Electrostatic layer-by-layer assembled carbon nanotube multilayer film and its electrocatalytic activity for O2 reduction

Multilayer films of shortened multiwalled carbon nanotubes (MWNTs) are homogeneously and stably assembled on glassy carbon electrodes with the layer-by-layer (LBL) method, based on electrostatic interaction of positively charged poly(diallyldimethylammonium chloride) and negatively charged and shortened MWNTs. The film assembly and electrochemical property as well as the electrocatalytic activity toward O2 reduction of the MWNT multilayer film are studied. Scanning electron microscopy, the quartz crystal microbalance technique, ultraviolet-visible-near-infrared spectroscopy, and cyclic voltammetry are used for characterization of film assembly. Experimental results revealed that film growth is uniform, almost with the same coverage of the MWNTs in each layer, and that the assembled MWNTs are mainly in the form of small bundles or single tubes on the electrodes. Electrochemical studies indicate that the LBL assembled MWNT films possess a remarkable electrocatalytic activity toward O2 reduction in alkaline media. This property, combined with the well-dispersed, porous and conductive features of the MWNT film illustrated with the LBL method, suggests the potential application of the MWNT film for constructing an efficient alkaline air electrode for energy conversions.     

水体透射光谱结合主成分分析(PCA )改进化学需氧量(COD)含量估算研究

化学需氧量（Chemical Oxygen Demand,COD）是水体有机污染的一项重要指标,化学需氧量越高,表示水污染程度越严重。 为了解决传统的COD测量方法耗时较长,不利于快速、实时地获取水体中COD的信息等问题。本文提出了基于透射光谱测量结合主成分分析(Principal Component Analysis, PCA)改进水体COD含量估算模型。具体的,采集100组COD水体光谱信息,分别使用3种不同的高光谱数据预处理方法对光谱数据进行预处理,分析不同预处理方法对模型精度的影响,并基于不同的预处理方法分别建立高斯过程回归模型(Gaussian Process Regression, GPR)和BP神经网络模型,分析不同预处理方法对模型精度的影响;并对各模型结合PCA数据降维方法进行模型的改进,通过比较模型的精度选择最优模型进行水体COD含量的检测。结果显示,相比于原始光谱数据建立的GPR模型和BP神经网络模型,数据预处理后的模型精度明显提升;且结合PCA对预处理后的数据进一步降维处理后,模型精度得到了进一步的提升。其中,基于标准正态变量变换特征结合PCA改进BP神经网络模型基于PCA改进的BP神经网络模型R^2高达0.9940,均方根误差RMSE为0.022540。证明了基于PCA改进的BP神经网络数据降维方法对预处理后的光谱数据进行降维处理,有利于去除光谱中的冗余信息,提取特征信息,可以实现高光谱检测方法可以实现COD含量估算模型的优化,从而为传统COD测量方法存在的问题提出了一种新的解决思路。     

Visible Light-Driven Alkyne-Grafted Ethylene-Bridged Azobenzene Chromophores for Photothermal Utilization

Molecular photoswitches are considered to be important candidates in the field of solar energy storage due to their sensitive and reversible bidirectional optical response. Nevertheless, it is still a daunting challenge to design a molecular photoswitch to improve the low solar spectrum utilization and quantum yields while achieving charging and discharging of heat without solvent assistance. Herein, a series of visible-light-driven ethylene-bridged azobenzene (b-Azo) chromophores with different alkyne substituents which can undergo isomerization reactions promoted in both directions by visible light are reported. Their visible light responsiveness improves their solar spectrum utilization while also having high quantum yields. In addition, as the compounds are liquids, there is no need to dissolve the compounds in order to exploit this switching. The photoisomerization of b-Azo can be adjusted by alkyne-related substituents, and hexyne-substituted b-Azo is able to store and release photothermal energy with a high density of 106.1 J·g⁻¹, and can achieve a temperature increase of 1.8 °C at a low temperature of −1 °C.     

