Majorana fermions induced fast-and slow-light in a hybrid semiconducting nanowire/superconductor device

We investigate theoretically Rabi-like splitting and Fano resonance in absorption spectra of quantum dots(QDs)based on a hybrid QD-semiconducting nanowire/superconductor(SNW/SC)device mediated by Majorana fermions(MFs).Under the condition of pump on-resonance and off-resonance,the absorption spectrum experiences the conversion from Fano resonance to Rabi-like splitting in different parametric regimes.In addition,the Fano resonances are accompanied by the rapid normal phase dispersion,which will indicate the coherent optical propagation.The results indicate that the group velocity index is tunable with controlling the interaction between the QD and MFs,which can reach the conversion between the fast-and slow-light.Fano resonance will be another method to detect MFs and our research may indicate prospective applications in quantum information processing based on the hybrid QD-SNW/SC devices.     

Facile Synthesis of Manganese Tellurite Nanoparticles for Magnetic Resonance Imaging-Guided Photothermal Therapy

In this study, manganese tellurite (MnTeO₃) nanoparticles are developed as theranostic agents for magnetic resonance imaging (MRI)-guided photothermal therapy of tumor. MnTeO₃ nanoparticles are synthesized via a simple one-step method. The as-synthesized MnTeO₃ nanoparticles with uniform size show good biocompatibility. In particular, MnTeO₃ nanoparticles exhibit a high photothermal conversion efficiency (η = 26.3%), which is higher than that of gold nanorods. Moreover, MnTeO₃ nanoparticles also have high MRI performance. The longitudinal relaxivity (r₁) value of MnTeO₃ nanoparticles is determined to be 8.08 ± 0.2 mm ⁻¹ s⁻¹, which is higher than that of clinically approved T₁-contrast agents Gd-DTPA (4.49 ± 0.1 mm ⁻¹ s⁻¹). The subsequent MnTeO₃ nanoparticles-mediated photothermal therapy displays a highly efficient ablation of tumor cells both in vitro and in vivo with negligible toxicity. It is demonstrated that MnTeO₃ nanoparticles can serve as promising theranostic agents with great potentials for MRI-guided photothermal therapy.     

3.0T MRI检查结合X线钼靶诊断乳腺恶性肿瘤的研究

本研究探讨3.0T磁共振成像(MRI)结合X线钼靶诊断乳腺恶性肿瘤的价值。采用回顾性研究方法,选取乳腺肿块患者110例162个病灶,给予3.0T MRI及X线钼靶检查。经病理确诊为恶性病变101个;恶性病灶形态不规则、边缘毛刺、时间-信号强度曲线(TIC)类型Ⅲ型和早期增强率≥60%比例明显高于良性病灶(P<0.05),而分叶状比例和表观扩散系数(ADC)值明显低于良性病变(P<0.05);恶性病变X线钼靶表现:形态不规则、钙化、结构不对称和大导管征比例明显高于良性病变(P<0.05);MRI联合X线钼靶诊断乳腺恶性病变的灵敏性、准确性和阴性预测值明显高于MRI诊断(P<0.05)。3.0T MRI检查结合X线钼靶诊断乳腺恶性肿瘤有较好的价值。     

A combination of dispersive liquid–liquid microextraction and surface plasmon resonance sensing of gold nanoparticles for the determination of ziram pesticide

We have developed a reliable, fast, and highly sensitive analytical method utilizing dispersive liquid–liquid microextraction and gold nanoparticles probes for ziram (zinc bis(dimethyldithiocarbamate)) determination. The method is based on the in situ formation of gold nanoparticles in carbon tetrachloride as an organic phase. It was found that the trace levels of ziram influenced the formation of gold nanoparticles, leading to absorbance change of a sedimented phase. The results of the colorimetric ziram determination were in the concentration range of 0.12–2.52 ng/mL with a limit of detection of 0.06 ng/mL. The formation of the stable and dispersed gold nanoparticles in the organic phase provides a good precision for dispersive liquid–liquid microextraction method, resulting in the relative standard deviation of 3.8 and 1.2% for 0.56 and 1.58 ng/mL of ziram, respectively. This method has been successfully used for the ziram determination in samples of well and river water, soil, potato, carrot, wheat, and paddy soil.     

One‐Step Labeling of Collagen Hydrogels with Polydopamine and Manganese Porphyrin for Non‐Invasive Scaffold Tracking on Magnetic Resonance Imaging

Biomaterial scaffolds are the cornerstone to supporting 3D tissue growth. Optimized scaffold design is critical to successful regeneration, and this optimization requires accurate knowledge of the scaffold's interaction with living tissue in the dynamic in vivo milieu. Unfortunately, non‐invasive methods that can probe scaffolds in the intact living subject are largely underexplored, with imaging‐based assessment relying on either imaging cells seeded on the scaffold or imaging scaffolds that have been chemically altered. In this work, the authors develop a broadly applicable magnetic resonance imaging (MRI) method to image scaffolds directly. A positive‐contrast “bright” manganese porphyrin (MnP) agent for labeling scaffolds is used to achieve high sensitivity and specificity, and polydopamine, a biologically derived universal adhesive, is employed for adhering the MnP. The technique was optimized in vitro on a prototypic collagen gel, and in vivo assessment was performed in rats. The results demonstrate superior in vivo scaffold visualization and the potential for quantitative tracking of degradation over time. Designed with ease of synthesis in mind and general applicability for the continuing expansion of available biomaterials, the proposed method will allow tissue engineers to assess and fine‐tune the in vivo behavior of their scaffolds for optimal regeneration.     

