The use of magnetic nanoparticles to convert electromagnetic energy into heat is known to be a key strategy for numerous biomedical applications but is also an approach of growing interest in the field of catalysis. The heating efficiency of magnetic nanoparticles is limited by the poor magnetic properties of most of them. Here we show that the new generation of iron carbide nanoparticles of controlled size and with over 80 % crystalline Fe2.2 C leads to exceptional heating properties, which are much better than the heating properties of currently available nanoparticles. Associated to catalytic metals (Ni, Ru), iron carbide nanoparticles submitted to magnetic excitation very efficiently catalyze CO2 hydrogenation in a dedicated continuous-flow reactor. Hence, we demonstrate that the concept of magnetically induced heterogeneous catalysis can be successfully applied to methanation of CO2 and represents an approach of strategic interest in the context of intermittent energy storage and CO2 recovery.

Magnetically Induced Continuous CO2 Hydrogenation Using Composite Iron Carbide Nanoparticles of Exceptionally High Heating Power

The intrinsic complexity of carbohydrate structures has hampered access to pure glycans and hence impeded progress in the glycosciences. Automated Glycan Assembly (AGA) has facilitated the procurement of synthetic glycans, to be used in diagnostics, vaccine development, enzyme characterization and structure–function relationship studies. A general approach for obtaining complex glycans from mammalian, bacterial, fungal and plant classes provides molecular tools for glycobiology research. Recent advances in AGA technology pave the way for the production of novel carbohydrate materials. This perspective describes the state-of-the art of AGA and aspects of the technology where additional improvements are needed.

Automated Glycan Assembly: A Perspective

Flow chemistry is a widely explored technology whose intrinsic features both facilitate and provide reproducible access to a broad range of chemical processes that are otherwise inefficient or problematic. At its core, a flow chemistry module is a stable set of conditions - traditionally thought of as an externally applied means of activation/control (e.g. heat or light) - through which reagents are passed. In an attempt to simplify the teaching and dissemination of this field, we envisioned that the key advantages of the technique, such as reproducibility and the correlation between reaction time and position within the reactor, allow for the redefinition of a flow module to a more synthetically relevant one based on the overall induced effect. We suggest a rethinking of the approach to flow modules, distributing them in two subclasses: transformers and generators, which can be described respectively as a set of conditions for either performing a specific transformation or for generating a reactive intermediate. The chemistry achieved by transformers and generators is (ideally) independent of the substrate introduced, meaning that they must be robust to small adjustments necessary for the adaptation to different starting materials and reagents while ensuring the same chemical outcome. These redefined modules can be used for single-step reactions or in multistep processes, where modules can be connected to each other in reconfigurable combinations to create chemical assembly systems (CAS) targeting compounds and libraries sharing structural cores. With this tutorial review, we provide a guide to the overall approach to flow chemistry, discussing the key parameters for the design of transformers and generators as well as the development of chemical assembly systems.

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How to approach flow chemistry

The outcome of a glycosylation reaction critically depends on the reactivity of all reaction partners involved: the donor glycoside (the electrophile), the activator (that generally provides the leaving group on the activated donor species) and the glycosyl acceptor (the nucleophile). The influence of the donor on the outcome of a glycosylation reaction is well appreciated and documented. Differences in donor reactivity have led to the development of chemoselective glycosylation reactions and the reactivity of donor glycosides has been tuned to affect stereoselective glycosylation reactions. The quantification of donor reactivity has enabled the conception of streamlined one-pot glycosylation sequences. In contrast, although it has long been known that the nature and the reactivity of the nucleophile influence the outcome of a glycosylation, the knowledge of acceptor reactivity and insight into the consequences thereof are often circumstantial or anecdotal. This review documents how the reactivity impacts the glycosylation reaction outcome both in terms of chemical yield and stereoselectivity. The effect of acceptor nucleophilicity on the reaction mechanism is described and steric, conformational and electronic influences are outlined. Quantitative and computational approaches to comprehend acceptor nucleophilicity are assessed. The increasing insight into the stereoelectronic effects governing glycoside reactivity will eventually enable the conception of effective stereoselective glycosylation methodology that can be tuned to the reaction partners at hand.

