a significant change in the state of points in a region in a 3-D space over an interval of time. After the event is initiated it may change the state of points over larger regions and longer periods of time. Networked sensing is a typical approach for geospatial event detection. In contrast to traditional sensor networks comprised of a small number of high quality (and expensive) sensors, trends in personal computing devices and consumer electronics have made it possible to build large, dense networks at a low cost. The changes in sensor capability, network composition, and system constraints call for new models and algorithms suited to the opportunities and challenges of the new generation of sensor networks. This thesis offers a single unifying model and a Bayesian framework for analyzing different types of geospatial events in such noisy sensor networks. It presents algorithms and theories for estimating the speed and accuracy of detecting geospatial events as a function of parameters from both the underlying geospatial system and the sensor network. Furthermore, the thesis addresses network scalability issues by presenting rigorous scalable algorithms for data aggregation for detection. These studies provide insights to the design of networked sensing systems for detecting geospatial events. In addition to providing an overarching framework, this thesis presents theories and experimental results for two very different geospatial problems: detecting earthquakes and hazardous radiation. The general framework is applied to these specific problems, and predictions based on the theories are validated against measurements of systems in the laboratory and in the field.

Electro-optic integration of embedded electrodes and waveguides in LiNbO3 using a femtosecond laser

We describe the fabrication of a Mach-Zehnder optical modulator in LiNbO3 by femtosecond laser micormachining, which is composed of optical waveguides inscripted by a femtosecond laser and embedded microelectrodes subsequently using femtosecond laser ablation and selective electroless plating. A half-wave voltage close to 19 V is achieved at a wavelength of 632.8 nm with an interaction length of 2.6 mm. This simple and cost-effective technique opens up new opportunities for fabricating integrated electro-optic devices. (C) 2008 Optical Society of America

Fabrication of an integrated Raman sensor by selective surface metallization using a femtosecond laser oscillator

We demonstrate that a Raman sensor integrated with a micro-heater, a microfluidic chamber, and a surface-enhanced Raman scattering (SERS) substrate can be fabricated in a glass chip by femtosecond laser micromachining. The micro-heater and the SERS substrate are fabricated by selective metallization on the glass surface using a femtosecond laser oscillator, whereas the microfluidic chamber embedded in the glass sample is fabricated by femtosecond laser ablation using a femtosecond laser amplifier. We believed that this new strategy for fabricating multifunctional integrated microchips has great potential application for lab-on-a-chips. (C) 2008 Elsevier B.V. All rights reserved.

Geometric integration applied to moving mesh methods and degenerate Lagrangians

Moving mesh methods (also called r-adaptive methods) are space-adaptive strategies used for the numerical simulation of time-dependent partial differential equations. These methods keep the total number of mesh points fixed during the simulation, but redistribute them over time to follow the areas where a higher mesh point density is required. There are a very limited number of moving mesh methods designed for solving field-theoretic partial differential equations, and the numerical analysis of the resulting schemes is challenging. In this thesis we present two ways to construct r-adaptive variational and multisymplectic integrators for (1+1)-dimensional Lagrangian field theories. The first method uses a variational discretization of the physical equations and the mesh equations are then coupled in a way typical of the existing r-adaptive schemes. The second method treats the mesh points as pseudo-particles and incorporates their dynamics directly into the variational principle. A user-specified adaptation strategy is then enforced through Lagrange multipliers as a constraint on the dynamics of both the physical field and the mesh points. We discuss the advantages and limitations of our methods. The proposed methods are readily applicable to (weakly) non-degenerate field theories---numerical results for the Sine-Gordon equation are presented.

In an attempt to extend our approach to degenerate field theories, in the last part of this thesis we construct higher-order variational integrators for a class of degenerate systems described by Lagrangians that are linear in velocities. We analyze the geometry underlying such systems and develop the appropriate theory for variational integration. Our main observation is that the evolution takes place on the primary constraint and the 'Hamiltonian' equations of motion can be formulated as an index 1 differential-algebraic system. We then proceed to construct variational Runge-Kutta methods and analyze their properties. The general properties of Runge-Kutta methods depend on the 'velocity' part of the Lagrangian. If the 'velocity' part is also linear in the position coordinate, then we show that non-partitioned variational Runge-Kutta methods are equivalent to integration of the corresponding first-order Euler-Lagrange equations, which have the form of a Poisson system with a constant structure matrix, and the classical properties of the Runge-Kutta method are retained. If the 'velocity' part is nonlinear in the position coordinate, we observe a reduction of the order of convergence, which is typical of numerical integration of DAEs. We also apply our methods to several models and present the results of our numerical experiments.

Selective data gathering in community sensor networks

Smartphones and other powerful sensor-equipped consumer devices make it possible to sense the physical world at an unprecedented scale. Nearly 2 million Android and iOS devices are activated every day, each carrying numerous sensors and a high-speed internet connection. Whereas traditional sensor networks have typically deployed a fixed number of devices to sense a particular phenomena, community networks can grow as additional participants choose to install apps and join the network. In principle, this allows networks of thousands or millions of sensors to be created quickly and at low cost. However, making reliable inferences about the world using so many community sensors involves several challenges, including scalability, data quality, mobility, and user privacy.

This thesis focuses on how learning at both the sensor- and network-level can provide scalable techniques for data collection and event detection. First, this thesis considers the abstract problem of distributed algorithms for data collection, and proposes a distributed, online approach to selecting which set of sensors should be queried. In addition to providing theoretical guarantees for submodular objective functions, the approach is also compatible with local rules or heuristics for detecting and transmitting potentially valuable observations. Next, the thesis presents a decentralized algorithm for spatial event detection, and describes its use detecting strong earthquakes within the Caltech Community Seismic Network. Despite the fact that strong earthquakes are rare and complex events, and that community sensors can be very noisy, our decentralized anomaly detection approach obtains theoretical guarantees for event detection performance while simultaneously limiting the rate of false alarms.

The gated integration technique for the accurate measurement of the autocorrelation function of speckle intensities scattered from random phase screens

A new approach based on the gated integration technique is proposed for the accurate measurement of the autocorrelation function of speckle intensities scattered from a random phase screen. The Boxcar used for this technique in the acquisition of the speckle intensity data integrates the photoelectric signal during its sampling gate open, and it repeats the sampling by a preset number, in. The average analog of the in samplings output by the Boxcar enhances the signal-to-noise ratio by root m, because the repeated sampling and the average make the useful speckle signals stable, while the randomly varied photoelectric noise is suppressed by 1/ root m. In the experiment, we use an analog-to-digital converter module to synchronize all the actions such as the stepped movement of the phase screen, the repeated sampling, the readout of the averaged output of the Boxcar, etc. The experimental results show that speckle signals are better recovered from contaminated signals, and the autocorrelation function with the secondary maximum is obtained, indicating that the accuracy of the measurement of the autocorrelation function is greatly improved by the gated integration technique. (C) 2006 Elsevier Ltd. All rights reserved.