乙烯-醋酸乙烯共聚物改性研究进展

乙烯-醋酸乙烯共聚物(EVA)具有优异的柔韧性、耐腐性、加工性、绿色环保,广泛应用于薄膜、热熔胶、黏合剂、电线电缆等领域。然而,EVA存在粘度低、热性能和力学性能差等缺点,限制了其发展和应用。因此,制备性能优良的新型改性EVA材料成为近年来的研究热点。本综述回顾了近年来关于EVA不同改性方法的研究进展,对化学改性和物理改性两方面进行了重点介绍,分析了不同改性方法的优势和短板,最后对EVA改性方法及研究方向进行展望。     

LDPE／EVA共混体系的结晶行为

本文通过DSC、WAXD、偏光显微镜、DMA等方法,对LDPE/EVA共混体系进行了研究。结果表明,EVA可使LDPE的熔融峰温提高15℃。并在LDPE结晶过程中起稀释剂作用。LDPE/EVA共混体系为非晶相相容。     

含氯聚合物和乙烯-醋酸乙烯酯共聚物共混体系的红外光谱研究

关于氯化聚乙烯(CPE)或聚氯乙烯(PVC)与乙烯—醋酸乙烯酯共聚物(EVA)共混体系相容性的研究,已有不少报道,其中Coleman等人运用FTIR方法研究了含VA45％的EVA与CPE、PVC的共混体系,测定了该体系的低临界共溶温度(LCST)。一般认为,如果VA含量更低,由于EVA本身的结晶,共混体系将变得难以     

EVA-g-VC的结构和动态粘弹性

本文研究了聚乙烯-醋酸乙烯酯(EVA,VAc:14％)与氯乙烯(VC)接枝共聚物(EVA-g-VC)的相结构和分子结构。接枝物EVA-g-VC由游离EVA、均聚PVC和EVA-VC接枝高分子三者组成,EVA呈连续相,PVC呈分散微粒。EVA-g-VC中EVA的含量越高,PVC粒子体积越小。实验结果表明,接枝物中“凝胶”的EVA玻璃化温度,随投料比(VC/EVA)的减小而升高;另外随VC/EVA减小,凝胶中PVC的含量和PVC的分子量也减小。这些结果说明,VC/EVA较小时得到的接枝物中,EVA上VC接枝点的数目较多,而PVC接枝链的长度较短。EVA-VC是不相容两相——EVA和PVC的“粘着剂”,其作用表现在:VC/EVA越小,接枝物中EVA和PVC的玻璃化温度越靠近。     

抗氧化剂活性的电子顺磁共振（EPR）研究

抗氧化剂在化工、食品以及生命科学等领域具有重要的作用,近年来更是由于其在解决与人类健康相关问题方面所起到的重要作用而受到广泛重视。本综述较为详细地介绍了利用电子顺磁共振（EPR）技术测定抗氧化剂活性的原理和方法,系统总结了近年来在天然抗氧化剂以及合成抗氧化剂化学活性方面的EPR研究最新进展,特别是定量测定表征抗氧化活性...     

液相色谱－串联质谱法测定食品接触橡胶密封垫圈中7种抗氧化剂

采用液相色谱－串联质谱技术建立了食品接触橡胶密封垫圈中7种抗氧化剂（抗氧化剂ZKF、抗氧化剂259、抗氧化剂1035、抗氧化剂425、抗氧化剂2246、抗氧化剂1520、抗氧化剂565）的同时检测方法。样品经甲醇微波提取,Acquity UPLC BEH C18（100 mm × 2.1 mm,1.7 μm）色谱柱分离,以乙腈－乙酸铵水溶液（5 mmol/L）为流动相进行梯度洗脱,在正、负离子模式下测定。结果显示,7种抗氧化剂在0.01～0.5 mg/L质量浓度范围内线性良好,相关系数（r2）均大于0.999。方法检出限（MLOD）为0.005～0.125 mg/kg,方法定量下限（MLOQ）为0.02～0.40 mg/kg,平均加标回收率为73.1%～120%,相对标准偏差（RSD,n = 6）为0.70%～9.3%。该方法前处理简单,15 min内可同时完成7种抗氧化剂的检测,且检出限较低,可满足食品接触橡胶材料及制品中抗氧化剂的检测需要。     

