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.

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.

Advanced Nanostructured Electro-Catalysts for Electricity Generation and Biorenewable Alcohol Conversion

In my Ph.D research, a wet chemistry-based organic solution phase reduction method was developed, and was successfully applied in the preparation of a series of advanced electro-catalysts, including 0-dimensional (0-D) Pt, Pd, Au, and Pd-Ni nanoparticles (NPs), 1-D Pt-Fe nanowires (NWs) and 2-D Pd-Fe nanoleaves (NLs), with controlled size, shape, and morphology. These nanostructured catalysts have demonstrated unique electro-catalytic functions towards electricity production and biorenewable alcohol conversion. The molecular oxygen reduction reaction (ORR) is a long-standing scientific issue for fuel cells due to its sluggish kinetics and the poor catalyst durability. The activity and durability of an electro-catalyst is strongly related with its composition and structure. Based on this point, Pt-Fe NWs with a diameter of 2 - 3 nm were accurately prepared. They have demonstrated a high durability in sulfuric acid due to its 1-D structure, as well as a high ORR activity attributed to its tuned electronic structure. By substituting Pt with Pd using a similar synthesis route, Pd-Fe NLs were prepared and demonstrated a higher ORR activity than Pt and Pd NPs catalysts in the alkaline electrolyte. Recently, biomass-derived alcohols have attracted enormous attention as promising fuels (to replace H2) for low-temperature fuel cells. From this point of view, Pd-Ni NPs were prepared and demonstrated a high electro-catalytic activity towards ethanol oxidation. Comparing to ethanol, the biodiesel waste glycerol is more promising due to its low price and high reactivity. Glycerol (and crude glycerol) was successfully applied as the fuel in an Au-anode anion-exchange membrane fuel cell (AEMFC). By replacing Au with a more active Pt catalyst, simultaneous generation of both high power-density electricity and value-added chemicals (glycerate, tartronate, and mesoxalate) from glycerol was achieved in an AEMFC. To investigate the production of valuable chemicals from glycerol electro-oxidation, two anion-exchange membrane electro-catalytic reactors were designed. The research shows that the electro-oxidation product distribution is strongly dependent on the anode applied potential. Reaction pathways for the electro-oxidation of glycerol on Au/C catalyst have been elucidated: continuous oxidation of OH groups (to produce tartronate and mesoxalate) is predominant at lower potentials, while C-C cleavage (to produce glycolate) is the dominant reaction path at higher potentials.

PROPERTIES AND STRUCTURES OF Li-N BASED HYDROGEN STORAGE MATERIALS

Traditional transportation fuel, petroleum, is limited and nonrenewable, and it also causes pollutions. Hydrogen is considered one of the best alternative fuels for transportation. The key issue for using hydrogen as fuel for transportation is hydrogen storage. Lithium nitride (Li3N) is an important material which can be used for hydrogen storage. The decompositions of lithium amide (LiNH2) and lithium imide (Li2NH) are important steps for hydrogen storage in Li3N. The effect of anions (e.g. Cl-) on the decomposition of LiNH2 has never been studied. Li3N can react with LiBr to form lithium nitride bromide Li13N4Br which has been proposed as solid electrolyte for batteries. The decompositions of LiNH2 and Li2NH with and without promoter were investigated by using temperature programmed decomposition (TPD) and X-ray diffraction (XRD) techniques. It was found that the decomposition of LiNH2 produced Li2NH and NH3 via two steps: LiNH2 into a stable intermediate species (Li1.5NH1.5) and then into Li2NH. The decomposition of Li2NH produced Li, N2 and H2 via two steps: Li2NH into an intermediate species --- Li4NH and then into Li. The kinetic analysis of Li2NH decomposition showed that the activation energies are 533.6 kJ/mol for the first step and 754.2 kJ/mol for the second step. Furthermore, XRD demonstrated that the Li4NH, which was generated in the decomposition of Li2NH, formed a solid solution with Li2NH. In the solid solution, Li4NH possesses a similar cubic structure as Li2NH. The lattice parameter of the cubic Li4NH is 0.5033nm. The decompositions of LiNH2 and Li2NH can be promoted by chloride ion (Cl-). The introduction of Cl- into LiNH2 resulted in the generation of a new NH3 peak at low temperature of 250 °C besides the original NH3 peak at 330 °C in TPD profiles. Furthermore, Cl- can decrease the decomposition temperature of Li2NH by about 110 °C. The degradation of Li3N was systematically investigated with techniques of XRD, Fourier transform infrared (FT-IR) spectroscopy, and UV-visible spectroscopy. It was found that O2 could not affect Li3N at room temperature. However, H2O in air can cause the degradation of Li3N due to the reaction between H2O and Li3N to LiOH. The produced LiOH can further react with CO2 in air to Li2CO3 at room temperature. Furthermore, it was revealed that Alfa-Li3N is more stable in air than Beta-Li3N. The chemical stability of Li13N4Br in air has been investigated by XRD, TPD-MS, and UV-vis absorption as a function of time. The aging process finally leads to the degradation of the Li13N4Br into Li2CO3, lithium bromite (LiBrO2) and the release of gaseous NH3. The reaction order n = 2.43 is the best fitting for the Li13N4Br degradation in air reaction. Li13N4Br energy gap was calculated to be 2.61 eV.