Search

We present a stopped-flow benchtop NMR system (composed of commercially available hardware components) that allows for quantitative reaction monitoring to be completed with relative ease, even with experimentally complex reaction systems.

A novel enantioselective route to tetrabenazine has been developed using continuous CIDT in a multiwell crystallization/racemization device outfitted with real-time HPLC to visualize and control the dynamic process.

The crystallization-induced diastereomer transformation of a precursor to navoximod was investigated using online HPLC and optimized with inline racemization.

Automation has become an increasingly popular tool for synthetic chemists over the past decade. Recent advances in robotics and computer science have led to the emergence of automated systems that execute common laboratory procedures including parallel synthesis, reaction discovery, reaction optimization, time course studies, and crystallization development. While such systems offer many potential benefits, their implementation is rarely automatic due to the highly specialized nature of synthetic procedures. Each reaction category requires careful execution of a particular sequence of steps, the specifics of which change with different conditions and chemical systems. Careful assessment of these critical procedural requirements and identification of the tools suitable for effective experimental execution are key to developing effective automation workflows. Even then, it is often difficult to get all the components of an automated system integrated and operational. Data flows and specialized equipment present yet another level of challenge. Unfortunately, the pain points and process of implementing automated systems are often not shared or remain buried deep in the SI. This perspective provides an overview of the current state of automation of synthetic chemistry at the benchtop scale with a particular emphasis on core considerations and the ensuing challenges of deploying a system. Importantly, we aim to reframe automation as decidedly not automatic but rather an iterative process that involves a series of careful decisions (both human and computational) and constant adjustment., The process of automating chemistry involves a wide variety of considerations that are often overlooked.

The mechanisms for the three- and four-component variants of the Castagnoli-Cushman reaction (CCR) have been investigated. A series of crossover experiments were conducted to probe the structure and reactivity of known amide-acid intermediates for the three- and four-component variants of the CCR (3CR and 4CR, respectively). Control experiments paired with in situ reaction monitoring with infrared spectroscopy for the 4CR align with a mechanism in which amide-acids derived from maleic anhydride can reversibly form free amine and cyclic anhydride. Although this equilibrium is unfavorable, the aldehyde present can trap the primary amine through imine formation and react with the enol form of the anhydride through a Mannich-like mechanism. This detailed mechanistic investigation coupled with additional crossover experiments supports an analogous mechanism for the 3CR and has led to the elucidation of new 3CR conditions with homophthalic anhydride, amines, and aldehydes for the formation of dihydroisoquinolones in good yields and excellent diastereoselectivity. This work represents the culmination of more than a decade of mechanistic speculation for the 3- and 4CR, enabling the design of new multicomponent reactions that exploit this novel mechanism.

Autonomous process optimization involves the human intervention-free exploration of a range of pre-defined process parameters in order to improve responses such as reaction yield and product selectivity. Utilizing off-the-shelf components, we developed a closed-loop system capable of carrying out parallel autonomous process optimization experiments in batch with significantly reduced cycle times. Upon implementation of our system in the autonomous optimization of a palladium-catalyzed stereoselective Suzuki-Miyaura coupling, we found that the definition of a set of meaningful, broad, and unbiased process parameters was the most critical aspect of a successful optimization. In addition, we found that categorical parameters such as phosphine ligand were vital to determining the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing an element of bias into the experimental design. In seeking a systematic method for the selection of a diverse set of phosphine ligands fully representative of the chemical space, we developed a strategy that leveraged computed molecular descriptor clustering analysis. This strategy allowed for the successful autonomous optimization of a stereoselective Suzuki-Miyaura coupling between a vinyl sulfonate and an arylboronic acid to selectively generate the E-product isomer in high yield.