倍半硅氧烷/改性乙烯-乙酸乙烯酯共聚物复配降凝剂对蜡油倾点的影响

乙烯-乙酸乙烯酯共聚物(EVA)是原油常用的降凝剂(PPDs),但其分子结构相对比较单一,对部分油品降凝效果不佳。 为了提高EVA的降凝效果,使用硬脂酰氯与羟基化的EVA直接反应的方法制备烷基长链接枝改性EVA,并与带有烷基长链的倍半硅氧烷(SS)纳米粒子进行复配。 研究了改性EVA和SS复配降凝剂对蜡油的降凝效果和降凝机理。 结果表明:复配降凝剂为蜡提供晶核,使蜡晶变小并降低蜡的沉淀量,导致蜡油中形成的蜡晶难以搭接在一起,形成了松散的结构,当复配质量比m(EVA-g)∶m(SS-L)=1∶2时,在蜡油中的质量分数为0.1%时,蜡油倾点降低了25 ℃。     

多组份聚丙烯共混物的微观结构及共混纤维的染色性能

研究了PP GPET、PP GPET EVA、PP GPET EVA PS共混物的结构和性能 .研究表明 ,PP GPET体系属于非相容共混体系 ,共混物呈典型海岛型两相结构 ,EVA的加入可以改善体系相容性 ;共混物的结晶度比纯聚丙烯低 ,PS有增大共混物晶粒尺寸的作用 ;改性聚丙烯纤维的染色性有明显提高 ,用分散染料E EX可染成深蓝色     

反相气相色谱法研究聚合物共混物的相溶性

 用反相气相色谱法测定了聚氯乙烯(PVC)／ 乙烯-醋酸乙烯共聚物(EVA)共混体系中分子间表观热力学相互作用参数χ′23,并以χ′23 为判定依据,研究了共混物的相溶性。 初步探讨了共混物的组成、聚合物分子 链结 构、温度与χ′23的关系以及探针分子性质 对χ′23参数的影响。结果表明：χ[ HT6〗′23值能够准确有效地判定PVC与EVA共混物的 相溶性,醋酸乙烯质量分数低的EVA与PVC的共混物是热力学不相溶的;而醋酸乙烯质量 分数中等的EVA与PVC的共混物则具有部分相溶性。结果与其它方法得到的结论是一致的 。     

PVC/EVA-g-VC共混物的形态与加工条件和性能的关系

PVC/EVA(-14)及 PVC/EVA(-14)-g-VC的等速升温Brabender塑化曲线上有两个扭矩峰,分别标志着EVA和PVC的塑化,对应着共混形态经历的三个变化:(1)EVA塑化——PVC粉粒破碎;(2)EVA呈连续相——PVC集结粒子解体;(3)EVA呈分散相——PVC初级粒子熔化。聚合投料比(VC/EVA)越小,EVA-g-VC的塑化温度和熔体粘性越高,两个扭矩峰靠得越近。实验结果表明,EVA-g-VC与EVA相比,不仅与PVC有更好的相容性,而且有较好的均匀可混性。冲击强度的测定结果表明:EVA连续网——PVC初级粒子结构具有较高的冲击强度。VC/EVA较小时所得EVA-g-VC改性的PVC可在较宽的加工温度范围保持EVA连续网结构和较高的冲击强度。     

本征值方法在抗氧化剂定量构效关系研究中的应用

Antioxidants are of great interest because of their involvement in many important biological and industrial processes. It is meaningful to study their structure-antioxidant activity relationship (SAAR) and design novel, efficient and low-toxicity antioxidant. In this paper, Eigen Value Analysis (EVA), a 3-dimensional quantitative structure activity relationship (3-D QSAR) method, was employed to study antioxidant SAAR. Significant relational models were obtained with all the PLS cross-validate qcv^2 values being larger than 0.5, meaning that the models have sound predictive power. Compared with other QSAR methods, EVA possesses several advantages, especially that it does not need alignment. It should be believed that EVA will be an efficient approach to SAAR.     

