Effects of elevated atmospheric CO2 and O3 on wood density, antomical proerties and decomposition of Northern Hardwoods

Anthropogenic activities continue to drive atmospheric CO2 and O3 concentrations to levels higher than during the pre-industrial era. Accumulating evidence indicates that both elevated CO2 and elevated O3 could modify the quantity and biochemistry of woody plant biomass. Anatomical properties of woody plants are largely influenced by the activity of the cambium and the growth characteristics of wood cells, which are in turn influenced by a range of environmental factors. Hence, alterations in the concentrations of atmospheric CO2 and / or O3 could also impact wood anatomical properties. Many fungi derive their metabolic resources for growth from plant litter, including woody tissue, and therefore modifications in the quantity, biochemistry and anatomical properties of woody plants in response to elevated CO2 and / or O3 could impact the community of wood-decaying fungi and rates of wood decomposition. Consequently carbon and nutrient cycling and productivity of terrestrial ecosystem could also be impacted. Alterations in wood structure and biochemistry of woody plants could also impact wood density and subsequently impact wood quality. This dissertation examined the long term effects of elevated CO2 and / or O3 on wood anatomical properties, wood density, wood-decaying fungi and wood decomposition of northern hardwood tree species at the Aspen Free-Air CO2 and O3 Enrichment (Aspen FACE) project, near Rhinelander, WI, USA. Anatomical properties of wood varied significantly with species and aspen genotypes and radial position within the stem. Elevated CO2 did not have significant effects on wood anatomical properties in trembling aspen, paper birch or sugar maple, except for marginally increasing (P < 0.1) the number of vessels per square millimeter. Elevated O3 marginally or significantly altered vessel lumen diameter, cell wall area and vessel lumen area proportions depending on species and radial position. In line with the modifications in the anatomical properties, elevated CO2 and O3, alone, significantly modified wood density but effects were species and / or genotype specific. However, the effects of elevated CO2 and O3, alone, on wood anatomical properties and density were ameliorated when in combination. Wood species had a much greater impact on the wood-decaying fungal community and initial wood decomposition rate than did growth or decomposition of wood in elevated CO2 and / or O3. Polyporales, Agaricales, and Russulales were the dominant orders of fungi isolated. Based on the current results, future higher levels of CO2 and O3 may have moderate effects on wood quality of northern hardwoods, but for utilization purposes these may not be considered significant. However, wood-decaying fungal community composition and decomposition of northern hardwoods may be altered via shifts in species and / or genotype composition under future higher levels of CO2 and O3.

Fabrication and characterization of room temperature operating single electron transistors using focused ion beam technologies

The single electron transistor (SET) is a Coulomb blockade device, whose operation is based on the controlled manipulation of individual electrons. Single electron transistors show immense potential to be used in future ultra lowpower devices, high density memory and also in high precision electrometry. Most SET devices operate at cryogenic temperatures, because the charging energy is much smaller than the thermal oscillations. The room temperature operation of these devices is possible with sub- 10nm nano-islands due to the inverse dependance of charging energy on the radius of the conducting nano-island. The fabrication of sub-10nm features with existing lithographic techniques is a technological challenge. Here we present the results for the first room temperature operating SET device fabricated using Focused Ion Beam deposition technology. The SET device, incorporates an array of tungsten nano-islands with an average diameter of 8nm. The SET devices shows clear Coulomb blockade for different gate voltages at room temperature. The charging energy of the device was calculated to be 160.0 meV; the capacitance per junction was found to be 0.94 atto F; and the tunnel resistance per junction was calculated to be 1.26 G Ω. The tunnel resistance is five orders of magnitude larger than the quantum of resistance (26 k Ω) and allows for the localization of electrons on the tungsten nano-island. The lower capacitance of the device combined with the high tunnel resistance, allows for the Coulomb blockade effects observed at room temperature. Different device configurations, minimizing the total capacitance of the device have been explored. The effect of the geometry of the nano electrodes on the device characteristics has been presented. Simulated device characteristics, based on the soliton model have been discussed. The first application of SET device as a gas sensor has been demonstrated.