Conventional materials discovery is a laborious and time-consuming process that can take decades from initial conception of the material to commercialization. Recent developments in materials acceleration platforms promise to accelerate materials discovery using automation of experiments coupled with machine learning. However, most of the automation efforts in chemistry focus on synthesis and compound identification, with integrated target property characterization receiving less attention. In this work, we introduce an automated platform for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. Our platform encompasses automated lego-like synthesis, product identification, and optical characterization that can be executed in a fully integrated end-to-end fashion. Using this workflow to screen organic laser candidates, we have discovered 8 potential candidates for organic lasers. We test the lasing threshold of 4 molecules in thin-film devices and find 2 molecules with state-of-the-art performance. These promising results show the potential of automated synthesis and screening for accelerated materials development. This article is protected by copyright. All rights reserved.

Background Biomass valorization has been suggested as a sustainable alternative to petroleum-based energy and commodities. In this context, the copper radical oxidases (CROs) from Auxiliary Activity Family 5/Subfamily 2 (AA5_2) are attractive biocatalysts for the selective oxidation of primary alcohols to aldehydes. Originally defined by the archetypal galactose 6-oxidase from Fusarium graminearum, fungal AA5_2 members have recently been shown to comprise a wide range of specificities for aromatic, aliphatic and furan-based alcohols. This suggests a broader substrate scope of native CROs for applications. However, only 10% of the annotated AA5_2 members have been characterized to date. Results Here, we define two homologues from the filamentous fungi Fusarium graminearum and F. oxysporum as predominant aryl alcohol oxidases (AAOs) through recombinant production in Pichia pastoris, detailed kinetic characterization, and enzyme product analysis. Despite possessing generally similar active-site architectures to the archetypal FgrGalOx, FgrAAO and FoxAAO have weak activity on carbohydrates, but instead efficiently oxidize specific aryl alcohols. Notably, both FgrAAO and FoxAAO oxidize hydroxymethyl furfural (HMF) directly to 5-formyl-2-furoic acid (FFCA), and desymmetrize the bioproduct glycerol to the uncommon L-isomer of glyceraldehyde. Conclusions This work expands understanding of the catalytic diversity of CRO from AA5_2 to include unique representatives from Fusarium species that depart from the well-known galactose 6-oxidase activity of this family. Detailed enzymological analysis highlights the potential biotechnological applications of these orthologs in the production of renewable plastic polymer precursors and other chemicals.

Teaching laboratories are designed to educate students on basic theories and techniques through guided laboratory experiments. As advances in automation revolutionize research chemistry laboratories, teaching tools can advance to reflect these changes. In this study, we integrated several programmable instruments, including automated pumps for liquid transfer, into an augmented titration setup that can be operated remotely. Our setup empowers students to learn the conceptual, technical, and practical aspects of titration by leveraging laboratory automation tools. Our system uses Siri as a speech recognition tool and the Python programming language to develop action routines for titration uses. The application of Siri provides a smooth user-to-instrument experience, and the use of Python allows coders to effectively develop and maintain the workflow. Three chemistry professors were invited to participate in usability tests of the system using a mobile device. Their positive feedback highlights the promise of our system as a valuable addition to undergraduate chemistry teaching laboratories. This titration setup addresses accessibility challenges and distance learning requirements for chemical education. Further, our tool introduces students to the type of automated experimentation that is emerging as the modern standard in chemistry research laboratories.

Current PAT tools struggle to provide real time solid-phase composition data for crystallizations. HPLC offers a variety of benefits over other solution-phase PATs and can be used to infer solid-phase composition when combining total (solid plus solution phase) sampling with solution-only sampling. However, the selective solution-phase sampling of supersaturated solutions is extremely challenging. We herein present a dynamically flushed in situ filtration device that attaches to the tip of Mettler-Toledo's EasySampler probe and makes use of its mechanical motion to avoid surface fouling. Filter functionality was tested under both increasing saturation (via cooling or antisolvent addition) as well as decreasing saturation (via heating or solvent addition), illustrating its accuracy, reliability and versatility. The utility of the filter tip was highlighted by monitoring the classical resolution of TBZ, an important drug precursor.

