A statistical method to estimate low-energy hadronic cross sections

In this article we propose a model based on the Statistical Bootstrap approach to estimate the cross sections of different hadronic reactions up to a few GeV in c.m.s energy. The method is based on the idea, when two particles collide a so called fireball is formed, which after a short time period decays statistically into a specific final state. To calculate the probabilities we use a phase space description extended with quark combinatorial factors and the possibility of more than one fireball formation. In a few simple cases the probability of a specific final state can be calculated analytically, where we show that the model is able to reproduce the ratios of the considered cross sections. We also show that the model is able to describe proton\,-\,antiproton annihilation at rest. In the latter case we used a numerical method to calculate the more complicated final state probabilities. Additionally, we examined the formation of strange and charmed mesons as well, where we used existing data to fit the relevant model parameters.Comment: 12 pages, 12 figures, submitted to EPJ

Visualization 1: Two-stage optical recording: photoinduced birefringence and surface-mediated bits storage in bisazo-containing copolymers towards ultrahigh data memory

Readout of multi-level bits by changing the reading beam polarization. The bits intensities smoothly transit from one state (bright or dack) to the other (dack or bright). Originally published in Optics Express on 03 October 2016 (oe-24-20-23557

Relationships between Antibiotics and Antibiotic Resistance Gene Levels in Municipal Solid Waste Leachates in Shanghai, China

Many studies have quantified antibiotics and antibiotic resistance gene (ARG) levels in soils, surface waters, and waste treatment plants (WTPs). However, similar work on municipal solid waste (MSW) landfill leachates is limited, which is concerning because antibiotics disposal is often in the MSW stream. Here we quantified 20 sulfonamide (SA), quinolone (FQ), tetracycline (TC), macrolide (ML), and chloramphenicol (CP) antibiotics, and six ARGs (<i>sul1</i>, <i>sul2</i>, <i>tetQ</i>, <i>tetM</i>, <i>ermB</i>, and <i>mefA</i>) in MSW leachates from two Shanghai transfer stations (TS; sites Hulin (HL) and Xupu (XP)) and one landfill reservoir (LR) in April and July 2014. Antibiotic levels were higher in TS than LR leachates (985 ± 1965 ng/L vs 345 ± 932 ng/L, n = 40), which was because of very high levels in the HL leachates (averaging at 1676 ± 5175 ng/L, <i>n</i> = 40). The mean MLs (3561 ± 8377 ng/L, <i>n</i> = 12), FQs (975 ± 1608 ng/L, <i>n</i> = 24), and SAs (402 ± 704 ng/L, <i>n</i> = 42) classes of antibiotics were highest across all samples. ARGs were detected in all leachate samples with normalized <i>sul2</i> and <i>ermB</i> levels being especially elevated (−1.37 ± 1.2 and −1.76 ± 1.6 log (copies/16S-rDNA), respectively). However, ARG abundances did not correlate with detected antibiotic levels, except for <i>tetW</i> and <i>tetQ</i> with TC levels (<i>r</i> = 0.88 and 0.81, respectively). In contrast, most measured ARGs did significantly correlate with heavy metal levels (<i>p</i> < 0.05), especially with Cd and Cr. This study shows high levels of ARGs and antibiotics can prevail in MSW leachates and landfills may be an underappreciated as a source of antibiotics and ARGs to the environment

Quantum Sieving in Metal–Organic Frameworks: A Computational Study

In this work, a systematic computational study was performed to investigate the quantum sieving in nine typical metal–organic frameworks (MOFs) for the separation of hydrogen isotope mixtures. The results show that Cu(F-pymo)₂ and CPL-1 exhibit exceptional selectivity that is higher than other MOFs as well as other nanoporous materials such as carbon nanotubes, slit-shaped graphites, and zeolites studied so far. A concept named “quantum effective pore size” (QEPS) was proposed in this work, which can incorporate the effects of quantum sieving, and thus is temperature-dependent. On the basis of the new pore size, good correlations between pore size and selectivity can be established for the MOFs considered; particularly, they can explain the different selectivity performance of the two MOFs with highest selectivity at 40 and 77 K. This work indicates that MOFs are suitable candidates for the separation of hydrogen isotopes through quantum sieving

