Applications of LiDAR remote sensing of forest structure in the Upper Great Lakes region, USA

We used active remote sensing technology to characterize forest structure in a northern temperate forest on a landscape- and local-level in the Upper Peninsula of Michigan. Specifically, we used a form of active remote sensing called light detection and ranging (e.g., LiDAR) to aid in the depiction of current forest structural stages and total canopy gap area estimation. On a landscape-level, LiDAR data are shown not only to be a useful tool in characterizing forest structure, in both coniferous and deciduous forest cover types, but also as an effective basis for data-driven surrogates for classification of forest structure. On a local-level, LiDAR data are shown to be a benchmark reference point to evaluate field-based canopy gap area estimations, due to the highly accurate nature of such remotely sensed data. The application of LiDAR remote sensed data can help facilitate current and future sustainable forest management.

Preparation of highly-ordered TiO₂ nanotube arrays and their applications in Dye-sensitized solar cells

Titanium oxide is an important semiconductor, which is widely applied for solar cells. In this research, titanium oxide nanotube arrays were synthesized by anodization of Ti foil in the electrolyte composed of ethylene glycol containing 2 vol % H2O and 0.3 wt % NH4F. The voltages of 40V-50V were employed for the anodizing process. Pore diameters and lengths of the TiO2 nanotubes were evaluated by field emission scanning electron microscope (FESEM). The obtained highly-ordered titanium nanotube arrays were exploited to fabricate photoelectrode for the Dye-sensitized solar cells (DSSCS). The TiO2 nanotubes based DSSCS exhibited an excellent performance with a high short circuit current and open circuit voltage as well as a good power conversion efficiency. Those can be attributed to the high surface area and one dimensional structure of TiO2 nanotubes, which could hold a large amount of dyes to absorb light and help electron percolation process to hinder the recombination during the electrons diffusion in the electrolyte.

NICKEL-COBALT SOLID SOLUTION AND ITS APPLICATIONS AS CATALYSTS FOR CARBON DIOXIDE REFORMING OF METHANE

Global warming issue becomes more significant to human beings and other organisms on the earth. Among many greenhouse gases, carbon dioxide (CO2) has the largest contribution to global warming. To find an effective way to utilize the greenhouse gas is urgent. It is the best way to convert CO2 to useful compounds. CO2 reforming of methane is an attractive process to convert CO2 and methane into synthesis gas (CO/H2), which can be used as a feedstock for gasoline, methanol, and other hydrocarbons. Nickel and cobalt were found to have good activity for CO2 reforming. However, they have a poor stability due to carbon deposition. This research developed efficient Ni-Co solid solution catalysts with excellent activities and high stability for CO2 reforming of methane. First, the structure of binary oxide solid solution of nickel and cobalt was investigated. It was found that while the calcination of Ni(NO3)2 and Co(NO3)2 mixture with 1:1 molar ratio at a high temperature above 800 oC generated NiO-CoO solid solution, only Ni3O4-Co3O4 solid solution was observed after the calcination at a low temperature of 500 oC. Furthermore, if the calcination was carried out at a medium temperature arranged from 600 to 700 oC, both NiO-CoO and Ni3O4-Co3O4 solid solutions can be formed. This occurred because Co3O4 can induce the formation of Ni3O4, whereas NiO can stabilize CoO. In addition, the lattice parameter of Ni3O4, which was predicted by using Vegard’s Law, is 8.2054 Å. As a very important part of this dissertation, Ni-Co solid solution was evaluated as catalysts for CO2 reforming of methane. It was revealed that nickel-cobalt solid solution showed excellent catalytic performance and high stability for CO2 reforming of methane. However, the stability of Ni-Co solid solution catalysts is strongly dependent on their composition and preparation condition. The optimum composition is 50%Ni-50%Co. Furthermore, the structure of Ni-Co catalysts was characterized by XRD, Vvis, TPR, TPD, BET, AES, TEM, XANES and EXAFS. The relationship between the structure and the catalytic performance was established: (1) The reduced NiO-CoO solid solution possesses better catalytic performance and stability than the reduced Ni3O4-Co3O4 solid solution. (2) Ni is richer on surface in Ni-Co catalysts. And (3) the reduction of Ni-Co-O solid solution generated two types of particles, small and large particles. The small ones are dispersed on large ones as catalytic component.

