Plasma removal of Parylene C

Parylene C, an emerging material in microelectromechanical systems, is of particular interest in biomedical and lab-on-a-chip applications where stable, chemically inert surfaces are desired. Practical implementation of Parylene C as a structural material requires the development of micropatterning techniques for its selective removal. Dry etching methods are currently the most suitable for batch processing of Parylene structures. A performance comparison of three different modes of Parylene C plasma etching was conducted using oxygen as the primary reactive species. Plasma, reactive ion and deep reactive ion etching techniques were explored. In addition, a new switched chemistry process with alternating cycles of fluoropolymer deposition and oxygen plasma etching was examined to produce structures with vertical sidewalls. Vertical etch rates, lateral etch rates, anisotropy and sidewall angles were characterized for each of the methods. This detailed characterization was enabled by the application of replica casting to obtain cross sections of etched structures in a non-destructive manner. Application of the developed etch recipes to the fabrication of complex Parylene C microstructures is also discussed

B-meson Semi-inclusive Decay to 2−+2^{-+} Charmonium in NRQCD and X(3872)

The semi-inclusive B-meson decay into spin-singlet D-wave $2^{-+}$ charmonium, $B\to \eta_{c2}+X$, is studied in nonrelativistic QCD (NRQCD). Both color-singlet and color-octet contributions are calculated at next-to-leading order (NLO) in the strong coupling constant $\alpha_s$. The non-perturbative long-distance matrix elements are evaluated using operator evolution equations. It is found that the color-singlet $^1D_2$ contribution is tiny, while the color-octet channels make dominant contributions. The estimated branching ratio $B(B\to \eta_{c2}+X)$ is about $0.41\,\times10^{-4}$ in the Naive Dimensional Regularization (NDR) scheme and $1.24\,\times10^{-4}$ in the t'Hooft-Veltman (HV) scheme, with renormalization scale $\mu=m_b=4.8$\,GeV. The scheme-sensitivity of these numerical results is due to cancelation between ${}^1S_0^{[8]}$ and ${}^1P_1^{[8]}$ contributions. The $\mu$-dependence curves of NLO branching ratios in both schemes are also shown, with $\mu$ varying from $\frac{m_b}{2}$ to $2m_b$ and the NRQCD factorization or renormalization scale $\mu_{\Lambda}$ taken to be $2m_c$. Comparison of the estimated branching ratio of $B\to \eta_{c2}+X$ with the observed branching ratio of $B \to X(3872)+K$ may lead to the conclusion that X(3872) is unlikely to be the $2^{-+}$ charmonium state $\eta_{c2}$.Comment: Version published in PRD, references added, 26 pages, 9 figure

The M-Scale Model: A Multi-Scale Model for Decision Support of On-Site Remediation.

Remedial decisions for sediment management involve knowledge of the biogeochemical processes affecting contaminant fate and transport in the sediment, as well as the spatial distribution of the contaminant. Spatial statistical models provide estimates of the spatial distribution, and the results depend on validity of the assumptions inherent to the selected statistical tools, and the appropriateness of these tools with respect to the objectives of the estimation. Most decision tools for site assessment depend on spatial estimation/simulation that either misinterpret the extent of exceedance, or assigns a single decision map corresponding to a given uncertainty criterion. The specific objectives of this work are (i) to provide a spatial statistical approach, the M-Scale model, for characterization of the spatial structure and spatial distribution of an attribute, such as contaminant concentration or microbiological parameters; (ii) to investigate the applicability of the developed model to field data relevant to contaminated sediments using various performance diagnostics; and (iii) to explore the sensitivity of the M-Scale model and other methods to the nugget effect (artificially induced error and micro-scale variability) using laboratory and field data from the Anacostia River (NJ). Results using artificial data indicate the developed model generates estimates that (i) reproduce spatial variability evident in the sample, with reasonable precision for classifying exceedance/non-exceedance of a design threshold, and (ii) reproduce the overall attribute value distribution. Cross-validation results using datasets from the Passaic River yield similar performance metrics for the M-Scale model relative to CK in the reproduction of the overall value distribution, and relative to OK in the precision of classification. Estimation results using samples at both the site-scale and the micro-scale from the Anacostia River further indicate the possibility of reducing the uncertainty associated with estimates by characterizing the actual micro-scale variability. Cross-validation results using the same datasets indicate that each data point in a small-size sample set is essential in the estimation process. The reproduction of spatial variability demonstrated in this dissertation indicates improvement of spatial estimation by characterizing multi-scale covariances of means. The model has broad applicability for situations where multi-scale characterization issues drive spatial management decisions.Ph.D.Environmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58473/1/mengyl_1.pd

