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Selective product crystallization as a new catalyst recycling tool in homogeneous transition metal catalysis exemplified by the conversion of oleochemicals

Selective product crystallization as a new catalyst recycling tool in homogeneous transition metal catalysis exemplified by the conversion of oleochemicals
选择性产物结晶作为均相过渡金属催化中新的催化剂回收工具,以油脂化学品的转化为例
批准号:
424535516
负责人:
Dr. Thomas Seidensticker
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2019
资助国家:
德国
项目状态:
已结题
起止时间:
2018-12-31 至 2022-12-31

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中文摘要
翻译
在全球原材料变化的时代,新的和可持续的生物聚合物方法是人们关注的焦点。特别是,天然脂肪和油的原料具有结构特性,这使得它们对生产某些聚合物很感兴趣,而这些聚合物以前只能通过石油化学方法获得。在均相过渡金属催化剂的帮助下,这些油脂化学物质转化为必要的聚合物前体的效率特别高。这种形式的催化的许多优点,如高选择性,温和的反应条件和反应控制在均相,抵消了下游产品和催化剂的有效分离的关键任务。然而,分离对于生态和经济可行的应用至关重要:一方面,获得无重金属污染的产品。另一方面,使通常昂贵的贵金属催化剂的回收利用成为可能。然而,对于通过均相催化剂从油脂化学品中生产的一类非常有趣的聚合物前体,这一任务尚未得到解决。原则上,均质混合物的分离过程是基于所含物质的不同分子性质,例如沸点(蒸馏)、相对溶解度(萃取)或分子大小(纳滤)。然而,对于所讨论的物质类别,在既定的分离工艺中,催化剂和产物在其性质上没有足够的差异,这就是为什么没有达到预期的分离效率。因此,虽然已经显示出巨大的兴趣和潜力,但由于未解决的分离任务,目前无法扩展到工业规模。有趣的是,由于熔点不同而产生的分离还没有被考虑。因此,本研究项目的目的是首次使用结晶作为均相催化剂的分离和回收过程,从而通过模型反应从根本上证明其在现有分离任务中的潜力。为了使各自的反应和结晶参数最佳地相互适应并尽早发现相互作用,需要一种跨学科的方法。这样,均相催化和冷却结晶的结合成功地将产物作为纯固体从反应混合物中选择性地分离出来。同时,均相催化剂保留在溶液中,固/液分离完成后可重复使用,实现了其循环利用。这些基础研究的长期目标是将这些有价值的、可持续的聚合物前体的均相催化合成整合到一个有效的、工业上适用的过程中。
英文摘要
New and sustainable ways to biopolymers are in focus of attention in times of global raw material change. In particular, raw materials from natural fats and oils have structural properties that make them interesting for the production of certain polymers that were previously petrochemically accessible only. The conversion of these oleochemicals into the necessary polymer precursors is particularly efficient with the aid of homogeneous transition metal catalysts. The many advantages of this form of catalysis, such as high selectivity, mild reaction conditions and reaction control in a homogeneous phase, are countered by the critical task of efficient separation of products and catalyst in the downstream. However, separation is crucial with regard to an ecological and economical viable application: On the one hand, to obtain products free of heavy metal contamination. On the other hand, to enable recycling of the usually expensive precious metal catalysts. However, this task has not yet been solved for the very interesting class of polymer precursors from oleochemicals produced via homogeneous catalysts. In principle, processes for the separation of homogeneous mixtures are based on the different molecular properties of the substances contained, e.g. boiling points (distillation), relative solubilities (extraction), or molecular sizes (nanofiltration). For the discussed substance class, however, there are no sufficient differences between catalyst and product in their properties used in the established separation processes, which is why the desired separation efficiency is not achieved. As a result, scaling to industrial scale presently fails due to the unsolved separation task, although great interest and potential has already been shown. Interestingly, a separation due to different melting points has not yet been considered.The aim of the present research project is therefore to use crystallization for the first time as a separation and recycling process for homogeneous catalysts and thus to fundamentally demonstrate its potential for the existing separation task by means of a model reaction. An interdisciplinary approach is required in order to optimally adapt the respective parameters of reaction and crystallization to each other and to detect mutual interactions as early as possible. By doing so, the combination of homogeneous catalysis and cooling crystallization succeeds to selectively separate the product as a pure solid from the reaction mixture. At the same time, the homogeneous catalyst remains in solution and can be reused after solid/liquid separation has been completed, thus achieving its recycling. These fundamental investigations serve the long-term goal of integrating the homogeneous catalytic synthesis of these valuable, sustainable polymer precursors into an efficient and industrially applicable process.
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M-矩阵(张量)最小特征值估计及其相关问题研究
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