DEVELOPMENT AND APPLICATIONS OF GENOMIC RESOURCES FOR TWO NORTH AMERICAN HARDWOOD TAXA

Hardwoods comprise about half of the biomass of forestlands in North America and present many uses including economic, ecological and aesthetic functions. Forest trees rely on the genetic variation within tree populations to overcome the many biotic, abiotic, anthropogenic factors which are further worsened by climate change, that threaten their continued survival and functionality. To harness these inherent genetic variations of tree populations, informed knowledge of the genomic resources and techniques, which are currently lacking or very limited, are imperative for forest managers. The current study therefore aimed to develop genomic microsatellite markers for the leguminous tree species, honey locust, Gleditsia triacanthos L. and test their applicability in assessing genetic variation, estimation of gene flow patterns and identification of a full-sib mapping population. We also aimed to test the usefulness of already developed nuclear and gene-based microsatellite markers in delineation of species and taxonomic relationships between four of the taxonomically difficult Section Lobatae species (Quercus coccinea, Q. ellipsoidalis, Q. rubra and Q. velutina. We recorded 100% amplification of G. triacanthos genomic microsatellites developed using Illumina sequencing techniques in a panel of seven unrelated individuals with 14 of these showing high polymorphism and reproducibility. When characterized in 36 natural population samples, we recorded 20 alleles per locus with no indication for null alleles at 13 of the 14 microsatellites. This is the first report of genomic microsatellites for this species. Honey locust trees occur in fragmented populations of abandoned farmlands and pastures and is described as essentially dioecious. Pollen dispersal if the main source of gene flow within and between populations with the ability to offset the effects of random genetic drift. Factors known to influence gene include fragmentation and degree of isolation, which make the patterns gene flow in fragmented populations of honey locust a necessity for their sustainable management. In this follow-up study, we used a subset of nine of the 14 developed gSSRs to estimate gene flow and identify a full-sib mapping population in two isolated fragments of honey locust. Our analyses indicated that the majority of the seedlings (65-100% - at both strict and relaxed assignment thresholds) were sired by pollen from outside the two fragment populations. Only one selfing event was recorded confirming the functional dioeciousness of honey locust and that the seed parents are almost completely outcrossed. From the Butternut Valley, TN population, pollen donor genotypes were reconstructed and used in paternity assignment analyses to identify a relatively large full-sib family comprised of 149 individuals, proving the usefulness of isolated forest fragments in identification of full-sib families. In the Ames Plantation stand, contemporary pollen dispersal followed a fat-tailed exponential-power distribution, an indication of effective gene flow. Our estimate of δ was 4,282.28 m, suggesting that insect pollinators of honey locust disperse pollen over very long distances. The high proportion of pollen influx into our sampled population implies that our fragment population forms part of a large effectively reproducing population. The high tendency of oak species to hybridize while still maintaining their species identity make it difficult to resolve their taxonomic relationships. Oaks of the section Lobatae are famous in this regard and remain unresolved at both morphological and genetic markers. We applied 28 microsatellite markers including outlier loci with potential roles in reproductive isolation and adaptive divergence between species to natural populations of four known interfertile red oaks, Q. coccinea, Q. ellpsoidalis, Q. rubra and Q. velutina. To better resolve the taxonomic relationships in this difficult clade, we assigned individual samples to species, identified hybrids and introgressive forms and reconstructed phylogenetic relationships among the four species after exclusion of genetically intermediate individuals. Genetic assignment analyses identified four distinct species clusters, with Q. rubra most differentiated from the three other species, but also with a comparatively large number of misclassified individuals (7.14%), hybrids (7.14%) and introgressive forms (18.83%) between Q. ellipsoidalis and Q. velutina. After the exclusion of genetically intermediate individuals, Q. ellipsoidalis grouped as sister species to the largely parapatric Q. coccinea with high bootstrap support (91 %). Genetically intermediate forms in a mixed species stand were located proximate to both potential parental species, which supports recent hybridization of Q. velutina with both Q. ellipsoidalis and Q. rubra. Analyses of genome-wide patterns of interspecific differentiation can provide a better understanding of speciation processes and taxonomic relationships in this taxonomically difficult group of red oak species.