Single deep ultraviolet light emission from boron nitride nanotube film

Light in deep ultraviolet DUV region has a wide range of applications and the demand for finding DUV light emitting materials at nanoscale is increasingly urgent as they are vital for building miniaturized optic and optoelectronic devices. We discover that boron nitride nanotubes BNNTs with a well-crystallized cylindrical multiwall structure and diameters smaller than 10 nm can have single DUV emission at 225 nm 5.51 eV. The measured BNNTs are grown on substrate in the form of a thin film. This study suggests that BNNTs may work as nanosized DUV light sources for various applications. © 20

Spindle oscillations are generated in the dorsal thalamus and modulated by the thalamic reticular nucleus

Spindle waves occur during the early stage of slow wave sleep and are thought to arise in the thalamic reticular nucleus (TRN), causing inhibitory postsynaptic potential spindle-like oscillations in the dorsal thalamus that are propagated to the cortex. We have found that thalamocortical neurons exhibit membrane oscillations that have spindle frequencies, consist of excitatory postsynaptic potentials, and co-occur with electroencephalographic spindles. TRN lesioning prolonged oscillations in the medial geniculate body (MGB) and auditory cortex (AC). Injection of GABA~A~ antagonist into the MGB decreased oscillation frequency, while injection of GABA~B~ antagonist increased spindle oscillations in the MGB and cortex. Thus, spindles originate in the dorsal thalamus and TRN inhibitory inputs modulate this process, with fast inhibition facilitating the internal frequency and slow inhibition limiting spindle occurrence

Static Quark Potential and the Renormalized Anisotropy on Tadpole Improved Anisotropic Lattices

Static quark potential is studied using a tadpole improved gauge lattice action. The scale is set using the potential for a wide range of bare parameters. The renormalized anisotropy of the lattice is also measured.Comment: 11 pages, 5 figures, accepted for publication in Int. J. Mod. Phys.

Graph Few-shot Learning via Knowledge Transfer

Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating information of its neighbors. However, most GNNs have shallow layers with a limited receptive field and may not achieve satisfactory performance especially when the number of labeled nodes is quite small. To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph. Specifically, a transferable metric space characterized by a node embedding and a graph-specific prototype embedding function is shared between auxiliary graphs and the target, facilitating the transfer of structural knowledge. Extensive experiments and ablation studies on four real-world graph datasets demonstrate the effectiveness of our proposed model.Comment: Full paper (with Appendix) of AAAI 202

Density-dependent deformed relativistic Hartree-Bogoliubov theory in continuum

The deformed relativistic Hartree-Bogoliubov theory in continuum with the density-dependent meson-nucleon couplings is developed. The formulism is briefly presented with the emphasis on handling the density-dependent couplings, meson fields, and potentials in axially deformed system with partial wave method. Taking the neutron-rich nucleus $^{38}$Mg as an example, the newly developed code is verified by the spherical relativistic continuum Hartree-Bogoliubov calculations, where only the spherical components of the densities are considered. When the deformation is included self-consistently, it is shown that the spherical components of density-dependent coupling strengths are dominant, while the contributions from low-order deformed components are not negligible.Comment: 5 pages, 3 figures, and 1 tabl