地铁沿线建筑内噪声多维度感受影响机制

随着城市铁路系统的快速发展,铁路沿线建筑内人群对地铁运行产生噪声的抱怨逐渐增多,为控制室内地铁噪声对人群所产生的负面影响,有必要对地铁噪声产生的多维度负面情绪进行评估。采集了79名受试者对建筑内地铁噪声的多维度负面感受(压抑感,不舒适度和不满意度),基于偏最小二乘法分析了主客观影响因素对多维度负面感受的作用机制,并比较了多维度负面感受在反映地铁噪声影响上的差异。结果表明,多维度负面感受主要取决于主观响度和声学参数,活动干扰度、地铁噪声的敏感度、厌烦度和适应性的影响明显更弱;3个多维度负面感受中,相比于压抑感,不舒适度和不满意度受主观响度的影响轻微更显著,在相同的主观响度感受下,不满意度等级最高。 该文可以为地铁沿线建筑物内噪声负面感受的评价和改善提供参考。     

声波团聚净化尿素造粒塔尾气实验研究*

尿素造粒塔大多采用水洗工艺处理尾气粉尘,存在较为严重的细颗粒粉尘溢出和水汽拖尾问题,因此深入探究声波团聚技术在造粒塔尾气处理方面的应用。通过实验研究了声波与喷雾对除尘效率的影响,发现声波单独作用时在3500 Hz达到最佳除尘效率,为74.49%,协同喷雾时2600 Hz即可实现相近的除尘效率,为73.83%。进一步探究其中机理,采用以粒径测试为依据的团聚效率作为评价指标,结果显示,2600 Hz声波与喷雾单独作用时团聚效率分别为30.37%和28.82%,协同作用时团聚效率大幅提升至57.95%。该研究可为后续造粒塔工程改造提供理论与数据支持。     

Synthesis of PNIPAAm-g-P4VP Microgel as Draw Agent in Forward Osmosis by RAFT Polymerization and Reverse Suspension Polymerization to Improve Water Flux

Microgels have unique and versatile properties allowing their use in forward osmosis areas as a draw agent. In this contribution, poly(4-vinylpyridine) (P4VP) was synthesized via RAFT polymerization and then grafted to a poly(N-Isopropylacrylamide) (PNIPAAm) crosslinking network by reverse suspension polymerization. P4VP was successfully obtained by the quasiliving polymerization with the result of nuclear magnetic resonance and gel permeation chromatography characterization. The particle size and particle size distribution of the PNIPAAm-g-P4VP microgels containing 0, 5, 10, 15 and 20 wt% P4VP were measured by means of a laser particle size analyzer. It was found that all the microgels were of micrometer scale and the particle size was increased with the P4VP load. Inter/intra-molecular-specific interactions, i.e., hydrogen bond interactions were then investigated by Fourier infrared spectroscopy. In addition, the water flux measurements showed that all the PNIPAAm-g-P4VP microgels can draw water more effectively than a blank PNIPAAm microgel. For the copolymer microgel incorporating 20 wt% P4VP, the water flux was measured to be 7.48 L∙m⁻²∙h⁻¹.     

Facile Synthesis of Tricyclic 1,2,4-Oxadiazolines-Fused Tetrahydro-Isoquinolines from Oxime Chlorides with 3,4-Dihydroisoquinoline Imines

A mild and efficient strategy for the synthesis of tricyclic 1,2,4-oxadiazolines-fused tetrahydro-isoquinolines derivatives via [3 + 2] cycloaddition reaction is reported. The reactions provided the functionalized tricyclic 1,2,4-oxadiazolines in high yields (up to 96%). This protocol is simple and easy to handle. Moreover, a gram-scale experiment further highlights the synthetic utility. The chemical structure of the product was determined by X-ray single-crystal structure analysis. A possible mechanism for this transformation is proposed to explain the reaction process.     

Rutin Inhibits Ox-LDL-Mediated Macrophage Inflammation and Foam Cell Formation by Inducing Autophagy and Modulating PI3K/ATK Signaling

Atherosclerosis (AS) is one of the leading causes of death among the elderly, and is primarily caused by foam cell generation and macrophage inflammation. Rutin is an anti-inflammatory, anti-oxidant, anti-allergic, and antiviral flavonoid molecule, known to have anti-atherosclerotic and autophagy-inducing properties, but its biological mechanism remains poorly understood. In this study, we uncovered that rutin could suppress the generation of inflammatory factors and reactive oxygen species (ROS) in ox-LDL-induced M2 macrophages and enhance their polarization. Moreover, rutin could decrease foam cell production, as shown by oil red O staining. In addition, rutin could increase the number of autophagosomes and the LC3II/I ratio, while lowering p62 expression. Furthermore, rutin could significantly inhibit the PI3K/ATK signaling pathway. In summary, rutin inhibits ox-LDL-mediated macrophage inflammation and foam cell formation by inducing autophagy and modulating PI3K/ATK signaling, showing potential in treating atherosclerosis.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.