Calibration of the HAWC Gamma-Ray Observatory

The High-Altitude Water Cherenkov (HAWC) Experiment is a gamma-ray observatory that utilizes water silos as Cherenkov detectors to measure the electromagnetic air showers created by gamma rays. The experiment consists of an array of closely packed water Cherenkov detectors (WCDs), each with four photomultiplier tubes (PMTs). The direction of the gamma ray will be reconstructed using the times when the electromagnetic shower front triggers PMTs in each WCD. To achieve an angular resolution as low as 0.1 degrees, a laser calibration system will be used to measure relative PMT response times. The system will direct 300ps laser pulses into two fiber-optic networks. Each network will use optical fan-outs and switches to direct light to specific WCDs. The first network is used to measure the light transit time out to each pair of detectors, and the second network sends light to each detector, calibrating the response times of the four PMTs within each detector. As the relative PMT response times are dependent on the number of photons in the light pulse, neutral density filters will be used to control the light intensity across five orders of magnitude. This system will run both continuously in a low-rate mode, and in a high-rate mode with many intensity levels. In this thesis, the design of the calibration system and systematic studies verifying its performance are presented.

Multiscale constitutive modeling of polymer materials

Materials are inherently multi-scale in nature consisting of distinct characteristics at various length scales from atoms to bulk material. There are no widely accepted predictive multi-scale modeling techniques that span from atomic level to bulk relating the effects of the structure at the nanometer (10-9 meter) on macro-scale properties. Traditional engineering deals with treating matter as continuous with no internal structure. In contrast to engineers, physicists have dealt with matter in its discrete structure at small length scales to understand fundamental behavior of materials. Multiscale modeling is of great scientific and technical importance as it can aid in designing novel materials that will enable us to tailor properties specific to an application like multi-functional materials. Polymer nanocomposite materials have the potential to provide significant increases in mechanical properties relative to current polymers used for structural applications. The nanoscale reinforcements have the potential to increase the effective interface between the reinforcement and the matrix by orders of magnitude for a given reinforcement volume fraction as relative to traditional micro- or macro-scale reinforcements. To facilitate the development of polymer nanocomposite materials, constitutive relationships must be established that predict the bulk mechanical properties of the materials as a function of the molecular structure. A computational hierarchical multiscale modeling technique is developed to study the bulk-level constitutive behavior of polymeric materials as a function of its molecular chemistry. Various parameters and modeling techniques from computational chemistry to continuum mechanics are utilized for the current modeling method. The cause and effect relationship of the parameters are studied to establish an efficient modeling framework. The proposed methodology is applied to three different polymers and validated using experimental data available in literature.

Non-intrusive pressure measurement in microchannels

A non-intrusive interferometric measurement technique has been successfully developed to measure fluid compressibility in both gas and liquid phases via refractive index (RI) changes. The technique, consisting of an unfocused laser beam impinging a glass channel, can be used to separate and quantify cell deflection, fluid flow rates, and pressure variations in microchannels. Currently in fields such as microfluidics, pressure and flow rate measurement devices are orders of magnitude larger than the channel cross-sections making direct pressure and fluid flow rate measurements impossible. Due to the non-intrusive nature of this technique, such measurements are now possible, opening the door for a myriad of new scientific research and experimentation. This technique, adapted from the concept of Micro Interferometric Backscatter Detection (MIBD), boasts the ability to provide comparable sensitivities in a variety of channel types and provides quantification capability not previously demonstrated in backscatter detection techniques. Measurement sensitivity depends heavily on experimental parameters such as beam impingement angle, fluid volume, photodetector sensitivity, and a channel’s dimensional tolerances. The current apparatus readily quantifies fluid RI changes of 10-5 refractive index units (RIU) corresponding to pressures of approximately 14 psi and 1 psi in water and air, respectively. MIBD reports detection capability as low as 10-9 RIU and the newly adapted technique has the potential to meet and exceed this limit providing quantification in the place of detection. Specific device sensitivities are discussed and suggestions are provided on how the technique may be refined to provide optimal quantification capabilities based on experimental conditions.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.