Instant Cyclohexene Epoxidation Over Ni-TUD-1 Under Ambient Conditions

Catalysis Letters - To avoid the aggregation problem and activity loss of nickel oxide (NiO) nanoparticles (NPs) in organic reactions, NiO NPs were incorporated into TUD-1 mesoporous material....     

A hidden Markov model to detect regime changes in cryptoasset markets

The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well-described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.     

A Polyallylamine Anchored Amine‐Rich Laser‐Ablated Graphene Platform for Facile and Highly Selective Electrochemical IgG Biomarker Detection

Current immunosensors have an insufficient number of binding sites for the recognition of biomolecules, which leads to false positive or negative results. In this research, a facile, cost‐effective, disposable, and highly selective electrochemical immunosensing platform is developed based on cationic polyelectrolyte polyallylamine (PAAMI) anchored laser‐ablated graphene (LAG). Here, for the first time, PAAMI is introduced to stabilize LAG flakes, while retaining the intrinsic thermal and electronic properties of the substrate by noncovalent π–π interaction and electrostatic physical absorption. The sensing platform offers a suitable number of anchoring sites for the immobilized antibodies by providing ? NH₂ functional groups. The proper grafting of PAAMI is confirmed through X‐ray photoelectron spectroscopy and Raman spectroscopy. The immunosensing platform is applied to detect immunoglobulin (IgG) biomarkers as a proof of concept. Under optimized conditions, the sensing platform exhibits a linear range of 0.012–15 and 15–352 ng mL^?1 with a limit of detection of 6 pg mL^?1 for IgG detection with high selectivity. Based on the analysis, the developed immunosensing platform can be used for point‐of‐care detection of IgG in clinical diagnostic centers. Furthermore, the developed strategy is well suited for the detection of other cancer biomarkers after immobilizing the relevant antibodies.     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

Studies on influence of hydrogen and carbon monoxide concentration on reduction progression behavior of molybdenum oxide catalyst

Temperature programmed reduction (TPR) analysis was applied to investigate the chemical reduction progression behavior of molybdenum oxide (MoO₃) catalyst. The composition and morphology of the reduced phases were characterized by X-ray diffraction spectroscopy (XRD), X-ray photoelectron spectroscopy (XPS), and field emission scanning electron microscopy (FE-SEM). The reduction progression of MoO₃ catalyst was attained with different reductant types and concentration (10% H₂/N₂, 10% and 20% CO/N₂ (%, v/v)). Two different modes of reduction process were applied. The first approach of reduction involved non-isothermal mode reduction up to 700 °C, while the second approach of reduction involved the isothermal mode reduction for 60 min at 700 °C. Hydrogen temperature programmed reduction (H₂-TPR) results showed the reduction progression of three-stage reduction of MoO₃ (Mo⁶⁺ → Mo⁵⁺ → Mo⁴⁺ → Mo⁰) with Mo⁵⁺ and Mo⁴⁺. XRD analysis confirmed the formation of Mo₄O₁₁ phase as an intermediate phase followed by MoO₂ phase. After 60 min of isothermal reduction, peaks of metallic molybdenum (Mo) appeared. Whereas, FESEM analysis showed porous crater-like structure on the surface cracks of MoO₂ layer which led to the growth of Mo phase. Meanwhile, the reduction of MoO₃ catalyst in 10% carbon monoxide (CO) showed the formation of unstable intermediate phase of Mo₉O₂₆ at the early stage of reduction. Furthermore, by increasing 20% CO led to the carburization of MoO₂ phase, resulted in the formation of Mo₂C rather than the formation of metallic Mo, as confirmed by XPS analysis. Therefore, the presented study shows that hydrogen gave better reducibility due to smaller molecular size, which contributed to high diffusion rate and achieved deeper penetration into the MoO₃ catalyst compared to carbon monoxide reductant. Hence, the reduction of MoO₃ in carbon monoxide atmosphere promoted the formation of Mo₂C which was in agreement with the thermodynamic assessment.     

FexCo1−x-doped titanium oxide nanotubes as effective photocatalysts for hydrogen extraction from ammonium phosphate

Theoretically, tri-ammonium phosphate (NH₄)₃PO₄ embeds considerable amount of hydrogen. Typically, the expected hydrogen release from this cheap and stable material is 73.83 mmol/g_salt if a proper catalyst is exploited in the hydrolysis reaction. In this study Fe_xCo_1?x-doped titanium oxide nanotubes are introduced as an efficient photocatalyst under solar radiation. The introduced modified titanium oxide nanotubes have been prepared in two successive steps. First, Na-doped TiO₂ nanotubes were synthesized by hydrothermal treatment in presence of 10 N NaOH solution at 160 °C for 16 h. Then, doping by the proposed metals was carried out by ion exchange process in a microwave oven. X-ray photoelectron microscopy (XPS) and transmission electron microscopy (TEM) confirmed the success of the doping process and the nanotubular morphology, respectively. Study the photo characteristics indicated that the proposed metal doping shifted the band gap from UV to the visible light region as the estimated band gap energies for the as-prepared and doped nanotubes were 3.4 and 2.1 eV, respectively. Moreover, distinct enhancement for the visible light absorption capacity was observed. Accordingly, a distinguished improvement in the photocatalytic activity toward tri-ammonium phosphate hydrolysis was observed. However, the two metals content has a strong influence on the amount of the obtained hydrogen per gram of tri-ammonium phosphate salt. Numerically, the maximum obtained hydrogen was 4.0, 11.2, 11.2, 11.6, 13.4, 16.5, 17.4, 13.4 and 9.8 mmol/g_salt for the pristine TiO₂, and Fe_xCo_1?x-doped TiO₂ with x = 1, 0.8, 0.6, 0.5, 0.4, 0.3, 0.2, and 0.0, respectively.     

Reordering for improving global Arnoldi–Tikhonov method in image restoration problems

This paper discusses the solution of large-scale linear discrete ill-posed problems arising from image restoration problems. Since the scale of the problem is usually very large, the computations with the blurring matrix can be very expensive. In this regard, we consider problems in which the coefficient matrix is the sum of Kronecker products of matrices to benefit the computation. Here, we present an alternative approach based on reordering of the image approximations obtained with the global Arnoldi–Tikhonov method. The ordering of the intensities is such that it makes the image approximation monotonic and thus minimizes the finite differences norm. We present theoretical properties of the method and numerical experiments on image restoration.