TanDEM-X High Resolution DEMs and Their Applications to Flow Modeling

Lava flow modeling can be a powerful tool in hazard assessments; however, the ability to produce accurate models is usually limited by a lack of high resolution, up-to-date Digital Elevation Models (DEMs). This is especially obvious in places such as Kilauea Volcano (Hawaii), where active lava flows frequently alter the terrain. In this study, we use a new technique to create high resolution DEMs on Kilauea using synthetic aperture radar (SAR) data from the TanDEM-X (TDX) satellite. We convert raw TDX SAR data into a geocoded DEM using GAMMA software [Werner et al., 2000]. This process can be completed in several hours and permits creation of updated DEMs as soon as new TDX data are available. To test the DEMs, we use the Harris and Rowland [2001] FLOWGO lava flow model combined with the Favalli et al. [2005] DOWNFLOW model to simulate the 3-15 August 2011 eruption on Kilauea's East Rift Zone. Results were compared with simulations using the older, lower resolution 2000 SRTM DEM of Hawaii. Effusion rates used in the model are derived from MODIS thermal infrared satellite imagery. FLOWGO simulations using the TDX DEM produced a single flow line that matched the August 2011 flow almost perfectly, but could not recreate the entire flow field due to the relatively high DEM noise level. The issues with short model flow lengths can be resolved by filtering noise from the DEM. Model simulations using the outdated SRTM DEM produced a flow field that followed a different trajectory to that observed. Numerous lava flows have been emplaced at Kilauea since the creation of the SRTM DEM, leading the model to project flow lines in areas that have since been covered by fresh lava flows. These results show that DEMs can quickly become outdated on active volcanoes, but our new technique offers the potential to produce accurate, updated DEMs for modeling lava flow hazards.

MAGNETO-PHOTONIC CRYSTALS FOR OPTICAL SENSING APPLICATIONS

Among the optical structures investigated for optical sensing purpose, a significant amount of research has been conducted on photonic crystal based sensors. A particular advantage of photonic crystal based sensors is that they show superior sensitivity for ultra-small volume sensing. In this study we investigate polarization changes in response to the changes in the cover index of magneto-optic active photonic band gap structures. One-dimensional photonic-band gap structures fabricated on iron garnet materials yield large polarization rotations at the band gap edges. The enhanced polarization effects serve as an excellent tool for chemical sensing showing high degree of sensitivity for photonic crystal cover refractive index changes. The one dimensional waveguide photonic crystals are fabricated on single-layer bismuth-substituted rare earth iron garnet films ((Bi, Y, Lu)3(Fe, Ga)5O12 ) grown by liquid phase epitaxy on gadolinium gallium garnet substrates. Band gaps have been observed where Bragg scattering conditions links forward-going fundamental waveguide modes to backscattered high-order waveguide modes. Large near-band-edge polarization rotations which increase progressively with backscattered-mode order have been experimentally demonstrated for multiple samples with different composition, film thickness and fabrication parameters. Experimental findings are supported by theoretical analysis of Bloch modes polarization states showing that large near stop-band edge rotations are induced by the magneto-photonic crystal. Theoretical and experimental analysis conducted on polarization rotation sensitivity to waveguide photonic crystal cover refractive index changes shows a monotonic enhancement of the rotation with cover index. The sensor is further developed for selective chemical sensing by employing Polypyrrole as the photonic crystal cover layer. Polypyrrole is one of the extensively studied conducting polymers for selective analyte detection. Successful detection of aqueous ammonia and methanol has been achieved with Polypyrrole deposited magneto-photonic crystals.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

