Methane oxidation activity and nanoscale characterization of Pd/CeO2 catalysts prepared by dry milling Pd acetate and ceria
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Cell wall composition influences biomass use as a forage and as a feedstock for biofuel and chemical conversion. To examine the influence of environment on composition of switchgrass (Panicum virgatum L.), we utilized a multi-environment experiment consisting of clones of switchgrass genotypes grown at up to ten locations in the continental US. We tested the influence of different milling treatments on biomass composition trait predictions via near-infrared reflectance spectroscopy (NIRS). We found that most compositional trait predictions (29/34) were significantly different (P < 0.05) when a single lot of biomass was subjected to disparate milling treatments, i.e., knife milling vs. knife milling with an additional cyclone milling. Further, depending on the plant material tested, three to eight compositional trait predictions vary (P < 0.05) when identical biomass was knife milled at different sites followed by cyclone milling at a single site, including for traits such as Klason lignin, nitrogen, and carbon. In some cases, variation due to milling site exceeded environmentally induced compositional variation of a single switchgrass genotype grown at different sites. From these observations, we recommend a protocol with two sequential millings that decouples growth environment from a particular mill. Utilizing this approach, we found that 46/46 biomass composition traits from the warm season herbaceous forage and switchgrass bioethanol NIRS equations vary significantly (P < 0.001) in clones of a switchgrass genotype (WBC) grown at ten sites, with the growth site representing the largest average source of variation (41%). This multi-site milling approach can be used to examine environmental and gene-by-environment influences on composition with the goal of optimizing cell wall composition in different environments for biomass utilization.
Cellulose is typically characterized as a mixture of crystalline and amorphous material. A large part of the general understanding of the chemical, biochemical and physical properties of cellulosic materials is thought to depend on the consequences of the ratio of these components. For example, amorphous materials are said to be more reactive and have less tensile strength but comprehensive understanding and definitive analysis remain elusive. Ball milling has been used for decades to increase the ratio of amorphous material. The present work used 13 techniques to follow the changes in cotton fibers (nearly pure cellulose) after ball milling for 15, 45 and 120 min. X-ray diffraction results were analyzed with the Rietveld method; DNP (dynamic nuclear polarization) natural abundance 2D NMR studies in the next paper in this issue assisted with the interpretation of the 1D analyses in the present work. A conventional NMR model’s paracrystalline and inaccessible crystallite surfaces were not needed in the model used for the DNP studies. Sum frequency generation (SFG) spectroscopy also showed profound changes as the cellulose was decrystallized. Optical microscopy and field emission-scanning electron microscopy results showed the changes in particle size; molecular weight and carbonyl group analyses by gel permeation chromatography confirmed chemical changes. Specific surface areas and pore sizes increased. Fourier transform infrared (FTIR) and Raman spectroscopy also indicated progressive changes; some proposed indicators of crystallinity for FTIR were not in good agreement with our results. Thermogravimetric analysis results indicated progressive increase in initial moisture content and some loss in stability. Although understanding of structural changes as cellulose is amorphized by ball milling is increased by this work, continued effort is needed to improve agreement between the synchrotron and laboratory X-ray methods used herein and to provide physical interpretation of the SFG results.
A workable analytical abrasion model that relates critical knife-mill process parameters (geometry and rotational speed) to critical material attributes of inorganic mineral species in feedstock (density, size, and aspect ratio) and substrate (hardness and elastic modulus) was formulated to model wear of knives in knife-milling systems. Results of the model were compared to experimental observations of the edge recession of knives used in a knife mill marketed by Eberbach. Results showed good agreement between the predicted and measured shape of a worn knife and showed that a quality-by-design approach can be developed to predict component reliability based on scientific engineering principles in lieu of trial-and-error approaches.
This paper describes automated identification of the milling stability boundary using Bayesian machine learning and experiments. The Bayesian machine learning process begins with the user’s initial beliefs about milling stability. This “prior” is a distribution that uses all available information, which may be based only on experience or may be informed by physics-based model predictions. Experiments are then completed to update this prior by calculating the “posterior,” a modified probabilistic description of the milling stability limit based on the new information. The approach is demonstrated and results are presented for both numerical and experimental cases.
We find exact multi-instanton solutions to the self-dual Yang–Mills equation on a large class of curved spaces with SO(3) isometry, generalizing the results previously found on R 4 . Here, the solutions are featured with explicit multi-centered expressions and topological properties. As examples, we demonstrate the approach on several different curved spaces, including the Einstein static universe and R × dS$^{E}_{3}$, and show that the exact multi-instanton solutions exist on these curved backgrounds.
