Enzyme carbomimetics: Single site sustainable catalysts for alcohols amination

胺在化学中无处不在，2020年全球市场市场规模超过150亿美元，并以每年超过4%的速度增长。它们是化学工业中的关键中间体，广泛应用于缓蚀剂、农用化学品、制药、洗涤剂、织物柔软剂、润滑剂、聚合物和食品添加剂。胺可通过工业规模的NH3烷基化、醇烷基化、硝基芳烃还原或卤化物取代制得。这些工艺需要苛刻的条件(50巴，100-250摄氏度)、外部还原剂和/或排放化学计量比的卤化氢废料。通常使用多相催化剂，由于NH3的反应活性较低，与较高的胺相比，多相催化剂的选择性和活性较低。该项目的目的是使用高通量合成方法来确定用于NH3与醇的选择性烷基化反应的氮掺杂碳(Mn4@C)催化剂(碳酶)上负载的单位金属。醇只产生H2O作为副产品，并含有丰富的生物质前体，因此是大规模应用的理想起始材料，在循环经济中实现更可持续的工业。单点催化剂(SSC)将解决与多相催化剂相关的问题(表面中毒，低选择性)，同时确保100%的金属效率，碳和氮作为可持续元素，适合防止SSC降解为氧化物或碳化物。催化剂的发现将得到理论方法(DFT和机器学习(ML))的帮助，以确定机理途径以及优化和发现新的未知催化剂(即双位点)。该项目还将测试性能最好的催化剂的流动条件，以便更容易地撞击到化学工业。

Amines are ubiquitous in chemistry, with a global market market at more than $15 billion in 2020, and growing by more than 4% every year. They are key intermediates in the chemical industry, with extensive applications as corrosion inhibitors, agrochemicals, pharmaceuticals, detergents, fabric softeners, lubricants, polymers, and food additives. Amines are made from NH3 alkylation on the industrial scale, by alcohol alkylation, nitroarene reduction, or halide substitution. These processes require harsh conditions (>50 bar, 100-250 C), external reductants and/or emit stoichiometric amounts of hydrogen halide waste. Heterogeneous catalysts are typically used, and suffer from low selectivity and activity due to the low reactivity of NH3 compared to higher amines. The aim of the project is to use high-throughput synthetic approaches to identify single-site metals supported on nitrogen-doped carbon (MN4@C) catalysts (carbozymes) for the selective alkylation of NH3 with alcohols. Alcohols only yield H2O as a byproduct, and are abundant in biomass precursors, thus constituting ideal starting materials for large scale applications towards a more sustainable industry in a circular economy.Single-site catalysts (SSC) will solve issues associated with heterogeneous catalysts (surface poisoning, low selectivity) while ensuring a 100% metal efficiency, with carbon and nitrogen as sustainable elements that are suitable to prevent SSC degradation into oxides or carbides. Catalyst discovery will be assisted by theoretical approaches (DFT and machine learning (ML)), to identify mechanistical pathways as well as to optimise and discover new unknown catalysts (i.e. dual sites). The project will also test the best performing catalysts for flow conditions for an easier impmentation into chemical industry.