As an important part of the periodic table, metal elements have attracted widespread attention due to their special physical and chemical properties, as well as effective functionalities. Many metals at the nanoscale level exhibit a wide array of applications, ranging from catalysis to photonics, electronics, energy conversion/storage, and medicine. To obtain a more effective functionality in application, it is indispensable to synthesize uniform metal nanoparticles with well-defined size, morphology, composition, and crystal structures. In this dissertation, we will demonstrate high-boiling point solvent method for synthesizing metal nanocrystals, ranging from single metal nanocrystals (e.g., iridium (Ir), ruthenium (Ru), germanium (Ge), bismuth (Bi)) to binary metal nanocrystals (e.g., Sn-Ge), and ternary intermetallic compounds (e.g., Pt1-xPdxBi). By varying different halogen ions, we can get different morphologies of metal nanocrystals. We will further study the catalytic effect of Pd metal nanocrystals supported on silicon spheres and realize the hydrodeoxygenation reaction of vanillin under mild conditions.First, we used bismuth as an example to study the shape-controlled synthesis of metal nanocrystals by adjusting the injection temperature and the added halide ions (e.g., Cl-, Br-). Our findings indicated that due to the different electronegativities, halide ions are selectively adsorbed on specific crystal planes during the growth of Bi NCs, leading to different morphologies. Then we proposed a tungsten hexacarbonyl (W(CO)6)-assisted reduction strategy for obtaining uniform metal nanoparticles (e.g., Ir, Ru, Ge, Bi) of different metal salts. This strategy was extended to the synthesis of uniform binary metal (e.g., Sn-Ge) nanoparticles, which we can get tunable bandgap (0.51 eV to 0.72 eV) based on the controlled reaction of Ge2+ precursor solution with uniform tin (Sn) nanocrystals (NCs) as the template. Next, we realized the synthesis of intermetallic Pt1-xPdxBi nanoplates with controllable compositions, including Pt0.5Pb0.5Bi, Pt0.25Pd0.75Bi, and Pt0.75Pd0.25Bi via the sequential complexation-reduction-sorting method. Furthermore, we used palladium (Pd) metal nanoparticles (NPs) as a photocatalyst to trigger the hydrodeoxygenation reaction of vanillin. We demonstrated a model to disperse free-standing Pd NP on dielectric silica nanospheres (SiOx NSs). The spherical shape of SiOx can cause scattering resonance, thereby enhancing the local electric field on or near the surface to enhance light absorption of Pd NPs, further realizing a more effective catalyze on chemical reactions. We found that the adsorption of H2 on Pd is too strong to support the reaction effectively, but light absorption can reduce the "poisoning effect" by weakening the adsorption of hydrogen on Pd surface. Overall, we use innovative strategies to effectively synthesize a variety of high-quality metal nanomaterials. Our work shows that the Pd-NP/SiOx-NS composite nanostructure using dielectric SiOx as an optical nanoantenna is a promising photocatalyst that can drive photonic chemical conversion with high efficiency. / Chemistry

Scania provides sustainable transport systems powered by bioethanol, biogas, biodiesel along with hybrid and conventional solutions. Today Scania offers the largest variety of engines operating on alternative fuels in the market. The number of the alternative fuel operated vehicles sold in 2016 increased by 40 % [1]. Nevertheless, one of the alternative fuels – biodiesel - is a source of inorganic contaminants. These impurities can detrimentally affect the diesel truck after-treatment system that is responsible for harmful emission abatement. As a consequence, better understanding of the alternative fuel impact on the after-treatment system is necessary for further development of a sustainable transportation system. This thesis is focused on the diesel oxidation catalyst (DOC) that is one of the major components in the diesel truck after-treatment system. Catalyst performance due to chemical deactivation of biodiesel derived inorganic contaminants (P, Na and Ca) is determined and analysed. The study covers PtPd/Al 2O3 DOC preparation and poisoning by the incipient wetness impregnation method, monolith dip-coating, fresh and poisoned catalyst characterization (BET, CO chemisorption, TPR, ICP-OES, TEM-EDS, SEM-EDS, XRD). Catalyst activity tests in a laboratory scale activity testing rig are performed to study carbon monoxide, nitric oxide and propylene oxidation reactions before and after the poisoning. Sulphur effect on the catalyst activity is determined after the gas-phase poisoning with SO2.