基于分子指纹对黄酮类化合物及其抗DPPH自由基活性的QSAR分析

应用定量构效关系（Quantitative structure activity relationship, QSAR）研究阐明黄酮类化合物（Flavonoid compounds, FCs）的子结构指纹（Substructure fingerprint）与1,1-二苯基-2-三硝基苯肼（1,1-Diphenyl-2-picrylhydrazyl, DPPH）自由基清除能力之间的关系,从而指导高效抗氧化物质的设计和发现。在PubMed数据库中收集77个具有明确抗氧化活性的黄酮类化合物,而在ChEMBL数据库中收集86个无抗DPPH活性的黄酮类化合物。这163个黄酮类化合物的子结构指纹由PubChem系统生成,然后通过卡方检验筛选出与黄酮类化合物的抗氧化活性显著相关的分子指纹,最后通过判别分析建立预测QSAR模型,并采用回代法和交叉验证法对已建立的模型进行准确性和稳健性的验证。结果表明,黄酮类化合物抗DPPH自由基活性与ESSSR环的计数、简单相邻原子的类型和简单的SMARTS模式等因素有关。此外,所建立的QSAR模型能较好地预测黄酮类化合物的DPPH自由基清除活性,可用于评价候选抗氧化剂的潜力。     

Spectroscopic QSAR methods and self-organizing molecular field analysis for relating molecular structure and estrogenic activity

The performance of three "spectroscopic" quantitative structure-activity relationship (QSAR) methods (eigenvalue (EVA), electronic eigenvalue (EEVA), and comparative spectra analysis (CoSA)) for relating molecular structure and estrogenic activity are critically evaluated. The methods were tested with respect to the relative binding affinities (RBA) of a diverse set of 36 estrogens previously examined in detail by the comparative molecular field analysis method. The CoSA method with (13)C chemical shifts appears to provide a predictive QSAR model for this data set. EEVA (i.e., molecular orbital energy in this context) is a borderline case, whereas the performances of EVA (i.e., vibrational normal mode) and CoSA with (1)H shifts are substandard and only semiquantitative. The CoSA method with (13)C chemical shifts provides an alternative and supplement to conventional 3D QSAR methods for rationalizing and predicting the estrogenic activity of molecules. If CoSA is to be applied to large data sets, however, it is desirable that the chemical shifts are available from common databases or, alternatively, that they can be estimated with sufficient accuracy using fast prediction schemes. Calculations of NMR chemical shifts by quantum mechanical methods, as in this case study, seem to be too time-consuming at this moment, but the situation is changing rapidly. An inherent shortcoming common to all spectroscopic QSAR methods is that they cannot take the chirality of molecules into account, at least as formulated at present. Moreover, the symmetry of molecules may cause additional problems. There are three pairs of enantiomers and nine symmetric (C(2) or C(2)(v)) molecules present in the data set, so that the predictive ability of full 3D QSAR methods is expected to be better than that of spectroscopic methods. This is demonstrated with SOMFA (self-organizing molecular field analysis). In general, the use of external test sets with randomized data is encouraged as a validation tool in QSAR studies.     

Development of models for prediction of the antioxidant activity of derivatives of natural compounds

Antioxidants are important for maintaining the appropriate balance between oxidizing and reducing species in the body and thus preventing oxidative stress. Many natural compounds are being screened for their possible antioxidant activity. It was found that a mushroom pigment Norbadione A, which is a pulvinic acid derivative, shows an antioxidant activity; the same was found for other pulvinic acid derivatives and structurally related coumarines. Based on the results of in vitro studies performed on these compounds as a part of this study quantitative structure–activity relationship (QSAR) predictive models were constructed using multiple linear regression, counter-propagation artificial neural networks and support vector regression (SVR). The models have been developed in accordance with current QSAR guidelines, including the assessment of the models applicability domains. A new approach for the graphical evaluation of the applicability domain for SVR models is suggested. The developed models show sufficient predictive abilities for the screening of virtual libraries for new potential antioxidants.     

Eigenvalue analysis of peroxisome proliferator-activated receptor gamma agonists

Eigenvalue analysis (EVA) was conducted on a series of potent agonists of peroxisome proliferator-activated receptor gamma (PPARgamma). Predictive EVA quantitative structure-activity relationship (QSAR) models were established using the SYBYL package, which had conventional r2 and cross-validated coefficient (q2) values up to 0.920 and 0.587 for the AM1 method and 0.863 and 0.586 for the PM3 method, respectively. These models were validated by a test set containing 18 compounds. The capability to predict by these two models for PPARgamma agonists, with the best predictive r2pred value of 0.614 for AM1 and 0.822 for PM3 methods, set a successful example for applying a similar approach in building QSAR models for PPARalpha and -delta that could potentially offer a new opportunity in the design of novel PPAR modulators.