Inhibition effect of butan-1-ol on the corrosion behavior of austenitic stainless steel (Type 304) in dilute sulfuric acid

The electrochemical behavior of austenitic stainless steel (Type 304) in 3 M sulfuric acid with 3.5% recrystallized sodium chloride at specific concentrations of butan-1-ol was investigated with the aid of potentiodynamic polarization, open circuit measurement and weight loss technique. Butan-1-ol effectively inhibited the steel corrosion with a maximum inhibition efficiency of 78.7% from weight-loss analysis and 80.9% from potentiodynamic polarization test at highest concentration studied. Adsorption of the compound obeyed the Freundlich isotherm. Thermodynamic calculations reveal physiochemical interactions and spontaneous adsorption mechanism. Surface characterizations showed the absence of corrosion products and topographic modifications of the steel. Statistical analysis depicts the overwhelming influence and statistical significance of inhibitor concentration on the inhibition performance.     

Probing nanocolumnar silver nanoparticle/zinc oxide hierarchical assemblies with advanced surface plasmon resonance and their enhanced photocatalytic performance for formaldehyde removal

Recent advances in photocatalysis focus on the development of materials with hierarchical structure and on the surface plasmon resonance (SPR) phenomenon exhibited by metal nanoparticles (NPs). In this work, both are combined in a material where size‐controllable Ag‐NPs are uniformly loaded onto the hierarchical microporous and mesoporous and nanocolumnar structures of ZnO, resulting in Ag‐NP/ZnO nanocomposites. The embedded Ag‐NPs slightly decrease the hydrophobicity of fibrous ZnO, improve its wettability, and increase the absorption of formaldehyde (H₂CO) onto the photocatalyst, all of this resulting in excellent photodegradation of formaldehyde in aqueous solution. Besides, we found that Ag‐NPs with optimal size not only accelerate the charge transfer to the surface of ZnO, but also strengthen the SPR effect in the intercolumnar channels of fibrous ZnO particles combining with high concentration of photo‐generated radical species. The micro‐to‐mesoporous ZnO is like a nanoarray packed Ag‐NPs. With Ag‐NPs of diameter 2.5 < ? < 6.5 nm, ZnO exhibits the most superior photodegradation rate constant value of 0.0239 min^?1 with total formaldehyde removal of 97%. This work presents a new feasible approach involving highly sophisticated Ag‐NP/ZnO architecture combining the SPR effect and hierarchically ordered structures, which results in high photocatalytic activity for formaldehyde photodegradation.     

Oxidative dehydrogenation of n-octane over a vanadium–magnesium oxide catalyst: Influence of the gas hourly space velocity

Oxidative dehydrogenation (ODH) of n-octane was carried out over a vanadium–magnesium oxide catalyst in a continuous flow fixed bed reactor. The catalyst was characterized by ICP–OES, powder XRD and SEM. The catalytic tests were carried out at different gas hourly space velocities (GHSVs), viz. 4000, 6000, 8000, and 10,000 h^?1. The best selectivity for octenes was obtained at the GHSV of 8000 h^?1, while that for C8 aromatics was attained at the GHSV of 6000 h^?1 at high temperatures (500 and 550 °C). The catalytic testing at the GHSV of 10,000 h^?1 showed the lowest activity, while that at the GHSV of 4000 h^?1 consistently showed the lowest ODH selectivity. Generally, the best ODH performance was obtained by the catalytic testing at the GHSVs of 6000 and 8000 h^?1. No phasic changes were observed after the catalytic testing.     

Automated chemical resonance generation and structure filtration for kinetic modeling

This work discusses efficient and automated methods for constructing a set of representative resonance structures for arbitrary chemical species, including radicals and biradicals, consisting of the elements H, C, O, N, and S. Determining the representative reactive structures of chemical species is crucial for identification of reactive sites and consequently applying the correct reaction templates to generate the set of important reactions during automated chemical kinetic model generation. We describe a fundamental set of resonance pathway types, accounting for simple resonating structures, as well as global approaches for polycyclic aromatic species. Automatically discovering potential localized structures along with filtration to identify the representative structures was shown to be robust and relatively fast. The algorithms discussed here were recently implemented in the Reaction Mechanism Generator (RMG) software. The final structures proposed by this method were found to be in reasonable agreement with quantum chemical computation results of localized structure contributions to the resonance hybrid.     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.