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首页> 外文期刊>RSC Advances >Spectral normalisation by error minimisation for prediction of conversion in solvent-free catalytic chain transfer polymerisations
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Spectral normalisation by error minimisation for prediction of conversion in solvent-free catalytic chain transfer polymerisations

机译：通过误差最小化对光谱进行归一化，以预测无溶剂催化链转移聚合中的转化率

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摘要

Oligomers are useful chemicals for a number of synthetic and industrial applications. Catalytic chain transfer (CCT) polymerisation has been shown to be an extremely effective methodology for the synthesis of oligomers. Monitoring the conversion of monomer during the production of oligomers can present challenges using conventional analytical techniques such as IR or Raman spectroscopy, due to overlap from spectral features from the retained alkene groups at the chain terminus of the oligomers. This can cause ambiguity when assigning monomeric and oligomeric peaks in the vibrational spectra. In addition to this, such reactions are often carried out in solvent-free systems making the normalisation of spectra difficult. Multivariate analysis offers a useful methodology to quantify monomer conversion using Raman spectroscopy, despite a high double bond content within the polymerisation mixture. Chemometric models were also used to determine suitable points at which to normalise the spectra by a process of error minimisation, since conventional normalisation methods are not effective when Raman bands of constant intensity are not present. A number of partial least squares regression (PLSR) models were used to predict conversion for a range of commercially important monomers, such as methyl methacrylate (MMA), tert-butyl methacrylate (t-BMA) and hydroxyethyl methacrylate (HEMA), with goodness-of-fit R-2 values typically above 0.99, and root-mean-square error of cross-validation (RMSECV) between 1-3% or within 5% of the maximum conversion. Additionally, the ability to detect the concentrations of dimer and trimer formed in the CCT polymerisation of MMA has been demonstrated.
机译：低聚物是许多合成和工业应用中有用的化学物质。催化链转移（CCT）聚合已被证明是一种合成低聚物的极其有效的方法。由于在低聚物链末端保留的烯基的光谱特征存在重叠，因此在常规的分析技术（例如IR或拉曼光谱法）中监控单体的转化会带来挑战。在振动光谱中分配单体峰和低聚物峰时，这可能会引起歧义。除此之外，此类反应通常在无溶剂系统中进行，从而使光谱的归一化变得困难。尽管聚合混合物中双键含量很高，但多变量分析提供了一种有用的方法来使用拉曼光谱法定量单体转化率。化学计量模型也被用来确定通过误差最小化过程对光谱进行归一化的合适点，因为当不存在恒定强度的拉曼谱带时，常规归一化方法无效。许多偏最小二乘回归（PLSR）模型用于预测一系列重要的商业单体的转化，例如甲基丙烯酸甲酯（MMA），甲基丙烯酸叔丁酯（t-BMA）和甲基丙烯酸羟乙酯（HEMA）。拟合R-2值通常高于0.99，并且交叉验证的均方根误差（RMSECV）在最大转化率的1-3％或5％之内。另外，已经证明了检测在MMA的CCT聚合中形成的二聚体和三聚体的浓度的能力。

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