Microscopic Phase Structure of Mo-based Catalyst and Its Catalytic Activity for Soot Oxidation

The MoO3 catalysts supported on nano-scale TiO2 with various loading rates (5%, 10%, 20%, and 40%) were prepared by an impregnation method. The phase structures of nano-scale MoO3/TiO2 catalysts were characterized by Brunner-Emmet-Teller, Fourier Transform Infrared Spectra, X-ray Diffraction, and Scanning Electron Microscope. The oxidation activities of catalysts over diesel soot were performed in a Thermogravimetric Analysis system. The kinetics of the catalytic oxidation process was analyzed based on Starink method. The characterization results showed that the phase structure of MoO3 supported on TiO2 depends heavily on the molybdenum contents, which put great effects on soot oxidation. The orthorhombic crystal system (α-MoO3) appeared on the surface of the catalysts when the MoO3 exceeds 10%. Due to the low melting point and good surface mobility of MoO3, the catalytic activity was increased and the characteristic temperatures were decreased with the increase in MoO3 contents. As a result, the activities of catalysts with different loading rates for soot oxidation can be ranked as: Mo5<Mo10<Mo2

Bi2O2Se nanowires presenting high mobility and strong spin-orbit coupling

Systematic electrical transport characterizations were performed on high-quality Bi2O2Se nanowires to illustrate its great transport properties and further application potentials in spintronics. Bi2O2Se nanowires synthesized by chemical vapor deposition method presented a high field-effect mobility up to 1.34*104 cm2V-1s-1, and exhibited ballistic transport in the low back-gate voltage (Vg) regime where conductance plateaus were observed. When further increasing the electron density by increasing Vg, we entered the phase coherent regime and weak antilocalization (WAL) was observed. The spin relaxation length extracted from the WAL was found to be gate tunable, ranging from ~100 nm to ~250 nm and reaching a stronger spin-obit coupling (SOC) than the two-dimensional counterpart (flakes). We attribute the strong SOC and the gate tunability to the presence of a surface accumulation layer which induces a strong inversion asymmetry on the surface. Such scenario was supported by the observation of two Shubnikov-de Haas oscillation frequencies that correspond to two types of carriers, one on the surface, and the other in the bulk. The high-quality Bi2O2Se nanowires with a high mobility and a strong SOC can act as a very prospective material in future spintronics.Comment: 22 pages, 7 figure

Universal conductance fluctuations and phase-coherent transport in a semiconductor Bi2_2O2_2Se nanoplate with strong spin-orbit interaction

We report on phase-coherent transport studies of a Bi$_2$O$_2$Se nanoplate and on observation of universal conductance fluctuations and spin-orbit interaction induced reduction in fluctuation amplitude in the nanoplate. Thin-layered Bi$_2$O$_2$Se nanoplates are grown by chemical vapor deposition (CVD) and transport measurements are made on a Hall-bar device fabricated from a CVD-grown nanoplate. The measurements show weak antilocalization at low magnetic fields at low temperatures, as a result of spin-orbit interaction, and a crossover toward weak localization with increasing temperature. Temperature dependences of characteristic transport lengths, such as spin relaxation length, phase coherence length, and mean free path, are extracted from the low-field measurement data. Universal conductance fluctuations are visible in the low-temperature magnetoconductance over a large range of magnetic fields and the phase coherence length extracted from the autocorrelation function is in consistence with the result obtained from the weak localization analysis. More importantly, we find a strong reduction in amplitude of the universal conductance fluctuations and show that the results agree with the analysis assuming strong spin-orbit interaction in the Bi$_2$O$_2$Se nanoplate.Comment: 11 pages, 4 figures, supplementary material

Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained

Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained

Circulating cell-free DNA and IL-10 from cerebrospinal fluids aid primary vitreoretinal lymphoma diagnosis

Primary vitreoretinal lymphoma (PVRL) is a rare variant of primary central nervous system lymphoma (PCNSL) that presents diagnostic challenges. Here, we focused on circulating cell-free DNA (cfDNA) and interleukin-10 (IL-10) isolated from cerebrospinal fluid. Twenty-three VRL patients (17 PVRL, 2 PCNSL/O, and 4 relapsed VRL, from 10/2018 to 12/2021) and 8 uveitis patients were included in this study. CSF samples from 19 vitreoretinal lymphoma patients had sufficient cfDNA for next-generation sequencing. Of these patients, 73.7% (14/19) had at least one meaningful non-Hodgkin lymphoma-related mutation. The characteristic MYD88L265P mutation was detected in the CSF of 12 VRL patients, with a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 63.2%, 100%, 100%, and 46.2%, respectively. No meaningful lymphoma related mutations were found in CSF samples from uveitis controls with typical intraocular lesions. Meanwhile, CSF IL-10 levels were elevated in 95.7% of the VRL patients, with a sensitivity, specificity, PPV, and NPV of 95.7%, 100%, 100% and 88.9%, respectively. Key somatic mutations like MYD88L265P and CD79B detected from CSF cfDNA and elevated CSF IL-10 levels can be promising adjuncts for primary vitreoretinal lymphoma diagnosis

Pavement dynamic monitoring data processing based on wavelet decomposition and reconfiguration methods

Early damage to asphalt pavements generally occurs due to the increasing traffic flow and the loads of vehicles, coupled with alternating high- and low-temperature cycles, freeze–thaw cycles, ultraviolet radiation, and other harsh environments. Several types of distress, such as rutting, cracking, and other damage, deteriorate the serviceability of asphalt pavements and shorten the road service life. Thus, the long-term structural mechanical response of asphalt pavements under the influence of loads and the environment is crucial data for the road sector, which provides guidance about road maintenance. Effectively processing the pavement dynamic monitoring data is a prerequisite to obtain the dynamic response of asphalt pavement structures. However, the dynamic monitoring data of pavements are often characterized by transient weak signals with strong noises, making it challenging to extract their essential characteristics. In this study, wavelet decomposition and reconstruction methods were applied to reduce the noise of pavement dynamic response data. The parameters of the signal-to-noise ratio (SNR) and root mean square error (RMSE) were introduced to compare and analyze the effect of the decomposition of two different wavelet functions: the symlet (sym) wavelet function and the Daubechies (db) wavelet function. The results showed that both the sym and db wavelet functions can effectively obtain the average similarity information and the detailed information of the dynamic response signals of the pavement, the SNR after the sym wavelet fixed-threshold denoising process is relatively higher, and the RMSE is smaller than that of the db wavelet. Thus, wavelet transformation exhibits good localization properties in both the time and frequency domains for processing pavement dynamic monitoring data, making it a suitable approach for handling massive pavement dynamic monitoring data