The main goal of this dissertation is to introduce and demonstrate a new method for the rapid determination of the nonlinear absorption spectra and the dispersion of the nonlinear refraction of optical materials in the visible and near IR spectral regions. However, conventional methods like, white-light continuum pump-probe and Z-scan techniques were used to measure the peak 2PA cross-sections for a number of commercially available photoinitiators. In the new method mentioned above, a high energy, broadband femtosecond white-light continuum is used to replace the single wavelength source conventionally used in a Z-scan experiment. In a Z-scan experiment, the transmittance of a focused beam through a sample is monitored as the sample travels through the focus, in the Z direction, along the focused beam. Providing the sample exhibits nonlinear absorption and/or refraction, the detector monitors a change in transmittance and/or a change in the beam divergence (if the energy is partially collected through an aperture in front of the detector). Replacing the single wavelength source with a white-light continuum allows for a much faster way of measuring nonlinear absorption/refraction spectra. This could eliminate the need for using other tunable sources (e.g. Optical Parameter Generators/Amplifiers) for nonlinear measurements. These sources made nonlinear spectroscopy using Z-scan experiments a time consuming task. This new source/method allows for rapid and simultaneous measurement of the nonlinear absorption spectrum and the dispersion of the nonlinear refraction. We have confirmed the functionality of the continuum as a source for nonlinear optical characterization of materials by using it to perform Z-scans on the well characterized semiconductors ZnSe and ZnS and on solutions of organic dyes.

Incineration of municipal solid waste (MSW) is common for energy recovery, and management of municipal solid waste incineration (MSWI) ashes has received a growing attention around the world. In the U.S., generation of MSW has increased up to 65% since 1980, to the current level of 251 million tons per year with 53.8% landfilled, 34.5% recycled and composted, and 11.7% incinerated with energy recovery. In the process of incineration, MSWI ash is being produced as byproducts; about 80 to 90% of the MSWI ash is bottom ash (BA) and 10 to 20% is fly ash (FA) by weight. The current practice of the U.S. is to combine both BA and FA to meet the criteria to qualify as non-hazardous, and all combined ashes are disposed in landfills. European countries have utilized MSWI BA as beneficial construction materials by separating it from FA. The FA is mostly limited to landfill disposal as hazardous material due to its high content of toxic elements and salts. BA has been actively recycled in the areas of roadbed, asphalt paving, and concrete products in many of European and Asian countries. In those countries, recycling programs (including required physical properties and environmental criteria) of ash residue management have been developed so as to encourage and enforce the reuse of MSWI ashes instead of landfill disposal. Moreover, many studies have demonstrated the beneficial use of MSWI ashes as engineering materials with minimum environmental impacts. On the other hand, the U.S. has shown a lack of consistent and effective management plans, as well as environmental regulations for the use of MSWI ashes., Due to persistent uncertainty of engineering properties and inconsistency in the Federal and State regulations in the U.S., however, the recycling of the MSWI ashes has been hindered and they are mostly disposed in landfills. In this research work, current management practice, existing regulations, and environmental consequences of MSWI ashes utilization are comprehensively reviewed worldwide and nationwide with an emphasis of the potential area of its utilization in asphalt paving and concrete product. This research also entails a detailed chemical and microstructural characterization of MSWI BA and FA produced from a Refuse Derived Fuel (RDF) facility in Florida so that the MSWI ash is well characterized for its beneficial uses as construction materials. The material characterization includes Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and X-ray Diffraction (XRD) techniques. In addition, leaching experiments have been conducted to investigate the environmental properties (e.g. leachate concentration) of BA and ash-mixed hot mix asphalt (HMA) and Portland cement concrete (PCC). Leaching results reveals the reduced leaching potential of toxic material from MSWI ashes while incorporated in HMA and PCC. Lastly, a preliminary experimental approach has been devised for the vitrification of FA which is a promising thermal process of transferring material into glassy state with higher physical and chemical integrity to reduce toxicity so that utilization of FA can be possible.