Quantum Sieving in Metal–Organic Frameworks: A Computational Study

In this work, a systematic computational study was performed to investigate the quantum sieving in nine typical metal–organic frameworks (MOFs) for the separation of hydrogen isotope mixtures. The results show that Cu(F-pymo)₂ and CPL-1 exhibit exceptional selectivity that is higher than other MOFs as well as other nanoporous materials such as carbon nanotubes, slit-shaped graphites, and zeolites studied so far. A concept named “quantum effective pore size” (QEPS) was proposed in this work, which can incorporate the effects of quantum sieving, and thus is temperature-dependent. On the basis of the new pore size, good correlations between pore size and selectivity can be established for the MOFs considered; particularly, they can explain the different selectivity performance of the two MOFs with highest selectivity at 40 and 77 K. This work indicates that MOFs are suitable candidates for the separation of hydrogen isotopes through quantum sieving

Quantum Sieving in Metal–Organic Frameworks: A Computational Study

In this work, a systematic computational study was performed to investigate the quantum sieving in nine typical metal–organic frameworks (MOFs) for the separation of hydrogen isotope mixtures. The results show that Cu(F-pymo)₂ and CPL-1 exhibit exceptional selectivity that is higher than other MOFs as well as other nanoporous materials such as carbon nanotubes, slit-shaped graphites, and zeolites studied so far. A concept named “quantum effective pore size” (QEPS) was proposed in this work, which can incorporate the effects of quantum sieving, and thus is temperature-dependent. On the basis of the new pore size, good correlations between pore size and selectivity can be established for the MOFs considered; particularly, they can explain the different selectivity performance of the two MOFs with highest selectivity at 40 and 77 K. This work indicates that MOFs are suitable candidates for the separation of hydrogen isotopes through quantum sieving

Quantum Sieving in Metal–Organic Frameworks: A Computational Study

In this work, a systematic computational study was performed to investigate the quantum sieving in nine typical metal–organic frameworks (MOFs) for the separation of hydrogen isotope mixtures. The results show that Cu(F-pymo)₂ and CPL-1 exhibit exceptional selectivity that is higher than other MOFs as well as other nanoporous materials such as carbon nanotubes, slit-shaped graphites, and zeolites studied so far. A concept named “quantum effective pore size” (QEPS) was proposed in this work, which can incorporate the effects of quantum sieving, and thus is temperature-dependent. On the basis of the new pore size, good correlations between pore size and selectivity can be established for the MOFs considered; particularly, they can explain the different selectivity performance of the two MOFs with highest selectivity at 40 and 77 K. This work indicates that MOFs are suitable candidates for the separation of hydrogen isotopes through quantum sieving

Quantum Sieving in Metal–Organic Frameworks: A Computational Study

In this work, a systematic computational study was performed to investigate the quantum sieving in nine typical metal–organic frameworks (MOFs) for the separation of hydrogen isotope mixtures. The results show that Cu(F-pymo)₂ and CPL-1 exhibit exceptional selectivity that is higher than other MOFs as well as other nanoporous materials such as carbon nanotubes, slit-shaped graphites, and zeolites studied so far. A concept named “quantum effective pore size” (QEPS) was proposed in this work, which can incorporate the effects of quantum sieving, and thus is temperature-dependent. On the basis of the new pore size, good correlations between pore size and selectivity can be established for the MOFs considered; particularly, they can explain the different selectivity performance of the two MOFs with highest selectivity at 40 and 77 K. This work indicates that MOFs are suitable candidates for the separation of hydrogen isotopes through quantum sieving

Quantum Sieving in Metal–Organic Frameworks: A Computational Study

In this work, a systematic computational study was performed to investigate the quantum sieving in nine typical metal–organic frameworks (MOFs) for the separation of hydrogen isotope mixtures. The results show that Cu(F-pymo)₂ and CPL-1 exhibit exceptional selectivity that is higher than other MOFs as well as other nanoporous materials such as carbon nanotubes, slit-shaped graphites, and zeolites studied so far. A concept named “quantum effective pore size” (QEPS) was proposed in this work, which can incorporate the effects of quantum sieving, and thus is temperature-dependent. On the basis of the new pore size, good correlations between pore size and selectivity can be established for the MOFs considered; particularly, they can explain the different selectivity performance of the two MOFs with highest selectivity at 40 and 77 K. This work indicates that MOFs are suitable candidates for the separation of hydrogen isotopes through quantum sieving

Performance of qualitative fecal immunochemical test for advanced adenomatous polyps.

<p>Sen: sensitivity; Spe: specificity; LR(+): positive likelihood ratio; LR(−): negative likelihood ratio; PPV: positive predictive value; NPV: negative predictive value.</p><p>*rates, absolute numbers, and 95% confidence intervals were provided.</p><p>Performance of qualitative fecal immunochemical test for advanced adenomatous polyps.</p