Underoil Superhydrophilic Metal Felt Fabricated by Modifying Ultrathin Fumed Silica Coatings for the Separation of Water-in-Oil Emulsions

Although various superhydrophobic/superoleophilic porous materials have been developed and successfully applied to separate water-in-oil emulsions through the size-sieving mechanism, the separation performance is restricted by their nanoscale pore size severely. In this study, the wettability of underoil water on fumed silica was experimentally observed, and the underlying mechanism was investigated by carrying out theoretical analysis and molecular dynamic (MD) simulations. Further, we present a novel, facile, and an inexpensive technique to fabricate an underoil superhydrophilic metal felt with microscale pores for the separation of water-in-oil emulsions using SiO2 nanoparticles (NPs) as building blocks. The as-prepared underoil superhydrophilic coating is closed-packed and ultrathin (the thickness is approximately hundreds of nanometers), as well as capable of being coated on a metal felt with complex structures without blocking its pores. The as-prepared metal felt could adsorb water droplets directly from oil, which endowed it with the ability to separate both surfactant-free and surfactant-stabilized water-in-oil emulsions with high separation efficiency up to 99.7% even though its pore size is larger than that of the emulsified droplet. The filtration flux for the separation of span 80-stabilized emulsion is up to ∼4000 L·m–2·h–1. Its separation performance is better than most of the other traditional membranes and superwettable materials used for the separation of water-in-oil emulsions. Moreover, the as-prepared metal felt retained outstanding separation performance even after 30 cycles of use, which demonstrated its excellent reusability and durability. Additionally, the distinctive wettability of underoil superhydrophilicity endued coated metal felt with superior antifouling properties toward crude oil. Overall, this study not only provides a new perspective on separating water-in-oil emulsions but also gives a universal approach to develop unique wettability surfaces

Hadronic Mass Moments in Inclusive Semileptonic B Meson Decays

We have measured the first and second moments of the hadronic mass-squared distribution in B -> X_c l nu, for P(lepton) > 1.5 GeV/c. We find <M_X^2 - M_D[Bar]^2> = 0.251 +- 0.066 GeV^2, )^2 > = 0.576 +- 0.170 GeV^4, where M_D[Bar] is the spin-averaged D meson mass. From that first moment and the first moment of the photon energy spectrum in b -> s gamma, we find the HQET parameter lambda_1 (MS[Bar], to order 1/M^3 and beta_0 alpha_s^2) to be -0.24 +- 0.11 GeV^2. Using these first moments and the B semileptonic width, and assuming parton-hadron duality, we obtain |V_cb| = 0.0404 +- 0.0013.Comment: 11 pages postscript, also available through http://w4.lns.cornell.edu/public/CLNS, submitted to PR

Observation of the Ωc0\Omega_{c}^{0} Charmed Baryon at CLEO

The CLEO experiment at the CESR collider has used 13.7 fb$^{-1}$ of data to search for the production of the $\Omega_c^0$ (css-ground state) in $e^{+}e^{-}$ collisions at $\sqrt{s} \simeq 10.6$ {\rm GeV}. The modes used to study the $\Omega_c^0$ are $\Omega^- \pi^+$, $\Omega^- \pi^+ \pi^0$, $\Xi^- K^- pi^+ \pi^+$, $\Xi^0 K^- pi^+$, and $\Omega^- \pi^+ \pi^- \pi^+$. We observe a signal of 40.4$\pm$9.0(stat) events at a mass of 2694.6$\pm$2.6(stat)$\pm$1.9(syst) {\rm MeV/$c^2$}, for all modes combined.Comment: 10 pages postscript, also available through http://w4.lns.cornell.edu/public/CLN

