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Each vinyl monomer of acrylates and styrene requires a ligand, initiator, and solvent set for its controlled radical polymerization because of the difference in the reactivity of the vinyl monomers. In this work, we present a platform for accelerated Cu(0)-mediated polymerization of the vinyl monomers in which a common ligand, initiator, and solvent set is used for all the vinyl monomers in a continuous-flow process with a copper metal-wire catalyst threaded through a transparent perfluoroalkoxy alkane (PFA) tube. The monomer conversion, molecular weight and its distributions (MWDs) were tuned by controlling the contact time between the catalyst and reaction solution, i.e. , controlling the rate of comproportionation/disproportionation between Cu(0), Cu( I ) and Cu( II ) as a function of the retention time (flow rate) as well as the length/thickness of the Cu(0)-wire. The versatile platform possessing intrinsic microfluidic merits delivered a monomer to polymer conversion up to 60–70% with a well-controlled MWD within a few minutes to an hour for polymerizations that traditionally take tens of hours, even with green and inexpensive reagents. The reliable platform preserves the end group fidelity and livingness of the polymer, which readily allows for serial synthesis of homo- and hetero-chain extensions as pseudoblock copolymers.

We report the scalable, all-aqueous synthesis of poly(dimethylacrylamide)–poly(diacetone acrylamide) (PDMAm–PDAAm) diblock copolymer spheres, worms and vesicles by reversible addition–fragmentation chain transfer (RAFT) aqueous dispersion polymerisation in a low-cost continuous-flow (CF) reactor. A transient state kinetic profiling method using a 5 mL reactor coil indicated a considerably faster rate than the equivalent batch reaction. Higher throughput was subsequently demonstrated by employing a 20 mL coil reactor for the synthesis of a 135 g, 30% w/w batch of PDMAm 113 macromolecular chain transfer agent (macro-CTA) at 98% conversion. This was used without further purification to polymerise DAAm in a CF reactor. During this polymerisation, the chains underwent polymerisation-induced self-assembly (PISA) producing block copolymer spheres. This reaction also proceeded faster than in batch, and the high resolution kinetics enabled clear observation of the rate enhancement which is characteristic of PISA systems. GPC studies indicated the formation of a copolymer with low molar mass dispersity and complete blocking efficiency, despite the high conversion achieved during the precursor macro-CTA synthesis. It was subsequently demonstrated that the PDMAm 113 macro-CTA could be used to prepare PDMAm 113 –PDAAm x block copolymer spheres (where x = 50, 100 and 200) with systematically increasing particle diameters. Finally, by reducing the PDMAm macro-CTA DP to 50 and increasing total solids to 20% w/w, it was possible to prepare worms and vesicles in the tubular reactor by tailoring the residence time to achieve specific degrees of polymerisation of the PDAAm block.

Pyridine–oxazoline-type ligands (PyOX) are an important class of chiral ligands for metal-catalyzed asymmetric transformations. Herein we describe an efficient and reliable flow route which is amenable for the synthesis of PyOX ligands at a scale of hundreds of milligrams per hour. Optimal flow conditions were rapidly identified through the assistance of an in-house built autonomous self-optimizing system integrating a custom-made optimization algorithm derived from the Nelder–Mead and golden section search methods. The preparation of a small library of representative PyOX ligands highlights the practical application of this flow route which should be of primary interest for synthetic chemists developing metal-catalyzed asymmetric transformations.

Based on in situ DRIFTS studies of ammonia storage mechanism over the Cu-SSZ-13 catalyst, a temperature-dependent heterogeneity constant was introduced in the traditional dual-site model, and was compared with traditional dual-site and multi-site models based on a series of NH 3 -TPD experiments in this work. The results showed that the modified dual-site model was in better agreement with experimental NH 3 -TPD measurements than the traditional dual-site model, and even better than the multi-site model at some temperatures. Moreover, the modified dual-site model was validated under transient conditions with different adsorption concentrations, flushing durations and heating rates. The results showed that the model predicted the NH 3 -TPD process well under these transient conditions. The accuracy was better than that of the traditional dual-site model, and comparable to that of the multi-site model. The modified dual-site model had similar accuracy, but less parameters compared to the multi-site model, and the reduced model complexity is of great significance for the model-based urea injection control of diesel engines.

Traditionally catalysis research and development has been limited to large purpose-built labs, requiring years of planning and implementation before the first molecules were even examined. However, recent developments in microfluidics, robotics, system miniaturization and machine intelligence allow the decoupling of research from multi-million dollar purpose-built facilities. Additionally this scaling-down of research has significant benefits for the environment, development timelines and researcher workload. In this publication we demonstrate the construction of a microfluidic catalysis research platform contained within a standard hard-sided case measuring just 0.73 m 2 , consuming under 100 W of power, and generating 66.7 μL of chemical waste per min. The system integrates a purpose-built microreactor with hot-swappable chuck, vacuum enclosure, manifolds, pumps, robotic autosampling, open-source controls and thermographic performance analysis. The system was used to investigate nine chemically different activators for a zirconocene-catalyzed α-olefin polymerization through efficient experimentation and automated transfer learning ML-based data interpretation. The contributions of different chemical structures to catalytic productivity were analyzed. Conclusions made include those regarding co-catalyst chemistry and probable operating conditions. This work demonstrates that a compact flow-based microfluidic platform can screen exothermic catalytic reactions and interpret the results using machine intelligence.

