橡胶的老化机理及老化行为的研究进展

橡胶制品在使用过程中会受到热、氧、热氧、光、辐照及机械应力等因素影响,会产生降解、交联等老化行为.不同种类橡胶因其使用环境和要求不同,其老化机理也各不相同.本文介绍了橡胶老化的化学机理、研究方法及预测方法和模型,综述了近年来不同品种的橡胶及橡胶/橡胶并用体系的老化行为研究进展.通过对橡胶老化行为和机理的综述,有助于系统...     

复杂条件下高分子材料老化规律、寿命预测与防治研究新进展

复杂条件下有机高分子材料的老化、寿命预测和防治研究对满足相关行业发展的迫切需求,实现节能减排、环境保护及可持续发展等战略目标具有重大意义。本文重点综述了近年来针对聚烯烃、工程塑料、橡胶、涂料等大宗高分子材料在我国复杂大气环境中的自然老化及人工模拟加速老化研究的新进展,对材料老化失效基本规律和分子机理、老化数据库的建立及老化分级图谱的绘制进行了介绍,探讨了户外自然环境和人工模拟环境下材料老化失效规律的对应关系、服役寿命理论的预测模型及失效防治延寿新方法,并对其中存在的问题及下一步发展方向进行了展望。     

废旧橡胶改性沥青的老化研究现状

废旧橡胶改性沥青可显著延长路面寿命。本文综述了国内外废旧橡胶改性沥青的热氧老化、紫外老化和水老化行为,使用宏观性能和微观结构变化分析了橡胶改性沥青老化特点。橡胶改性沥青在热、氧、紫外、水的作用下发生老化,软化点升高,针入度降低,羰基含量增加。特别是光、氧老化对经过水老化后的橡胶改性沥青老化作用尤为明显,水溶解了部分老化产物促进了沥青的老化。目前对于橡胶改性沥青老化过程中胶粉、沥青的结构性能变化和其环境耦合因素对橡胶改性沥青的老化研究及分析手段还很少,在今后的研究中须引起重视。     

丁苯橡胶寿命预测

对丁苯橡胶进行了海水加速老化实验,通过力学性能测试考察了断裂伸长率和拉伸强度随老化时间的变化,并用两种方法数学模型法和时温叠加法预测了丁苯橡胶的使用寿命,其使用寿命分别为8.15年和5.34年。以老化系数为参数,两种方法的老化表观活化能分别为33.64KJ·mol~(-1)和69.20KJ·mol~(-1)。     

导热橡胶研究进展北大核心CSCD

橡胶是热的不良导体,但在实际应用中,许多领域需要橡胶具备一定的导热性能,以满足使用要求。因此,人们对于橡胶导热的研究越来越重视。本文概述了导热填料种类、形状、粒径、界面结合状态以及填料在橡胶基体中的分散性等因素对橡胶复合材料导热性能的影响,从实验角度分析了填充型导热橡胶的导热机理,阐述了目前研究中人们所建立的各种物理和数学模型,浅析了这些模型的优缺点及适用范围,并将其应用于橡胶材料导热性能的预测,最后介绍了导热橡胶的应用领域,以及导热橡胶未来的发展方向。     

导热橡胶研究进展

橡胶是热的不良导体,但在实际应用中,许多领域需要橡胶具备一定的导热性能,以满足使用要求。因此,人们对于橡胶导热的研究越来越重视。本文概述了导热填料种类、形状、粒径、界面结合状态以及填料在橡胶基体中的分散性等因素对橡胶复合材料导热性能的影响,从实验角度分析了填充型导热橡胶的导热机理,阐述了目前研究中人们所建立的各种物理和数学模型,浅析了这些模型的优缺点及适用范围,并将其应用于橡胶材料导热性能的预测,最后介绍了导热橡胶的应用领域,以及导热橡胶未来的发展方向。     

高效液相色谱测定橡胶老化过程中防老剂残余量

在橡胶聚合过程中加入抗氧剂可以抑制生胶干燥和贮藏过程中的氧化作用,从而延长其使用寿命。定量测定抗氧剂在橡胶老化过程中的残余量可衡量其性能的优劣。但橡胶中组分较多,化学分离极为困难。国外曾报道用高效液相色谱测定酚类抗氧剂工业副产物,氯丁胶老化过程中抗氧剂的残余量还未见报道。 我们用HPLC法,以反相柱,甲醇、氯仿、水为洗提液,测定了甲叉-4426-S,KY-405,酚噻嗪在橡胶中不同老化时间的残余量,得到与其它物理检测相吻合的结果。     

国内集成橡胶SIBR的发展现状及前景展望

集成橡胶(SIBR)是橡胶新产品,主要用于制备高性能轮胎的胎面胶料,由于其安全、高性能、长寿命、经济性的特点,因此也是制备绿色轮胎的理想材料。集成橡胶研究是大分子设计研究领域的前沿项目,阴离子聚合已成功应用于控制集成橡胶结构单元的构象与序列分布。本文介绍了集成橡胶苯乙烯-异戊二烯-丁二烯三元共聚物的特点、分类及制备方法、技术研究现状、产业背景及原料市场状况等,结合国内现有研究基础和市场状况指出了其在我国的开发和利用前景,并对未来发展提出了有益的建议与指导。     

NEPE推进剂贮存寿命预估方法的比较

在密闭条件下,对两种NEPE推进剂(NA -1和NP -1)进行了不同温度(55、65、75、85、95℃)的加速老化.跟踪测定了质量损失、拉伸强度和有效安定剂含量.分别以质量损失和拉伸强度为变化参量,以质量损失1％所需时间(τ)作为安全贮存寿命的临界点,以拉伸强度下降至0.5 MPa所需时间(τ')作为力学老化寿命的...     

