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Reactions of Trimethylaluminium: Modelling the Chemical Degradation of Synthetic Lubricants.

ABSTRACT: In investigating and seeking to mimic the reactivity of trimethylaluminium (TMA) with synthetic, ester-based lubricating oils, the reaction of methyl propionate 1 was explored with 1, 2 and 3 equivalents of the organoaluminium reagent. Spectroscopic analysis points to the formation of the adduct 1(TMA) accompanied only by the low level 1:1 production of Me₂ AlOCEtMe₂ 2 and Me₂ AlOMe 3 when an equimolar amount of TMA is applied. The deployment of excess TMA favours reaction to give 2 and 3 over 1(TMA) adduct formation and spectroscopy reveals that in hydrocarbon solution substitution product 2 traps unreacted TMA to yield 2(TMA). The ¹ H NMR spectroscopic observation of two Al-Me signals not attributable to free TMA and in the ratio 1:4 suggests the formation of a previously only postulated, symmetrical metallacycle in Me₄ Al₂ (μ² -Me)(μ² -OCEtMe₂ ). In the presence of 3, 2(TMA) undergoes thermally induced exchange to yield Me₄ Al₂ (μ² -OMe)(μ² -OCEtMe₂ ) 4 and TMA. The reaction of methyl phenylacetate 5 with TMA allows isolation of the crystalline product Me₂ AlOCBnMe₂ (TMA) 6(TMA), which allows the first observation of the Me₄ Al₂ (μ² -Me)(μ² -OR) motif in the solid state. Distances of 2.133(3) Å (Al-Me_bridging ) and 1.951 Å (mean Al-Me_terminal ) are recorded. The abstraction of TMA from 6(TMA) by the introduction of Et₂ O has yielded 6, which exists as a dimer.

SUBMITTER: Slaughter J

PROVIDER: S-EPMC6680265 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Reactions of Trimethylaluminium: Modelling the Chemical Degradation of Synthetic Lubricants.

Slaughter Jonathan J Peel Andrew J AJ Wheatley Andrew E H AE

Chemistry (Weinheim an der Bergstrasse, Germany) 20161205 1

In investigating and seeking to mimic the reactivity of trimethylaluminium (TMA) with synthetic, ester-based lubricating oils, the reaction of methyl propionate 1 was explored with 1, 2 and 3 equivalents of the organoaluminium reagent. Spectroscopic analysis points to the formation of the adduct 1(TMA) accompanied only by the low level 1:1 production of Me2 AlOCEtMe2 2 and Me2 AlOMe 3 when an equimolar amount of TMA is applied. The deployment of excess TMA favour ...[more]

PMID: 27792841

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Project description:Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal ( http://cdb.ics.uci.edu) under the Toolkits section.

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Reactions of Trimethylaluminium: Modelling the Chemical Degradation of Synthetic Lubricants.

Publications

Reactions of Trimethylaluminium: Modelling the Chemical Degradation of Synthetic Lubricants.

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