The transformation of CO2 to value added commodity chemicals presents an impactful strategy to obtain products that are less dependent on fossil fuels. In this study, indium tribromide (InBr3) mixed with tetrabutylammonium bromide (NBu4Br) co-catalyst has been identified as a simple, highly efficient catalyst for the synthesis of cyclic carbonates from epoxides and CO2 at sub-atmospheric pressures, room temperature, and under solvent-free conditions. The InBr3/NBu4Br catalytic system is tolerant toward different functional groups with high conversions and >99% selectivity for cyclic carbonate without resorting to high pressures and temperatures. Moreover, a combination of in situ IR, NMR spectroscopy, and substrate labelling experiments enabled the proof of key catalytic steps and detection of reaction intermediates to elucidate the reaction mechanism. This technology represents a potential scalable system for the utilization of waste CO2.

Obtaining data-dense reaction profiles for reactions performed under an inert atmosphere remains a significant challenge obfuscating mechanistic analysis. To this end, we have developed a reaction ...

The abundances, relative distributions, and enantiomeric and isotopic compositions of amines, amino acids, and hydroxy acids in Miller Range (MIL) 090001 and MIL 090657 meteorites were determined. Chiral distributions and isotopic compositions confirmed that most of the compounds detected were indigenous to the meteorites and not the result of terrestrial contamination. Combined with data in the literature, suites of these compounds have now been analyzed in a set of six CR chondrites, spanning aqueous alteration types 2.0–2.8. Amino acid abundances ranged from 17 to 3300 nmol g−1 across the six CRs; hydroxy acid abundances ranged from 180 to 1800 nmol g−1; and amine abundances ranged from 40 to 2100 nmol g−1. For amino acids and amines, the weakly altered chondrites contained the highest abundances, whereas hydroxy acids were most abundant in the more altered CR2.0 chondrite. Because water contents in the meteorites are orders of magnitude greater than soluble organics, synthesis of hydroxy acids, which requires water, may be less affected by aqueous alteration than amines and amino acids that require nitrogen‐bearing precursors. Two chiral amino acids that were plausibly extraterrestrial in origin were present with slight enantiomeric excesses: L‐isovaline (~10% excess) and D‐β‐amino‐n‐butyric acid (~9% excess); further studies are needed to verify that the chiral excess in the latter compound is truly extraterrestrial in origin. The isotopic compositions of compounds reported here did not reveal definitive links between the different compound classes such as common synthetic precursors, but will provide a framework for further future in‐depth analyses.

A multi-well continuous CIDT approach with inline racemization of the solution phase is presented. Using two in-house built PATs and a flow reactor, we were able to successfully crystallize an enantiopure salt of TBZ, the active metabolite of the tardive dyskinesia drug valbenazine. Despite discovering an undesired racemic solid phase, inline racemization combined with careful control of crystallization conditions allowed for multigram quantities of enantiopure material to be harvested using our setup. Critically, this control was made possible by the use of PATs to observe and quantify the composition of both the solid and solution phases.

We have developed a convenient two-step, one-pot method for the facile synthesis of cyanoimidazoles. By integrating a suite of reaction-monitoring techniques, we were able to interrogate each transformation independently. We observed that formation of a key hemiaminal intermediate is complicated via many equilibrium processes, creating oligomers and eventually resulting in unproductive dimerization. DoE experiments indicated that despite the complex equilibria of this substrate, high conversion to the trifluoromethyl imidazole can still be achieved. We also report the effects of temperature, pH, and substrate concentrations on the rate and selectivity of the elimination cascade reaction converting the trifluoromethyl imidazole to the cyanoimidazole. By leveraging kinetic information gathered from each step independently, we report reaction conditions to achieve high yields of the cyanoimidazole from carbonyl-containing substrate directly in one pot.