This paper describes a physics-guided Bayesian framework for identifying the milling stability boundary and system parameters through iterative testing. Prior uncertainties for the parameters are identified through physical simulation and literature reviews, without physical testing of the actual milling system. Those uncertainties are then propagated to the stability map using a physics-based stability model, which is used to suggest a test point. The uncertainties are updated based on the new information acquired from the cutting test to form a new probability distribution, called the posterior. Finally, the posterior are compared to measured values for the stability boundary and system parameters to evaluate the approach. Based on experimental observations, the advantages and disadvantages of using a physics-guided model are discussed.
A transient color flux across null infinity in classical Yang-Mills theory is considered here. It is shown that a pair of test `quarks' initially in a color singlet generically acquire net color as a result of the flux. A nonlinear formula is derived for the relative color rotation of the quarks. For weak color flux the formula linearizes to the Fourier transform of the soft gluon theorem. Furthermore, this color memory effect is the Yang-Mills analog of the gravitational memory effect.
We introduce a web of strongly correlated interacting 3+1D topological superconductors/insulators of 10 particular global symmetry groups of Cartan classes, realizable in electronic condensed matter systems, and their new SU(N) generalizations. The symmetries include SU(N), SU(2), U(1), fermion parity, time reversal and relate to each other through symmetry embeddings. We overview the lattice Hamiltonian formalism. We complete the list of field theories of bulk symmetry-protected topological invariants (SPT invariants/partition functions that exhibit boundary ’t Hooft anomalies) via cobordism calculations, matching their full classification. We also present explicit 4-manifolds that detect these SPTs. On the other hand, once we dynamically gauge part of their global symmetries, we arrive in various new phases of SU(N) Yang–Mills (YM) gauge theories, analogous to quantum spin liquids with emergent gauge fields. We discuss how coupling YM theories to time reversal-SPTs affects the strongly coupled theories at low energy. For example, we point out a possibility of having two deconfined gapless time-reversal symmetric SU(2) YM theories at θ=π as two distinct conformal field theories, which although are secretly indistinguishable by correlators of local operators on orientable spacetimes nor by gapped SPT states, can be distinguished on non-orientable spacetimes or potentially by correlators of extended operators.
The pyrolysis of lignocellulosic materials is a promising technique to produce fuels and chemicals. It is well known that the most abundant products of lignin pyrolysis are oligomeric molecules, known as pyrolytic lignin (PL). The chemical composition of PL has been extensively studied; however, there is still an important debate whether these oligomers are produced directly from the lignin or from the recombination of monomeric pyrolytic products. Existing theories are unable to describe the effect of vacuum on the distribution of pyrolysis products. Hybrid poplar milled wood lignin (MWL) was initially isolated and thoroughly characterized by Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS). Chemical formulas were assigned to each oligomeric compound detected. The MWL was also subjected to vacuum pyrolysis in a modified pyroprobe at 250, 750, and 1000 mbar (absolute pressure), and the resulting liquid products were analyzed by FT-ICR MS. A new strategy to assign structural representations to the oligomeric PL products is proposed, based on the plausible pyrolysis reaction mechanisms of depolymerization/fragmentation applied to original MWL oligomer formulas. Our results support the hypothesis that PL is formed from the removal of moieties from primary lignin pyrolysis products with between three and five aromatic rings. This depolymerization/fragmentation allows the oligomers to reduce their molecular weights to the point where they can be removed from the reaction zone by direct vaporization. Furthermore, this phenomenon highlights the importance of pressure on removal mechanisms and their impact on the molecular weight of the resulting products from lignin pyrolysis.
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The breakdown of solid metal into powder during the hydride-dehydride process is commercially important for the formation of titanium and other metal powders. Typically, the milling of the brittle hydride powder occurs in a ball mill, where milling media impacts powder particles to break them down. The milling media can impact particles that are larger than desired, as desired, or smaller than desired, indiscriminately making all particles smaller. In this work, we investigate how to minimize waste powder production during milling using two different milling methods, planetary ball milling and milling in a sieve shaker (sieve-milling). Both processes yielded similar amounts of 20–75 μm diameter powder (the target size range); however, sieve-milling generated a significantly smaller amount of undersized waste powder. The powders were characterized by X-ray diffraction, SEM/STEM, and magnetic susceptibility. Several differences between ball milling and sieve-milling processes are discussed. We then conclude that the decreased yield of undersized powder in sieve-milling was due to a combination of lower impact energy in sieve-milling, unreacted metallic cores in the hydride flakes, and the ability to mill target particle sizes during sieve-milling. While these results are from the milling of brittle hydride powder, similar methods may be applicable to other brittle powders, including ceramics or salts.