Electro-assisted Molecular Assembly Giving Atomic-Scale Catalytic Active-Site Detection

The artificially accurate design of nonmetal electrocatalysts’ active site has been a huge challenge because no pure active species with the specific structure could be strictly controlled by traditional synthetic methods. Species with a multiconfiguration in the catalyst hinder identification of the active site and the subsequent comprehension of the reaction mechanism. We have developed a novel electro-assisted molecular assembly strategy to obtain a pure pentagon ring on perfect graphene avoiding other reconstructed structures. More importantly, the active atom was confirmed by the subtle passivation process as the topmost carbon atom. Recognition of the carbon-defect electrocatalysis reaction mechanism was first downsized to the single-atom scale from the experimental perspective. It is expected that this innovative electro-assisted molecular assembly strategy could be extensively applied in the active structure-controlled synthesis of nonmetal electrocatalysts and verification of the exact active atom.     

Regularity of optimal sets for some functional involving eigenvalues of an operator in divergence form

In this paper we consider minimizers of the functional$\min \{{\lambda }_1(\mathrm{\Omega })+\cdots +{\lambda }_k(\mathrm{\Omega })+\mathrm{\Lambda }|\mathrm{\Omega }|,:\mathrm{\Omega }\subset D\text{ open}\}$ where $D\subset {\mathbb{R}}^d$ is a bounded open set and where $0<{\lambda }_1(\mathrm{\Omega })\le \cdots \le {\lambda }_k(\mathrm{\Omega })$ are the first k eigenvalues on Ω of an operator in divergence form with Dirichlet boundary condition and with Hölder continuous coefficients. We prove that the optimal sets ${\mathrm{\Omega }}^{⁎}$ have finite perimeter and that their free boundary $\partial {\mathrm{\Omega }}^{⁎}\cap D$ is composed of a regular part, which is locally the graph of a $C^{1,\alpha }$-regular function, and a singular part, which is empty if $d<d^{⁎}$, discrete if $d=d^{⁎}$ and of Hausdorff dimension at most $d-d^{⁎}$ if $d>d^{⁎}$, for some $d^{⁎}\in \{5,6,7\}$.     

Prediction of the drag reduction effect of pulsating pipe flow based on machine learning

Prediction of drag reduction effect caused by pulsating pipe flows is examined using machine learning. First, a large set of flow field data is obtained experimentally by measuring turbulent pipe flows with various pulsation patterns. Consequently, more than 7000 waveforms are applied, obtaining a maximum drag reduction rate and maximum energy saving rate of 38.6% and 31.4%, respectively. The results indicate that the pulsating flow effect can be characterized by the pulsation period and pressure gradient during acceleration and deceleration. Subsequently, two machine learning models are tested to predict the drag reduction rate. The results confirm that the machine learning model developed for predicting the time variation of the flow velocity and differential pressure with respect to the pump voltage can accurately predict the nonlinearity of pressure gradients. Therefore, using this model, the drag reduction effect can be estimated with high accuracy.     

Metabolomics techniques applied in the investigation of phenolic acids from the agro-industrial by-product of Carapa guianensis Aubl

Processing of Carapa guianensis seeds to obtain oil on an industrial scale generates a significant amount of by-product, approximately 66% w/w, which is called cake and is a potential source of biomolecules, including simple phenolic structures. For this reason, studies were carried out on the chemical profiles of hydrolyzed extract from this agro-industrial by-product through High Performance Thin-Layer Chromatography (HPTLC) and Gas Chromatography coupled to Mass Spectrometry (GC–MS). These techniques were used to detect metabolic classes and/or groups, and to identify, for the first time, thirteen simple phenolic acids in this by-product. The sample antioxidant capacity was determined by methods of 2,2-diphenyl-1-picrylhydrazyl (DPPH)and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS⁺) radicals direct sequestration. The hydrolyzed fraction showed a total of 63.47% in the relative abundance of the total of compounds, standing out: p-hydroxybenzoic acid (39.19%) and protocatechuic acid (3,4-dihydroxybenzoic acid) (5.62%), both from hydroxybenzoic acids and 3-(3,4-dihydroxyphenyl)lactic acid, (7.76%) hydroxycinnamic acids derivatives. In these results, the fraction rich in simple phenolic acids was obtained, attributing the prominent behavior of this matrix antioxidant activity, expressed by (IC₅₀: of 16.42 µg/mL and 6.52 µg/mL for DPPH and ABTS⁺ radicals, respectively). The research demonstrated an alternative to applicability that involves sustainability from agro-industrial. These techniques were used to detect metabolic classes and/or groups, and to identify, for the first time, thirteen simple phenolic acids in this by-product, generating a process capable of converting biomass into a bioproduct, consisting of bioactive compounds, in addition to adding value to the industrial chain.     

