Hydrogenation of acetylene into ethane–ethene mixtures over modified Pd–alumina catalysts

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The support designs of the triply even binary codes of length 48

In this paper, we present examples of codes all of whose weight classes support 1‐designs, with duals whose classes include two that support 2‐designs. We can find these examples in the triply even binary codes of length 48, which have been classified by Betsumiya and Munemasa.     

One‐pot green synthesis of Cu/bone nanocomposite and its catalytic activity in the synthesis of 1‐substituted 1H‐1,2,3,4‐tetrazoles and reduction of hazardous pollutants

In this work, a simple and green method is reported for the biosynthesis of Cu/bone nanocomposite using Cordyline fruticosa extract as a stabilizer and reductant. Animal bone was used as a natural support to prevent the accumulation of Cu nanoparticles. The catalytic activity of Cu/bone nanocomposite was assessed in the synthesis of 1‐substituted 1H‐1,2,3,4‐tetrazoles and reduction of various organic dyes, including 4‐nitrophenol (4‐NP), nigrosin (NS), congo red (CR) and methylene blue (MB). The best catalytic performance in the synthesis of 1‐substituted tetrazoles was achieved using 0.05 g of Cu/bone nanocomposite at 120°C. In addition, under optimal conditions, the absorption bands corresponding to 4‐NP, CR, NS and MB completely disappeared after about 6 min, 3 min, 50 s and 7 s, respectively. The biosynthesis protocol used in the preparation of Cu/bone nanocomposite offers a very attractive area for further research.     

PSO-LSSVM对LIBS定量分析精度的提高

针对土壤定量分析受基体效应影响大，LIBS定量分析精度不佳等问题，采用粒子群算法对LSSVM进行优化，提高模型的精确度。选取Pb Ⅰ 405.78 nm和Cr Ⅰ 425.44 nm作为分析谱线进行分析。采集十二个不同浓度样品的特征光谱，每个浓度样品在不同点采集20组数据，将其中17组数据设为训练集，3组数据设为预测集，用LSSVM和PSO-LSSVM两种方法建立定标模型。对比两种模型的拟合相关系数（R2）、训练集均方根误差（RMSEC）和预测集均方根误差(RMSEP)。由于自吸收效应的影响，随着浓度的增加，预测值逐渐低于实际值，LSSVM定标模型的拟合程度较低，无法达到实验要求，模型性能有待提高。利用粒子群算法对LSSVM的模型参数惩罚系数和核函数参数进行优化，得到最佳的参数组合，Pb元素为(8 096.8, 138.865 7)，Cr元素为(4 908.6, 393.563 5)，用最佳的参数组合构建LSSVM的定标模型。相比于LSSVM，PSO-LSSVM定标模型的精确度更高，Pb和Cr元素的R2提高到了0.982 8和0.985 0，拟合效果明显提升。Pb和Cr元素的训练集均方根误差由0.026 0 Wt%和0.027 2 Wt%下降到0.022 4 Wt%和0.019 1 Wt%，预测集均方根误差由0.101 8 Wt%和0.078 8 Wt% 下降到0.045 8 Wt%和0.042 0 Wt%，模型的稳定性进一步提高。说明PSO-LSSVM算法能够更好地降低土壤基体效应和自吸收效应带来的影响，提高分析结果的精确度与稳定性。     

Formation and accumulation of contact deficiencies in a tunnel segmented lining

Damages observed in tunnels constructed with tunnel boring machines affect the overall quality of the structure and the efficiency of the construction process. Most of these damages are caused by contact deficiencies between segments that are generated by the sum of several tolerances on the shape and on the placement of the lining. Moreover, the imperfection of one ring affects the placement of the following ones, inducing an accumulation mechanism that magnifies the imperfection expected due to the sum of tolerances in a single isolated ring. The overall consideration of these phenomena yields an intricate analysis that must take into account some important probabilistic aspects. This paper explains how the tolerances may evolve into the contact deficiencies found in practice. Initially the types of tolerances and contact deficiencies more likely to affect the structural behavior of the lining are analyzed. A mathematical model is proposed to explain the relation between tolerances and contact deficiencies. The predictions obtained with the model are then compared with the measurements performed in the tunnel of Line 9 in Barcelona. The results obtained reinforce the importance of the model proposed in this study, which quantifies aspects that so far could only be studied qualitatively or on a trial and error basis.     

FACILE REDUCTION OF AROMATIC NITRO COMPOUNDS WITH HYDROIODIC ACID UNDER MICROWAVE IRRADIATION ON SOLID SUPPORT

Reduction of aromatic nitro compounds to amine with hydroiodic acid was revisited. Under microwave irradiation on solid support the reduction proceeded efficiently and rapidly.     

An in silico expert system for the identification of eye irritants

This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient chemicals for eye irritancy potential, which is based upon exclusion rules of five physicochemical properties – molecular weight (MW), hydrophobicity (log P), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA) and polarizability (Pol). These rules were developed using the ADMET Predictor software and a dataset of 917 eye irritant chemicals. The dataset was divided into 826 (90%) chemicals used for training set and 91 (10%) chemicals used for external validation set (every 10th chemical sorted by molecular weight). The sensitivity of these rules for the training and validation sets was 72.3% and 71.4%, respectively. These rules were also validated for their specificity using an external validation set of 2011 non-irritant chemicals to the eye. The specificity for this validation set was revealed as 77.3%. This method facilitates rapid screening and prioritization of data poor chemicals that are unlikely to be tested for eye irritancy in the Draize test.