PP-SBS共混物光氧化降解的研究

和橡胶类树脂进行共混是聚丙烯(PP)改性的重要途径之一,共混有橡胶组分的PP改性树脂在其力学性能获得改进的同时,其老化性能必然也有一定的变化,我们用红外光谱方法研究了聚丙烯和苯乙烯-丁二烯星型嵌段共聚物的共混物(PP-SBS)的光氧化降解行为。     

A representation transfer learning approach for enhanced prediction of growth hormone binding proteins

Growth hormone binding proteins (GHBPs) are soluble proteins that play an important role in the modulation of signaling pathways pertaining to growth hormones. GHBPs are selective and bind non-covalently with growth hormones, but their functions are still not fully understood. Identification and characterization of GHBPs are the preliminary steps for understanding their roles in various cellular processes. As wet lab based experimental methods involve high cost and labor, computational methods can facilitate in narrowing down the search space of putative GHBPs. Performance of machine learning algorithms largely depends on the quality of features that it feeds on. Informative and non-redundant features generally result in enhanced performance and for this purpose feature selection algorithms are commonly used. In the present work, a novel representation transfer learning approach is presented for prediction of GHBPs. For their accurate prediction, deep autoencoder based features were extracted and subsequently SMO-PolyK classifier is trained. The prediction model is evaluated by both leave one out cross validation (LOOCV) and hold out independent testing set. On LOOCV, the prediction model achieved 89.8%% accuracy, with 89.4% sensitivity and 90.2% specificity and accuracy of 93.5%, sensitivity of 90.2% and specificity of 96.8% is attained on the hold out testing set. Further a comparison was made between the full set of sequence-based features, top performing sequence features extracted using feature selection algorithm, deep autoencoder based features and generalized low rank model based features on the prediction accuracy. Principal component analysis of the representative features along with t-sne visualization demonstrated the effectiveness of deep features in prediction of GHBPs. The present method is robust and accurate and may complement other wet lab based methods for identification of novel GHBPs.     

Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton-viscose textiles

In this work, different approaches for variable selection are studied in the context of near-infrared (NIR) multivariate calibration of textile. First, a model-based regression method is proposed. It consists in genetic algorithm optimisation combined with partial least squares regression (GA-PLS). The second approach is a relevance measure of spectral variables based on mutual information (MI), which can be performed independently of any given regression model. As MI makes no assumption on the relationship between X and Y, non-linear methods such as feed-forward artificial neural network (ANN) are thus encouraged for modelling in a prediction context (MI-ANN). GA-PLS and MI-ANN models are developed for NIR quantitative prediction of cotton content in cotton-viscose textile samples. The results are compared to full-spectrum (480 variables) PLS model (FS-PLS). The model requires 11 latent variables and yielded a 3.74% RMS prediction error in the range 0-100%. GA-PLS provides more robust model based on 120 variables and slightly enhanced prediction performance (3.44% RMS error). Considering MI variable selection procedure, great improvement can be obtained as 12 variables only are retained. On the basis of these variables, a 12 inputs ANN model is trained and the corresponding prediction error is 3.43% RMS error.     

Prediction of viscosities of hydrocarbon mixtures

A new model for prediction of the viscosities of hydrocarbons including oil and gas mixtures is presented. The model is based on the principle of corresponding states with methane and decane as reference components. The viscosity of a given component or mixture is determined from the reduced viscosities of the reference components using the molecular weight as an interpolation parameter.

The model has been used for prediction of viscosities of both pure components and mixtures over large pressure ranges and for reduced temperatures above 0.476. The results are in good agreement with the experimental data. The new model compares favorably with earlier published methods, which use only one reference component.

Finally, the model has been tested on data for 6 oil mixtures from the North Sea. The mean deviation based on 34 experimental points was 6.4 %.     

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN. It employs statistical data analysis to enhance the prediction accuracy. The current work proposed a protein prediction approach from protein images based on Hidden Markov Model and Chapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein images’ binarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently, two counting algorithms, namely the Flood fill and Warshall are employed to classify the protein structures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classified structures for predicting the protein structure. The execution time and algorithmic performances are measured to evaluate the primary, secondary and tertiary protein structure prediction.     