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X-ray tomography is an imaging technique to inspect objects' internal structures with externally measured data by X-ray radiation non-destructively. However, there are concerns about X-ray radiation damage and tomographic acquisition speed in real-life applications. Strategies with insufficient measurements, such as measurements with insufficient dosage (low-dose) and measurements with insufficient projection angles (sparse-view), have been proposed to relieve these problems but are generally compromising imaging quality. Such a dilemma inspires the development of advanced tomographic imaging techniques, in particular, deep learning algorithms to improve reconstruction results with insufficient measurements. The overall aim of this thesis is to design efficient and robust data-driven algorithms with the help of prior knowledge from physics insights and measurement models. We first introduce a hierarchical synthesis CNN (HSCNN), which is a knowledge-incorporated data-driven tomographic reconstruction method for sparse-view and low-dose tomography with a split-and-synthesis approach. This proposed learning-based method informs the forward model biases based on data-driven learning but with reduced training data. The learning scheme is robust against sampling bias and aberrations introduced in the forward modeling. High-fidelity X-ray tomographic imaging reconstruction results are obtained with a very sparse number of projection angles for both numerical simulated and physics experiments. Comparison with both conventional non-learning-based algorithms and advanced learning-based approaches shows improved accuracy and reduced training data size. As a result of the split-and-synthesis strategy, the trained network could be transferable to new cases. We then present a deep learning-based enhancement method, HDrec (hybrid-dose reconstruction algorithm), for low-dose tomography reconstruction via a hybrid-dose acquisition strategy composed of textit{extremely sparse-view normal-dose measurements} and textit{full-view low-dose measurements}. The training is applied for each individual sample without the need of transferring the trained models for other samples. Evaluation of two experimental datasets under different hybrid-dose acquisition conditions shows significantly improved structural details and reduced noise levels compared to results with traditional analytical and regularization-based iterative reconstruction methods from uniform acquisitions under the same amount of total dosage. Our proposed approach is also more efficient in terms of single projection denoising and single image reconstruction. In addition, we provide a strategy to distribute dosage smartly with improved reconstruction quality. When the total dosage is limited, the strategy of combining a very few numbers of normal-dose projections and with not-too-low full-view low-dose measurements greatly outperforms the uniform distribution of the dosage throughout all projections. We finally apply the proposed data-driven X-ray tomographic imaging reconstruction techniques, HSCNN and HDrec, to the dynamic damage/defect characterization applications for the cellular materials and binder jetting additive manufacturing. These proposed algorithms improve data acquisition speeds to record internal dynamic structure changes. A quantitative comprehensive framework is proposed to study the dynamic internal behaviors of cellular structure, which contains four modules: (i) In-situ fast synchrotron X-ray tomography, which enables collection of 3D microstructure in a macroscopic volume; (ii) Automated 3D damage features detection to recognize damage behaviors in different scales; (iii) Quantitative 3D structural analysis of the cellular microstructure, by which key morphological descriptors of the structure are extracted and quantified; (iv) Automated multi-scale damage structure analysis, which provides a quantitative understanding of damage behaviors. In terms of binder jetting materials, we show a pathway toward the efficient acquisition of holistic defect information and robust morphological representation through the integration of (i) fast tomography algorithms, (ii) 3D morphological analysis, and (iii) machine learning-based big data analysis. The applications to two different 4D material characterization demonstrate the advantages of these proposed tomographic imaging techniques and provide quantitative insights into the global evolution of damage/defect beyond qualitative human observation. / Doctor of Philosophy / X-ray tomography is a nondestructive imaging technique to visualize interior structures of non-transparent objects, which has been widely applied to resolve implicit 3D structures, such as human organs and tissues for clinical diagnosis, contents of baggage for security check, internal defect evolution during additive manufacturing, observing fracturing accompanying mechanical tests, and etc. Multiple planar measurements with sufficient X-ray exposure time among different angles are desirable to reconstruct the unique high-quality 3D internal distribution. However, there are practical concerns about X-ray radiation damage to biology samples or long-time acquisition