An Auxiliary Activity Family 5 (AA5) copper-radical alcohol oxidase (AlcOx) with promiscuous activity towards simple alkyl and aromatic alcohols was evaluated using real-time reaction progress monitoring. Reaction kinetics using variable time normalization analysis (VTNA) were determined from reaction progress curves. By this approach, a detailed view of the entire reaction time course under various conditions was obtained and used to identify parameters that will inform further process optimization development. Optimal activity was found impacted by several factors, including reaction pH, oxygen saturation, and the source of a co-oxidant, either HRP or a chemical alternative, potassium ferricyanide. Analysis of reaction progress curves demonstrated that reaction stalling occurred as a result of oxygen depletion and from a loss of enzyme activity over time. The cooperativity between AlcOx, horseradish peroxidase (HRP), and catalase that result in enhanced reactivity was explored, with reaction pH being identified as a key factor for optimal activity. The results show that a process with HRP is more robust than with potassium ferricyanide, but that both oxidants likely activate AlcOx by a similar mechanism. The phenomenon of product inhibition was investigated for representative reactants, revealing that reaction inhibition was more significant for butyraldehyde than for benzaldehyde. Our analysis suggests that this is linked to the greater proportion in which butyraldehyde exists in the hydrated form.

Insights and mechanistic studies into biphasic reactions are often challenging due to difficulty in deploying in situ reaction-monitoring or sampling procedures. Herein, we describe the development...

Solid-liquid slurries are vital and increasingly prevalent in the pharmaceutical and chemical industries. Despite the importance of these heterogeneous systems, process control and optimization are fundamentally hindered by a lack of compatible real-time analytical techniques. We present herein an online HPLC monitoring platform enabling access to real-time compositional information on slurries. We demonstrate the system by investigating the heterogeneous synthesis reaction of tetrabenazine. Furthermore, we integrated our online HPLC platform with the orthogonal monitoring techniques of a pH probe and a microscopic imaging probe to provide additional mechanistic insight. These combined insights enable the optimization of tetrabenazine synthesis in terms of reaction time, byproduct formation, and diastereomeric purity of the final product.

A chiral aryl iodide promotes the enantioselective oxidative cyclization of 1-naphtholic sulfonamides, albeit in moderate ee and low yield. The products tend to crystallize as conglomerates. Recrystallization thus increases their ee to > 99% ee. This highly enantioenriched material provides seed crystals for the resolution of the racemate (prepared in high yield by oxidative cyclization with (diacetoxyiodo)benzene in trifluoroacetic acid) by coupled preferential crystallization. This enables the production of significant quantities of highly enantioenriched products, despite the low efficiency of the enantioselective reaction.

Automated kinetic profiling is a valuable tool for providing insights into key mechanistic features of complex catalytic systems. In an attempt to optimize a palladium-catalyzed Suzuki cross-coupling reaction, automated kinetic profiling was utilized with offline liquid chromatography to monitor reaction progress. Upon uncovering analytical sample instability issues, an online HPLC capability was developed and implemented through integration of a Chemspeed liquid handling robot with an Agilent HPLC to facilitate automated reaction set-up and monitoring. Application of this capability resulted in the observation that precatalyst activation was a key factor influencing the reaction rate. Leveraging this mechanistic insight, a more efficient method to access the active catalyst was developed. This change resulted in a five-fold increase in the reaction rate.

This article describes the integration of evaporative light scattering detection (ELSD) with automated robotic sampling to obtain reliable and data-rich solubility and crystallization profiles of minimally- or non-UV active compounds. The new technology allowed for the thermodynamic solubilities of various compounds to be profiled over broad temperature ranges without experimenter intervention. Two case studies are presented that illustrate the ability of the automated system to furnish solution phase composition of systems actively undergoing crystallization: first, the dynamic change in the solution phase concentration of multiple components during seeded preferential crystallization is monitored and time-course profiles showing the change in solution phase enantiomeric excess (e.e.) were created. Second, a diastereomeric resolution is profiled, illustrating the utility that monitoring the solution phase composition during crystallization has on reporting on the success of this classical resolution method. Validation of the sampling technology is provided by corroborating the ELSD data with in situ ReactIR monitoring.

Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.We propose three broad categories of data in chemistry: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid- and liquid-dosing modules with computer vision to automate solubility screening, thereby gathering fundamental data that are necessary for almost every experimental design. Using computer vision offers the additional benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Experimental data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chemical processes. The third platform uses automated process analytical technology to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to determine exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment.

