Formation of Chlorotriophenoxy Radicals from Complete Series Reactions of Chlorotriophenols with H and OH Radicals

The chlorothiophenoxy radicals (CTPRs) are key intermediate species in the formation of polychlorinated dibenzothiophenes/thianthrenes (PCDT/TAs). In this work, the formation of CTPRs from the complete series reactions of 19 chlorothiophenol (CTP) congeners with H and OH radicals were investigated theoretically by using the density functional theory (DFT) method. The profiles of the potential energy surface were constructed at the MPWB1K/6-311+G(3df,2p)//MPWB1K/6-31+G(d,p) level. The rate constants were evaluated by the canonical variational transition-state (CVT) theory with the small curvature tunneling (SCT) contribution at 600–1200 K. The present study indicates that the structural parameters, thermal data, and rate constants as well as the formation potential of CTPRs from CTPs are strongly dominated by the chlorine substitution at the ortho-position of CTPs. Comparison with the study of formation of chlorophenoxy radicals (CPRs) from chlorophenols (CPs) clearly shows that the thiophenoxyl-hydrogen abstraction from CTPs by H is more efficient than the phenoxyl-hydrogen abstraction from CPs by H, whereas the thiophenoxyl-hydrogen abstraction from CTPs by OH is less impactful than the phenoxyl-hydrogen abstraction from CPs by OH. Reactions of CTPs with H can occur more readily than that of CTPs with OH, which is opposite to the reactivity comparison of CPs with H and OH.     

Porous sunflower plate-like NiFe2O4/CoNi–S heterostructure as efficient electrocatalyst for overall water splitting

Constructing high-efficient and nonprecious electrocatalysts is of primary importance for improving the efficiency of water splitting. Herein, a novel sunflower plate-like NiFe₂O₄/CoNi–S nanosheet heterostructure was fabricated via facile hydrothermal and electrodeposition methods. The as-fabricated NiFe₂O₄/CoNi–S heterostructure array exhibits remarkable bifunctional catalytic activity and stability toward oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) in alkaline media. It presents a small overpotential of 219 mV and 149 mV for OER and HER, respectively, to produce a current density of 10 mA cm^?2. More significantly, when the obtained electrodes are used as both the cathode and anode in an electrolyzer, a voltage of 1.57 V is gained at 10 mA cm^?2, with superior stability for 72 h. Such outstanding properties are ascribed to: the 3D porous network structure, which exposes more active sites and accelerates mass transfer and gas bubble emission; the high conductivity of CoNi–S, which provides faster charge transport and thus promotes the electrocatalytic reaction of the composites; and the effective interface engineering between NiFe₂O₄ (excellent performance for OER) and CoNi–S (high activity for HER), which leads to a shorter transport pathway and thus expedites electron transfer. This work provides a new strategy for designing efficient and inexpensive electrocatalysts for water splitting.     

Effects of anion and cation doping on the thermoelectric properties of n-type PbS

The PbCl_xS_1-x and Pb_1-xBi_xS (x? =?0–0.05) bulks were fabricated with a facile method of hydrothermal synthesis and microwave sintering, and the effect of anionic and cationic donors on the thermoelectric performance of PbS was investigated. Although Cl^? and Bi³⁺ both effectively improved the thermoelectric properties of n-type PbS, more excellent thermoelectric performance was obtained from Cl^? doped samples because of higher electrical property and lower thermal conductivity at higher temperature (T? >?600?K). The thermoelectric figure of merit (ZT) reaches 1.04 for PbCl_0.015S_0.985 at 800?K and increases with temperature increasing without sign of saturation, which is probably the highest value ever reported for single-phase polycrystalline n-type PbS. The results also indicate that the hydrothermal synthesis and microwave sintering can realize anion doping as well as cation doping for n-type PbS at low cost, and PbS should be a robust alternative for PbTe thermoelectric materials.     

Bearing Failure Optimization of Composite Double-Lap Bolted Joints Based on a Three-Step Strategy Marked By Feasible Region Reduction and Model Decoupling

To minimize the mass and increase the bearing failure load of composite doublelap bolted joints, a three-step optimization strategy including feasible region reduction, optimization model decoupling and optimization was presented. In feasible region reduction, the dimensions of the feasible design region were reduced by selecting dominant design variables from numerous multilevel parameters by sensitivity analyses, and the feasible regions of variables were reduced by influence mechanism analyses. In model decoupling, the optimization model with a large number of variables was divided into various sub-models with fewer variables by variance analysis. In the third step, the optimization sub-models were solved one by one using a genetic algorithm, and the modified characteristic curve method was adopted as the failure prediction method. Based on the proposed optimization method, optimization of a double-lap single-bolt joint was performed using the ANSYS^® code. The results show that the bearing failure load increased by 13.5% and that the mass decreased by 8.7% compared with those of the initial design of the joint, which validated the effectiveness of the three-step optimization strategy.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.