Evidence of New States Decaying into Ξc′π\Xi^{\prime}_{c}\pi

Using 13.7 $fb^{-1}$ of data recorded by the CLEO detector at CESR, we report evidence for two new charmed baryons: one decaying into $\Xi_c^{0 \prime}\pi^+$ with the subsequent decay $\Xi_c^{0 \prime} \to \Xi_c^0 \gamma$, and its isospin partner decaying into $\Xi_c^{+ \prime} \pi^-$ followed by $\Xi_c^{+\prime} \to \Xi_c^+\gamma$. We measure the following mass differences for the two states: $M(\Xi_c^0 \gamma \pi^+)-M(\Xi_c^0)$=318.2+-1.3+-2.9 MeV, and $M(\Xi_c^+ \gamma \pi^-)-M(\Xi_c^+)$=324.0+-1.3+-3.0 MeV. We interpret these new states as the $J^P = 1/2^- \Xi_{c1}$ particles, the charmed-strange analogs of the $\Lambda_{c1}^+(2593)$.Comment: 10 pages postscript, also available through http://w4.lns.cornell.edu/public/CLN

Measurement of the Relative Branching Fraction of Υ(4S)\Upsilon(4S) to Charged and Neutral B-Meson Pairs

We analyze 9.7 x 10^6 B\bar{B}$ pairs recorded with the CLEO detector to determine the production ratio of charged to neutral B-meson pairs produced at the Y(4S) resonance. We measure the rates for B^0 -> J/psi K^{(*)0} and B^+ -> J/psi K^{(*)+} decays and use the world-average B-meson lifetime ratio to extract the relative widths f+-/f00 = Gamma(Y(4S) -> B+B-)/Gamma(Y(4S) -> B0\bar{B0}) = = 1.04 +/- 0.07(stat) +/- 0.04(syst). With the assumption that f+- + f00 = 1, we obtain f00 = 0.49 +/- 0.02(stat) +/- 0.01(syst) and f+- = 0.51 +/- 0.02(stat) +/- 0.01(syst). This production ratio and its uncertainty apply to all exclusive B-meson branching fractions measured at the Y(4S) resonance.Comment: 11 pages postscript, also available through http://w4.lns.cornell.edu/public/CLN

First Observation of the Decays B0→D∗−ppˉπ+B^{0}\to D^{*-}p\bar{p}\pi^{+} and B^{0}\to D^{*-}p\bar{n}$

We report the first observation of exclusive decays of the type B to D^* N anti-N X, where N is a nucleon. Using a sample of 9.7 times 10^{6} B-Bbar pairs collected with the CLEO detector operating at the Cornell Electron Storage Ring, we measure the branching fractions B(B^0 \to D^{*-} proton antiproton \pi^+) = ({6.5}^{+1.3}_{-1.2} +- 1.0) \times 10^{-4} and B(B^0 \to D^{*-} proton antineutron) = ({14.5}^{+3.4}_{-3.0} +- 2.7) times 10^{-4}. Antineutrons are identified by their annihilation in the CsI electromagnetic calorimeter.Comment: 9 pages postscript, also available through http://w4.lns.cornell.edu/public/CLN

A Search for B→τνB\to \tau\nu

We report results of a search for $B\to\tau\nu$ in a sample of 9.7 million charged $B$ meson decays. The search uses both $\pi\nu$ and $\ell\nu\bar\nu$ decay modes of the $\tau$, and demands exclusive reconstruction of the companion $\bar B$ decay to suppress background. We set an upper limit on the branching fraction ${\cal B}(B\to \tau\nu) < 8.4\times 10^{-4}$ at 90% confidence level. With slight modification to the analysis we also establish ${\cal B}(B^\pm\to K^\pm\nu\bar\nu) < 2.4\times 10^{-4}$ at 90% confidence level.Comment: 10 ages postscript, also available through http://w4.lns.cornell.edu/public/CLN

Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm

<p>Abstract</p> <p>Background</p> <p>Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space.</p> <p>Results</p> <p>We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (<it>Plasmodium chabaudi</it>), systemic acquired resistance in <it>Arabidopsis thaliana</it>, similarities and differences between inner and outer cotyledon in <it>Brassica napus </it>during seed development, and to <it>Brassica napus </it>whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples.</p> <p>Conclusions</p> <p>Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.</p