This study focused on fundamental data acquisition parameter selection for a benchtop nuclear magnetic resonance (NMR) system with continuous flow, applicable for reaction monitoring. The effect of flow rate on the mixing behaviors within a flow cell was observed, along with an exponential decay relationship between flow rate and the apparent spin-lattice relaxation time (T1*) of benzaldehyde. We also monitored sensitivity (as determined by signal-to-noise ratios; SNRs) under various flow rates, analyte concentrations, and temperatures of the analyte flask. Results suggest that a maximum SNR can be achieved with low to medium flow rates and higher analyte concentrations. This was consistent with data collected with parameters that promote either slow or fast data acquisition. We further consider the effect of these conditions on the analyte's residence time, T1*, and magnetic field inhomogeneity that is a product of continuous flow. Altogether, our results demonstrate how fundamental acquisition parameters can be manipulated to achieve optimal data acquisition in continuous-flow NMR systems.

We have developed a reaction monitoring platform capable of automated sampling and online HPLC analysis to generate temporal profiles for reactions performed from within a glovebox. The device allows for facile reaction progress analysis to aid in mechanistic studies of air-sensitive chemical transformations. The platform has demonstrated high reproducibility regarding sample mixing, dilution, delivery, and analysis. We employed the sampling platform to acquire temporal profiles for a series of Buchwald-Hartwig aminations. Parallel coupling reactions using iodobenzene and bromobenzene both exhibit complex kinetics. A competition reaction including both aryl halides demonstrated high selectivity for iodobenzene indicative of catalyst monopoly. The temporal profile for the difunctionalized substrate 1,4-iodobromobenzene was unexpected based a priori and is indicative of a distinct underlying mechanism. We attribute this unanticipated reactivity to intramolecular catalysts transfer through the π system as seen in “living” polymerization transfer catalyst systems. This automated sampling platform has greatly increased mechanistic understanding by performing only a small subset of experiments.

Chemists spend an inordinate amount of time performing low-level tasks based on visual observation. Camera-enabled laboratory equipment in conversation with computer vision algorithms can be used to automate many of these processes, thereby freeing up valuable time and resources. We developed a generalizable computer-vision based system capable of monitoring and controlling liquid-level across a variety of chemistry applications. This paper reports on the system’s motivation, architecture, and successful deployment in three experimental use cases which require continous stirring: continuous preferential crystallization (CPC), slurry filtration, and solvent swap distillation

An autonomous laboratory for thin film discovery is used to optimize the doping and annealing of organic semiconductors., Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate the power of this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies.

The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.

Detailed kinetic analysis for the Cu(I)-catalyzed Kinugasa reaction forming β-lactams has revealed an anomalous overall zero-order reaction profile, due to opposing positive and negative orders in nitrone and alkyne, respectively. Furthermore, the reaction displays a second-order dependence on the catalyst, confirming the critical involvement of a postulated bis-Cu complex. Finally, reaction progress analysis of multiple byproducts has allowed a new mechanism, involving a common ketene intermediate to be delineated. Our results demonstrate that β-lactam synthesis through the Kinugasa reaction proceeds via a cascade involving (3 + 2) cycloaddition, (3 + 2) cycloreversion, and finally (2 + 2) cycloaddition. Our new mechanistic understanding has resulted in optimized reaction conditions to dramatically improve the yield of the target β-lactams and provides the first consistent mechanistic model to account for the formation of all common byproducts of the Kinugasa reaction.

An imine-coupled [Fe–N2S2]2 complex, prepared from a readily available benzothiazolidine ligand, catalyzes selectively the hydroboration of aliphatic and aromatic aldehydes at low catalyst loadings (0.1 mol %) using pinacolborane. Both mono- and disubstituted aromatic and aliphatic aldehydes are hydroborated selectively in the presence of ketones, nitriles, alkenes, amines, and halides. Reaction of the [Fe–N2S2]2 complex with CO and preliminary reaction progress kinetic studies point to a complex mechanism.

An accessible machine-learning tool has been developed that can accelerate the optimization of a wide range of synthetic reactions — and reveals how cognitive bias might have undermined optimization by humans. Bayesian optimization for synthetic chemistry reactions.