SYNTHESIS OF GRAPHENE AND ITS APPLICATIONS FOR DYE-SENSITIZED SOLAR CELLS

Graphene, which is a two-dimensional carbon material, exhibits unique properties that promise its potential applications in photovoltaic devices. Dye-sensitized solar cell (DSSC) is a representative of the third generation photovoltaic devices. Therefore, it is important to synthesize graphene with special structures, which possess excellent properties for dye-sensitized solar cells. This dissertation research was focused on (1) the effect of oxygen content on the structure of graphite oxide, (2) the stability of graphene oxide solution, (3) the application of graphene precipitate from graphene oxide solution as counter electrode for DSSCs, (4) the development of a novel synthesis method for the three-dimensional graphene with honeycomb-like structure, and (5) the exploration of honeycomb structured graphene (HSG) as counter electrodes for DSSCs. Graphite oxide is a crucial precursor to synthesize graphene sheets via chemical exfoliation method. The relationship between the oxygen content and the structures of graphite oxides was still not explored. In this research, the oxygen content of graphite oxide is tuned by changing the oxidation time and the effect of oxygen content on the structure of graphite oxide was evaluated. It has been found that the saturated ratio of oxygen to carbon is 0.47. The types of functional groups in graphite oxides, which are epoxy, hydroxyl, and carboxylgroups, are independent of oxygen content. However, the interplanar space and BET surface area of graphite oxide linearly increases with increasing O/C ratio. Graphene oxide (GO) can easily dissolve in water to form a stable homogeneous solution, which can be used to fabricate graphene films and graphene based composites. This work is the first research to evaluate the stability of graphene oxide solution. It has been found that the introduction of strong electrolytes (HCl, LiOH, LiCl) into GO solution can cause GO precipitation. This indicates that the electrostatic repulsion plays a critical role in stabilizing aqueous GO solution. Furthermore, the HCl-induced GO precipitation is a feasible approach to deposit GO sheets on a substrate as a Pt-free counter electrode for a dye-sensitized solar cell (DSSC), which exhibited 1.65% of power conversion efficiency. To explore broad and practical applications, large-scale synthesis with controllable integration of individual graphene sheets is essential. A novel strategy for the synthesis of graphene sheets with three-dimensional (3D) Honeycomb-like structure has been invented in this project based on a simple and novel chemical reaction (Li2O and CO to graphene and Li2CO3). The simultaneous formation of Li2CO3 with graphene not only can isolate graphene sheets from each other to prevent graphite formation during the process, but also determine the locally curved shape of graphene sheets. After removing Li2CO3, 3D graphene sheets with a honeycomb-like structure were obtained. This would be the first approach to synthesize 3D graphene sheets with a controllable shape. Furthermore, it has been demonstrated that the 3D Honeycomb-Structured Graphene (HSG) possesses excellent electrical conductivity and high catalytic activity. As a result, DSSCs with HSG counter electrodes exhibit energy conversion efficiency as high as 7.8%, which is comparable to that of an expensive noble Pt electrode.

DESIGN, SYNTHESIS AND APPLICATIONS OF FLUORESCENT AND ELECTROCHEMICAL PROBES

“Seeing is believing” the proverb well suits for fluorescent imaging probes. Since we can selectively and sensitively visualize small biomolecules, organelles such as lysosomes, neutral molecules, metal ions, anions through cellular imaging, fluorescent probes can help shed light on the physiological and pathophysiological path ways. Since these biomolecules are produced in low concentrations in the biochemical pathways, general analytical techniques either fail to detect or are not sensitive enough to differentiate the relative concentrations. During my Ph.D. study, I exploited synthetic organic techniques to design and synthesize fluorescent probes with desirable properties such as high water solubility, high sensitivity and with varying fluorescent quantum yields. I synthesized a highly water soluble BOIDPY-based turn-on fluorescent probe for endogenous nitric oxide. I also synthesized a series of cell membrane permeable near infrared (NIR) pH activatable fluorescent probes for lysosomal pH sensing. Fluorescent dyes are molecular tools for designing fluorescent bio imaging probes. This prompted me to design and synthesize a hybrid fluorescent dye with a functionalizable chlorine atom and tested the chlorine re-activity for fluorescent probe design. Carbohydrate and protein interactions are key for many biological processes, such as viral and bacterial infections, cell recognition and adhesion, and immune response. Among several analytical techniques aimed to study these interactions, electrochemical bio sensing is more efficient due to its low cost, ease of operation, and possibility for miniaturization. During my Ph.D., I synthesized mannose bearing aniline molecule which is successfully tested as electrochemical bio sensor. A Ferrocene-mannose conjugate with an anchoring group is synthesized, which can be used as a potential electrochemical biosensor.