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Acceptor reactivity in glycosylation reactions

In multistep continuous flow chemistry, studying complex reaction mixtures in real time is a significant challenge, but provides an opportunity to enhance reaction understanding and control. We report the integration of four complementary process analytical technology tools (NMR, UV/Vis, IR and UHPLC) in the multistep synthesis of an active pharmaceutical ingredient, mesalazine. This synthetic route exploits flow processing for nitration, high temperature hydrolysis and hydrogenation reactions, as well as three inline separations. Advanced data analysis models were developed (indirect hard modeling, deep learning and partial least squares regression), to quantify the desired products, intermediates and impurities in real time, at multiple points along the synthetic pathway. The capabilities of the system have been demonstrated by operating both steady state and dynamic experiments and represents a significant step forward in data-driven continuous flow synthesis.

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Advanced Real‑Time Process Analytics for Multistep Synthesis in Continuous Flow**

Automated synthesis platforms accelerate and simplify the preparation of molecules by removing the physical barriers to organic synthesis. This provides unrestricted access to biopolymers and small molecules via reproducible and directly comparable chemical processes. Current automated multistep syntheses rely on either iterative1–4 or linear processes5–9, and require compromises in terms of versatility and the use of equipment. Here we report an approach towards the automated synthesis of small molecules, based on a series of continuous flow modules that are radially arranged around a central switching station. Using this approach, concise volumes can be exposed to any reaction conditions required for a desired transformation. Sequential, non-simultaneous reactions can be combined to perform multistep processes, enabling the use of variable flow rates, reuse of reactors under different conditions, and the storage of intermediates. This fully automated instrument is capable of both linear and convergent syntheses and does not require manual reconfiguration between different processes. The capabilities of this approach are demonstrated by performing optimizations and multistep syntheses of targets, varying concentrations via inline dilutions, exploring several strategies for the multistep synthesis of the anticonvulsant drug rufinamide10, synthesizing eighteen compounds of two derivative libraries that are prepared using different reaction pathways and chemistries, and using the same reagents to perform metallaphotoredox carbon–nitrogen cross-couplings11 in a photochemical module—all without instrument reconfiguration. An automated synthesis instrument comprising a series of continuous flow modules that are radially arranged around a central switching station can achieve both linear and convergent syntheses.

https://www.nature.com/articles/s41586-020-2083-5.pdf?proof=t

Automated radial synthesis of organic molecules

Reliable glycosylation reactions that allow for the stereo- and regioselective installation of glycosidic linkages are paramount to the chemical synthesis of glycan chains. The stereoselectivity of glycosylations is exceedingly difficult to control due to the reaction’s high degree of sensitivity and its shifting, simultaneous mechanistic pathways that are controlled by variables of unknown degree of influence, dominance, or interdependency. An automated platform was devised to quickly, reproducibly, and systematically screen glycosylations and thereby address this fundamental problem. Thirteen variables were investigated in as isolated a manner as possible, to identify and quantify inherent preferences of electrophilic glycosylating agents (glycosyl donors) and nucleophiles (glycosyl acceptors). Ways to enhance, suppress, or even override these preferences using judicious environmental conditions were discovered. Glycosylations involving two specific partners can be tuned to produce either 11:1 selectivi...

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An Empirical Understanding of the Glycosylation Reaction

Predicting the stereochemical outcome of chemical reactions is challenging in mechanistically ambiguous transformations. The stereoselectivity of glycosylation reactions is influenced by at least eleven factors across four chemical participants and temperature. A random forest algorithm was trained using a highly reproducible, concise dataset to accurately predict the stereoselective outcome of glycosylations. The steric and electronic contributions of all chemical reagents and solvents were quantified by quantum mechanical calculations. The trained model accurately predicts stereoselectivities for unseen nucleophiles, electrophiles, acid catalyst, and solvents across a wide temperature range (overall root mean square error 6.8%). All predictions were validated experimentally on a standardized microreactor platform. The model helped to identify novel ways to control glycosylation stereoselectivity and accurately predicts previously unknown means of stereocontrol. By quantifying the degree of influence of each variable, we begin to gain a better general understanding of the transformation, for example that environmental factors influence the stereoselectivity of glycosylations more than the coupling partners in this area of chemical space.

/pdf/predicting-glycosylation-stereoselectivity-using-machine-1014ujh4bb.pdf

Sourav Chatterjee

Papers

Automated radial synthesis of organic molecules

An Empirical Understanding of the Glycosylation Reaction

Predicting glycosylation stereoselectivity using machine learning.

Design and operation of a radio-frequency heated micro-trickle bed reactor for consecutive catalytic reactions

Design of a radio frequency heated isothermal micro-trickle bed reactor