Effect of example weights on prediction of protein-protein interactions

Protein-protein interactions (PPIs) prediction is an important issue in biology. Recently many computational methods have been proposed to determine PPIs. However, there is no golden standard dataset for these methods now. Furthermore, there exists different quality among training examples and the quality is always ignored by the current methods. In the condition of low-quality examples, the system should tolerate the data noise. Example weighting strategy is used in this paper to build a robust system and solve the problem of data noise. Training examples are investigated and a new example selecting/using strategy is proposed. Training example weighting method based on confidence is proposed. Different weight setting strategies are discussed and the corresponding results are given in the experiment. A new model integrating example weighting strategy, attraction-repulsion (AR) weight model, is proposed. Experimental results on Saccharomyces cerevisiae demonstrate that the new model outperforms the original AR model in the ROC score measure by over 8%. Furthermore, the example weighting strategy is applied to another domain-based PPIs prediction method, maximum likelihood estimation (MLE) method, and the modified MLE method obtains better performance than the original MLE method. At same time, our examples weighting strategy can be applied to any other training example based PPIs prediction methods.     

Prediction and rationalization of protein pKa values using QM and QM/MM methods

We describe the development and application of a computational method for the prediction and rationalization of pKa values of ionizable residues in proteins, based on ab initio quantum mechanics (QM) and the effective fragment potential (EFPs) method (a hybrid QM/MM method). The theoretical developments include (1) a covalent boundary method based on frozen localized orbitals, (2) divide-and-conquer methods for the ab initio computation of protein EFPs consisting of multipoles up to octupoles and dipole polarizability tensors, (3) a method for computing vibrational free energies for a localized molecular region, and (4) solutions of the polarized continuum model of bulk solvation equations for protein-sized systems. The QM-based pKa prediction method is one of the most accurate methods currently available and can be used in cases where other pKa prediction methods fail. Preliminary analysis of the computed results indicate that many pKa values (1) are primarily determined by hydrogen bonds rather than long-range charge-charge interactions and (2) are relatively insensitive to large-scale dynamical fluctuations of the protein structure.     

Determination of reliable fatigue life predictors for magnetorheological elastomers under dynamic equi-biaxial loading

Fatigue life prediction is of great significance in ensuring magnetorheological elastomer (MRE) based rubber components exhibit reliability and do not compromise safety under complex loading, and this necessitates the development of plausible fatigue life predictors for MREs. In this research, silicone rubber based MREs were fabricated by incorporating soft carbonyl iron magnetic particles. Equi-biaxial fatigue behaviour of the fabricated MREs was investigated by using the bubble inflation method. The relationship between fatigue life and maximum engineering stress, maximum strain and strain energy density were studied. The results showed that maximum engineering stress and stored energy density can be used as reliable fatigue life predictors for SR based MREs when they are subjected to dynamic equi-biaxial loading. General equations based on maximum engineering stress and strain energy density were developed for fatigue life prediction of MREs.     

基于深度学习的保留时间预测方法的研究进展及应用

在基于液相色谱-质谱联用的蛋白质组学研究中,肽段的保留时间作为有效区分不同肽段的特征参数,可以根据肽段自身的序列等信息对其进行预测。使用预测得到的保留时间辅助质谱数据鉴定肽段序列可以提高鉴定的准确性,因此对保留时间预测的工作一直受到领域内的广泛关注。传统的保留时间预测方法通常是根据氨基酸序列计算肽段的理化性质,进而计算肽段在特定色谱条件下的保留时间。近年来,深度学习方法取得了极大的进展,在蛋白质组学研究中发挥着越来越重要的作用。目前已发展出了多种基于深度学习的保留时间预测方法,与传统的保留时间预测方法相比有着更高的准确度,易于跨平台使用,并且能对修饰肽段的保留时间进行预测。但对某些复杂的修饰,如糖基化修饰等的预测结果还不够准确。如何进一步提高对修饰肽段预测的准确性是基于深度学习的保留时间预测方法的重要研究方向。这些预测的保留时间被应用于肽段鉴定的质量控制和方法评估,以及与预测的二级质谱谱图结合,建立模拟谱图库等方面。该文综述了深度学习方法在保留时间预测领域的最新研究进展以及应用成果,同时对其发展趋势和未来的应用方向进行了展望,以期为保留时间预测研究以及蛋白质组鉴定工作提供参考。     

LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions

The interactions between miRNAs and long non-coding RNAs (lncRNAs) are subject to intensive recent studies due to its critical role in gene regulations. Computational prediction of lncRNA-miRNA interactions has become a popular alternative strategy to the experimental methods for identification of underlying interactions. It is desirable to develop the machine learning-based models for prediction of lncRNA-miRNA based on the experimentally validated interactions between lncRNAs and miRNAs. The accuracy and robustness of existing models based on machine learning techniques are subject to further improvement.Considering that the attributes of lncRNA and miRNA contribute key importance in the interaction between these two RNAs, a deep learning model, named LMI-DForest, is proposed here by combining the deep forest and autoencoder strategies. Systematic comparison on the experiment validated datasets for lncRNA-miRNA interaction datasets demonstrates that the proposed method consistently shows superior performance over the other machine learning models in the lncRNA-miRNA interaction prediction.