MINING AND VERIFICATION OF TEMPORAL EVENTS WITH APPLICATIONS IN COMPUTER MICRO-ARCHITECTURE RESEARCH

Computer simulation programs are essential tools for scientists and engineers to understand a particular system of interest. As expected, the complexity of the software increases with the depth of the model used. In addition to the exigent demands of software engineering, verification of simulation programs is especially challenging because the models represented are complex and ridden with unknowns that will be discovered by developers in an iterative process. To manage such complexity, advanced verification techniques for continually matching the intended model to the implemented model are necessary. Therefore, the main goal of this research work is to design a useful verification and validation framework that is able to identify model representation errors and is applicable to generic simulators. The framework that was developed and implemented consists of two parts. The first part is First-Order Logic Constraint Specification Language (FOLCSL) that enables users to specify the invariants of a model under consideration. From the first-order logic specification, the FOLCSL translator automatically synthesizes a verification program that reads the event trace generated by a simulator and signals whether all invariants are respected. The second part consists of mining the temporal flow of events using a newly developed representation called State Flow Temporal Analysis Graph (SFTAG). While the first part seeks an assurance of implementation correctness by checking that the model invariants hold, the second part derives an extended model of the implementation and hence enables a deeper understanding of what was implemented. The main application studied in this work is the validation of the timing behavior of micro-architecture simulators. The study includes SFTAGs generated for a wide set of benchmark programs and their analysis using several artificial intelligence algorithms. This work improves the computer architecture research and verification processes as shown by the case studies and experiments that have been conducted.

HIGH VOLTAGE ELECTROPHORETIC DEPOSITION FOR ELECTROCHEMICAL ENERGY STORAGE AND OTHER APPLICATIONS

High voltage electrophoretic deposition (HVEPD) has been developed as a novel technique to obtain vertically aligned forests of one-dimensional nanomaterials for efficient energy storage. The ability to control and manipulate nanomaterials is critical for their effective usage in a variety of applications. Oriented structures of one-dimensional nanomaterials provide a unique opportunity to take full advantage of their excellent mechanical and electrochemical properties. However, it is still a significant challenge to obtain such oriented structures with great process flexibility, ease of processing under mild conditions and the capability to scale up, especially in context of efficient device fabrication and system packaging. This work presents HVEPD as a simple, versatile and generic technique to obtain vertically aligned forests of different one-dimensional nanomaterials on flexible, transparent and scalable substrates. Improvements on material chemistry and reduction of contact resistance have enabled the fabrication of high power supercapacitor electrodes using the HVEPD method. The investigations have also paved the way for further enhancements of performance by employing hybrid material systems and AC/DC pulsed deposition. Multi-walled carbon nanotubes (MWCNTs) were used as the starting material to demonstrate the HVEPD technique. A comprehensive study of the key parameters was conducted to better understand the working mechanism of the HVEPD process. It has been confirmed that HVEPD was enabled by three key factors: high deposition voltage for alignment, low dispersion concentration to avoid aggregation and simultaneous formation of holding layer by electrodeposition for reinforcement of nanoforests. A set of suitable parameters were found to obtain vertically aligned forests of MWCNTs. Compared with their randomly oriented counterparts, the aligned MWCNT forests showed better electrochemical performance, lower electrical resistance and a capability to achieve superhydrophpbicity, indicating their potential in a broad range of applications. The versatile and generic nature of the HVEPD process has been demonstrated by achieving deposition on flexible and transparent substrates, as well as aligned forests of manganese dioxide (MnO2) nanorods. A continuous roll-printing HVEPD approach was then developed to obtain aligned MWCNT forest with low contact resistance on large, flexible substrates. Such large-scale electrodes showed no deterioration in electrochemical performance and paved the way for practical device fabrication. The effect of a holding layer on the contact resistance between aligned MWCNT forests and the substrate was studied to improve electrochemical performance of such electrodes. It was found that a suitable precursor salt like nickel chloride could be used to achieve a conductive holding layer which helped to significantly reduce the contact resistance. This in turn enhanced the electrochemical performance of the electrodes. High-power scalable redox capacitors were then prepared using HVEPD. Very high power/energy densities and excellent cyclability have been achieved by synergistically combining hydrothermally synthesized, highly crystalline α-MnO2 nanorods, vertically aligned forests and reduced contact resistance. To further improve the performance, hybrid electrodes have been prepared in the form of vertically aligned forest of MWCNTs with branches of α-MnO2 nanorods on them. Large- scale electrodes with such hybrid structures were manufactured using continuous HVEPD and characterized, showing further improved power and energy densities. The alignment quality and density of MWCNT forests were also improved by using an AC/DC pulsed deposition technique. In this case, AC voltage was first used to align the MWCNTs, followed by immediate DC voltage to deposit the aligned MWCNTs along with the conductive holding layer. Decoupling of alignment from deposition was proven to result in better alignment quality and higher electrochemical performance.