Electrocatalysis in confined space

The complex interplay of restricted mass transport leading to local accumulation or depletion of educts, intermediates, products, counterions and co-ions influences the reactions at the active sites of electrocatalysts when electrodes are rough, three-dimensionally mesoporous or nanoporous. This influence is important with regard to activity, and even more to selectivity, of electrocatalytic reactions. The underlying principles are discussed based on the growing awareness of these considerations over recent years.     

基于串联两阶段网络DEA模型的供应链规模收益分析

数据包络分析(DEA)是评价供应链系统(Supply chain system)间相对有效性的一种重要的工具,但是传统的DEA不考虑供应链的内部结构,对系统效率评价偏高;而本文所研究两阶段串联供应链系统,考虑把部分中间产品作为最终产品输出,增加额外中间投入的情形.基于所提出的供应链系统结构,本文建立相应的串联结构下的网络DEA模型,并针对所建立模型进行相关理论的研究,给出了串联结构下的生产可能集和规模收益情况判定方法.最后,进行数值实验,以验证我们提出的结论.     

Bimetallic Covalent Organic Frameworks for Constructing Multifunctional Electrocatalyst

Covalent organic frameworks (COFs) are a new class of crystalline porous polymers comprised mainly of carbon atoms, and are versatile for the integration of heteroatoms such as B, O, and N into the skeletons. The designable structure and abundant composition render COFs useful as precursors for heteroatom-doped porous carbons for energy storage and conversion. Herein, we describe a multifunctional electrochemical catalyst obtained through pyrolysis of a bimetallic COF. The catalyst possesses hierarchical pores and abundant iron and cobalt nanoparticles embedded with standing carbon layers. By integrating these features, the catalyst exhibits excellent electrochemical catalytic activity in the oxygen reduction reaction (ORR), with a 50 mV positive half-wave potential, a higher limited diffusion current density, and a much smaller Tafel slope than a Pt-C catalyst. Moreover, the catalyst displays superior electrochemical performance toward the hydrogen evolution reaction (HER), with overpotentials of −0.26 V and −0.33 V in acidic and alkaline aqueous solution, respectively, at a current density of 10 mA cm⁻². The overpotential in the catalysis of the oxygen evolution reaction (OER) was 1.59 V at the same current density.     

Sobolev Stability of Plane Wave Solutions to the Nonlinear Schrödinger Equation

This article explores the questions of long time orbital stability in high order Sobolev norms of plane wave solutions to the NLSE in the defocusing case.     

Thermally induced solid-state transformation of cimetidine. A multi-spectroscopic/chemometrics determination of the kinetics of the process and structural elucidation of one of the products as a stable N3-enamino tautomer

Exposure of cimetidine (CIM) to dry heat (160–180 °C) afforded, upon cooling, a glassy solid containing new and hitherto unknown products. The kinetics of this process was studied by a second order chemometrics-assisted multi-spectroscopic approach. Proton and carbon-13 nuclear magnetic resonance (NMR), as well as ultraviolet and infrared spectroscopic data were jointly used, whereas multivariate curve resolution with alternating least squares (MCR-ALS) was employed as the chemometrics method to extract process information. It was established that drug degradation follows a first order kinetics.