Self-optimising chemical systems have experienced a growing momentum in recent years, with the evolution of self-optimising platforms leading to their application for reaction screening and chemical synthesis. With the desire for improved process sustainability, self-optimisation provides a cheaper, faster and greener approach to the chemical development process. The use of such platforms aims to enhance the capabilities of the researcher by removing the need for labor-intensive experimentation, allowing them to focus on more challenging tasks. The establishment of these systems have enabled opportunities for self-optimising platforms to become a key element of a laboratory's repertoire. To enable the wider adoption of self-optimising chemical platforms, this review summarises the history of algorithmic usage in chemical reaction self-optimisation, detailing the functionality of the algorithms and their applications in a way that is accessible for chemists and highlights opportunities for the further exploitation of algorithms in chemical synthesis moving forward.

Sulfur removal from transportation fuels is essential for maintaining a pollutant-free environment and ensuring a healthy living. While hydrodesulfurization (HDS) has been the state-of-the-art solution for sulfur reduction in fuels, the technology is characterized by high energy and cost consumption, and it is ineffective for the removal of heavy sulfur molecules, such as alkyl-dibenzothiophenes. For this reason, alternative technologies such as oxidative desulfurization, extractive desulfurization, biodesulfurization and adsorptive desulfurization are studied. Among these, adsorptive desulfurization (ADS) represents a promising alternative method of removing sulfur by adsorption under ambient conditions. The lifetime, selectivity and capacity of ADS highly depend on the type of sorbent used. Among the many sorbents studied, Y zeolites have shown to be excellent candidates for such application. Y zeolites have a unique pore structure, large surface area and available acid sites, all of which are beneficial for the adsorption of sulfur molecules. Moreover, the structure of Y zeolites is easily tunable; thus mesoporosity and metal cations can be incorporated in the structure and increase sulfur capacity and selectivity. Two types of adsorption mechanisms have been identified, namely π-complexation and direct S–M interaction. The biggest challenge with the ADS technology is the selective adsorption of refractory sulfur compounds in the presence of aromatics/olefins, since these compounds exhibit similar structure and adsorption energies. This review focuses on past and recent ADS studies using various modified zeolites to meet federal sulfur specifications for transportation fuels, such as gasoline, jet fuel and diesel, without compromising the quality of the fuels. The most effective sorbents utilize the synergy of bimetals as a means to increase adsorption selectivity and capacity, leading to higher sulfur uptake. In the deep desulfurization region, which involves refractory sulfur compounds, mesoporous Y zeolites have shown promise in higher sulfur capacity by overcoming diffusion limitations. Hence, many studies in the recent literature have demonstrated that ADS using Y zeolites can be a promising desulfurization technology, which can work either as a stand-alone process or complimentary to the HDS process.

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products, etc. ) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source.

The value of reaction kinetic models for manufacturing APIs (active pharmaceutical ingredients) has been well established in the quality by design (QbD) paradigm. Creating such models during the early phase of development when data and material are scarce is challenging. In this work, we present a model-based design of experiments framework for selecting a “fit for purpose” kinetic model from limited data. The framework leverages an estimability analysis to facilitate parameterizing candidate models. The essential elements can be applied in other domains where model selection is required, but an illustrative case study is presented for selecting the best of three proposed kinetic models for the 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU)-catalyzed N -methylation of a key intermediate in an API process using dimethyl carbonate (DMC). The case study concludes by selecting a mechanism that invokes an N -methylated DBU species as a key intermediate over other plausible mechanisms previously suggested in the literature. The framework is conceptually straightforward and requires minimal coding and computational time to execute.

Polyurethanes (PUs) are very versatile and popular polymers that play a key role in the automotive, construction and non-food consumable sectors. In general, two-step polyurethane synthetic procedures involve the addition of catalysts also in the second phase of the reaction with problems of high environmental impact and disposal. In this work, an innovative eco-sustainable and very cheap procedure for the production of high-quality rigid polyurethane (PU) foams was developed, starting from polyethylene glycol (PEG 400) and diisocyanates as reagents and using common inorganic salts as catalysts, such as sodium chloride. In particular, our innovatory method is based on a single initial addition of a very cheap catalyst that is proved to be effective for both the prepolymer formation and polyurethane chain elongation. Moreover, simultaneous with the formation of final polyurethane, the salt was restored for a new catalytic cycle. Then, our strategy for polyurethane foam synthesis can surely represent a valid alternative as a very inexpensive and eco-compatible process, also for the industrial field. Finally, detailed mechanistic hypotheses were formulated and supported by DFT calculations.