Recent Advances in Noble Metal (Pt, Ru, and Ir)-Based Electrocatalysts for Efficient Hydrogen Evolution Reaction

Noble metal (Pt, Ru, and Ir)-based electrocatalysts are currently considered the most active materials for the hydrogen evolution reaction (HER). Although they have been associated with high cost, easy agglomeration, and poor stability during the HER reaction, recent efforts to intentionally tailor noble-metal-based catalysts have led to promising improvements, with lower cost and superior activity, which are critical to achieving large-scale production of pure hydrogen. In this mini-review, we focus on the recent advances in noble-metal-based HER electrocatalysts. In particular, the synthesis strategies to enhance cost-effectiveness and the catalytic activity for HER are highlighted

Error bound of the multilevel adaptive cross approximation (MLACA)

An error bound of the multilevel adaptive cross approximation (MLACA 1, which is a multilevel version of the adaptive cross approximation-singular value decomposition (ACA-SVD), is rigorously derived. For compressing an off-diagonal submatrix of the method of moments MAD impedance matrix with a binary tree, the L-level MIACA includes L + 1 steps, and each step includes 2(L) ACA-SVD decompositions. If the relative Frobenius norm error of the ACA-SVD used in the MLACA is smaller than epsilon, the rigorous proof in this communication shows that the relative Frobenius norm error of the L-Ievel MLACA is smaller than (1 + epsilon)(L+1) - 1. In practical applications, the error bound of the MLACA can be approximated as epsilon(L + 1), because epsilon is always << 1. The error upper bound can he used to control the accuracy of the MLACA. To ensure an error of the L-level MLACA smaller than epsilon for different L, the ACA-SVD threshold can be set to (1 + epsilon)1/L+1 - 1, which approximately equals epsilon/(L + 1) for practical applications.Peer ReviewedPostprint (author's final draft

Neural option pricing for rough Bergomi model

The rough Bergomi (rBergomi) model can accurately describe the historical and implied volatilities, and has gained much attention in the past few years. However, there are many hidden unknown parameters or even functions in the model. In this work, we investigate the potential of learning the forward variance curve in the rBergomi model using a neural SDE. To construct an efficient solver for the neural SDE, we propose a novel numerical scheme for simulating the volatility process using the modified summation of exponentials. Using the Wasserstein 1-distance to define the loss function, we show that the learned forward variance curve is capable of calibrating the price process of the underlying asset and the price of the European-style options simultaneously. Several numerical tests are provided to demonstrate its performance

X-ray luminescence computed tomography using a focused X-ray beam

Due to the low X-ray photon utilization efficiency and low measurement sensitivity of the electron multiplying charge coupled device (EMCCD) camera setup, the collimator based narrow beam X-ray luminescence computed tomography (XLCT) usually requires a long measurement time. In this paper, we, for the first time, report a focused X-ray beam based XLCT imaging system with measurements by a single optical fiber bundle and a photomultiplier tube (PMT). An X-ray tube with a polycapillary lens was used to generate a focused X-ray beam whose X-ray photon density is 1200 times larger than a collimated X-ray beam. An optical fiber bundle was employed to collect and deliver the emitted photons on the phantom surface to the PMT. The total measurement time was reduced to 12.5 minutes. For numerical simulations of both single and six fiber bundle cases, we were able to reconstruct six targets successfully. For the phantom experiment, two targets with an edge-to-edge distance of 0.4 mm and a center-to-center distance of 0.8 mm were successfully reconstructed by the measurement setup with a single fiber bundle and a PMT.Comment: 39 Pages, 12 Figures, 2 Tables, In submission (under review) to JB

Solid waste mixtures combustion in a circulating fluidized Bed: emission properties of NOx, Dioxin, and Heavy Metals

To efficiently and environment friendly combust the domestic garbage, sludge, and swill waste fuels, five different fuels are prepared by mixing the waste fuels together with coal, and grass biomass at different mixing ratios, and finally those fuels were combusted in a circulating fluidized bed (CFB) reactor. The emission performances of NOx, dioxin, and heavy metal during the combustion tests are studied. The results showed that a stable furnace temperature can be reached at approximately 850 °C when combusting all studied mixed fuels, benefiting the thermal processes of sludge and domestic garbage and thus realizing the purpose of waste-to-fuel. In addition, the dioxin emissions are much lower than the emission standards, and NOx emissions could be reduced significantly by adjusting the ratio of waste fuels. However, the emissions of mercury, lead, and the combinations of chromium, tin, antimony, cupper and manganese components all exceeded the pollution control standard for hazardous wastes incineration, a further technology is required for heavy metal reductions to achieve the emission standards

Clinical Value of CD24 Expression in Retinoblastoma

Background. The expression of CD24 has been detected in a wide variety of human malignancies. Downregulation of CD24 inhibits proliferation and induces apoptosis in tumor cells, whereas its upregulation increases tumor growth and metastasis. However, no data on CD24 protein levels in retinoblastoma are available, and the mechanism of CD24 involvement in retinoblastoma progress has not been elucidated. The aim of this study was to explore the expression profile of CD24 in the retinoblastoma tumor samples and to correlate with clinicopathological parameters. Methods. Immunohistochemistry was performed for CD24 on the archival paraffin sections of retinoblastoma and correlated with clinicopathological features. Western blotting was performed to confirm immunoreactivity results. Results. CD24 immunoreactivity was observed in 72.0% (36/50) of the retinoblastoma specimens. Among the 35 low-risk tumors, CD24 was expressed in 62.9% (22/35) tumors and among the 15 high-risk tumors, CD24 was expressed in 93.3% (14/15) tumors. High-risk tumors showed significantly increased expression of CD24 compared to tumors with low-risk (P < 0.05). Conclusions. This is the first correlation between CD24 expression and histopathology in human retinoblastoma. Our study showed increased expression of CD24 in high risk tumors compared to low risk tumors. Further functional studies are required to explore the role of CD24 in retinoblastoma