for dynamic experiments in real-life applications. Insufficient measurements by reducing the number of total measurements or the time for each measurement, are proposed to solve this problem but doing so usually leads to the sacrifice of the reconstruction quality. Computational algorithms are developed for tomographic imaging under these insufficient measurement conditions to obtain reconstructions with improved quality. Deep learning has been successfully applied to numerous areas, such as in recognizing speech, translating languages, detecting objects, and etc. It has also been applied to X-ray tomographic imaging to improve the reconstruction results by learning the features through thousands to millions of corrupted and ideal reconstruction pairs. The aim of this thesis to design efficient deep learning-based algorithms with the help of physical and measurement priors to reduce the number of training datasets. We propose two different deep learning-based tomographic imaging techniques to improve reconstruction results with reduced training data under different insufficient measurement conditions. One way is to incorporate prior knowledge of the physics models to reduce the required amount of ground truth data, from thousands to hundreds. The training data requirement is further simplified with another hybrid measurement strategy, which could be implemented on each individual sample with only several high-quality measurements. In the end, we apply these two proposed algorithms to different dynamic damage/defect behavior characterization applications. Our methods achieve improved reconstruction results with greatly enhanced experimental speeds, which become suitable for dynamic 3D recording. Final results demonstrate the advantages of the proposed tomographic imaging techniques and provide quantitative insights into the global dynamic evolution inside the material. This quantitative analysis also provides a much more comprehensive understanding than qualitative human observation.

This research develops and validates computational tools for the design and analysis of structural components and systems constructed with Ultra-High Performance Concrete (UHPC). The modeling strategy utilizes the Lattice Discrete Particle Model (LDPM) to represent UHPC material and structural member response, and extends a structural-level triaxial continuum constitutive law to account for the addition of discrete fibers. The approach is robust, general, and could be utilized by other researchers to expand the computational capability and simulate the behavior of different composite materials. The work described herein identifies the model material parameters by conducting a complete material characterization for UHPC, with and without fiber reinforcement, describing its behavior in unconfined compression, uniaxial tension, and fracture toughness. It characterizes the effect of fiber orientations, fiber-matrix interaction, and resolves the issue of multi-axial stress states on fiber pullout. The capabilities of the computational models are demonstrated by comparing the material test data that were not used in the parameter identification phase to numerical simulations to validate the models' predictive capabilities. These models offer a mechanics-based shortcut to UHPC analysis that can strategically support ongoing development of material and structural design codes and standards. / Ph. D.

Portland cement concrete (PCC) structures deteriorate with age and need to be maintained or replaced. Early detection of deterioration in PCC (e.g., alkali-silica reaction, freeze/thaw damage, or chloride presence) can lead to significant reductions in maintenance costs. However, it is often too late to perform low-cost preventative maintenance by the time deterioration becomes evident. Non-destructive evaluation (NDE) methods are potentially among the most useful techniques developed for assessing constructed facilities. They are noninvasive and can be performed rapidly. Portland cement concrete can be nondestructively evaluated by electrically characterizing its complex dielectric constant. The real part of the dielectric constant depicts the velocity of electromagnetic waves in PCC. The imaginary part describes the conductivity of PCC and the attenuation of electromagnetic waves, and hence the losses within the PCC media. Dielectric properties of PCC have been investigated in a laboratory setting using a parallel plate capacitor operating in the frequency range of 0.1MHz to about 40MHz. This capacitor set-up consists of two horizontal-parallel plates with an adjustable separation for insertion of a dielectric specimen (PCC). While useful in research, this approach is not practical for field implementation In this research, a capacitance probe has been developed for field application. The probe consists of two planar conducting plates and is made of flexible materials for placement on exposed surfaces of the specimens to be tested. The calibration method of both capacitive systems has been extensively studied to minimize systematic errors in the measurement process. These two measurement systems will be discussed and compared to one another on the basis of sensitivity and measurement repeatability. / Master of Science