Use of a Droplet Platform To Optimize Pd-Catalyzed C–N Coupling Reactions Promoted by Organic Bases

Adaptive Photochemical Amination via Co(II) Catalysis

Transition-metal-catalyzed amination of aryl halides is one of the most employed methods for constructing N-arylation adducts. However, the broad success of these reactions largely relies on the screening of precatalysts, elaborated ligands, and case-by-case optimization of reaction conditions (solvent, base, additive, temperature, etc.) for electronically or structurally diverse nucleophiles. Herein, we report an adaptive photochemical C-N coupling of aryl halides with various nitrogen nucleophiles (aliphatic and aromatic amines, amides, sulfonamides, pyrazoles, and ammonium salts) by Co(II) catalysis under the same reaction conditions (same precatalyst, same ligand, same base, same solvent, same temperature) without the addition of any exogenous photocatalyst. This photochemical amination features a wide substrate scope (>130 examples, up to 95% yield) with excellent functional group tolerance. Mechanistic studies indicate that these C-N coupling reactions may proceed via a Co(I)/Co(III) catalytic cycle.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Reductive Transamination of Pyridinium Salts to N-Aryl Piperidines

Saturated N-heterocycles are found in numerous bioactive natural products and are prevalent in pharmaceuticals and agrochemicals. While there are many methods for their synthesis, each has its limitations, such as scope and functional group tolerance. Herein, we describe a rhodium-catalyzed transfer hydrogenation of pyridinium salts to access N-(hetero)aryl piperidines. The reaction proceeds via a reductive transamination process, involving the initial formation of a dihydropyridine intermediate via reduction of the pyridinium ion with HCOOH, which is intercepted by water and then hydrolyzed. Subsequent reductive amination with an exogenous (hetero)aryl amine affords an N-(hetero)aryl piperidine. This reductive transamination method thus allows for access of N-(hetero)aryl piperidines from readily available pyridine derivatives, expanding the toolbox of dearomatization and skeletal editing.

A strategy for the controllable generation of organic superbases from benchtop-stable salts

Organic superbases are a distinct class of strong base that enable numerous modern reaction applications. Despite their great synthetic potential, widespread use and study of superbases are limited by their air sensitivity and difficult preparation. To address this, we report air-stable carboxylate salts of BTPP and P2-t-Bu phosphazene superbases that, when added to solution with an epoxide, spontaneously generate freebase. These systems function as effective precatalysts and stoichiometric prereagents for superbase-promoted addition, substitution and polymerization reactions. In addition to improving the synthesis, shelf stability, handling and recycling of phosphazenes, this approach enables precise regulation of the rate of base generation in situ. The activation strategy effectively mimics manual slow addition techniques, allowing for control over a reaction's rate or induction period and improvement of reactions that require strong base but are also sensitive to its presence, such as Pd-catalyzed coupling reactions.

A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.

Continuous Ligand-Free Catalysis Using a Hybrid Polymer Network Support

Although the pharmaceutical and fine chemical industries primarily utilize batch homogeneous reactions to carry out chemical transformations, emerging platforms seek to improve existing shortcomings by designing effective heterogeneous catalysis systems in continuous flow reactors. In this work, we present a versatile network-supported palladium (Pd) catalyst using a hybrid polymer of poly(methylvinylether-alt-maleic anhydride) and branched polyethyleneimine for intensified continuous flow synthesis of complex organic compounds via heterogeneous Suzuki-Miyaura cross-coupling and nitroarene hydrogenation reactions. The hydrophilicity of the hybrid polymer network facilitates the reagent mass transfer throughout the bulk of the catalyst particles. Through rapid automated exploration of the continuous and discrete parameters, as well as substrate scope screening, we identified optimal hybrid network-supported Pd catalyst composition and process parameters for Suzuki-Miyaura cross-coupling reactions of aryl bromides with steady-state yields up to 92% with a nominal residence time of 20 min. The developed heterogeneous catalytic system exhibits high activity and mechanical stability with no detectable Pd leaching at reaction temperatures up to 95 °C. Additionally, the versatility of the hybrid network-supported Pd catalyst is demonstrated by successfully performing continuous nitroarene hydrogenation with short residence times (<5 min) at room temperature. Room temperature hydrogenation yields of >99% were achieved in under 2 min nominal residence times with no leaching and catalyst deactivation for more than 20 h continuous time on stream. This catalytic system shows its industrial utility with significantly improved reaction yields of challenging substrates and its utility of environmentally-friendly solvent mixtures, high reusability, scalable and cost-effective synthesis, and multi-reaction successes.

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R2 of 0.712) was obtained on our in-house data. This highlights that the GraphRXN model can be deployed in an integrated workflow which combines robotics and AI technologies for forward reaction prediction.

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

A Brief Introduction to Chemical Reaction Optimization

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

AROPS: A Framework of Automated Reaction Optimization with Parallelized Scheduling

With the development of automated experimental platforms and optimization algorithms, chemists can easily optimize chemical reactions in an automated and high-throughput fashion. However, the modules in existing automated experimental platforms are operated in a linear fashion without orchestrating with the optimization algorithm, thus leaving room for further efficiency improvement. Here, we introduced a framework of automated reaction optimization with parallelized scheduling (AROPS) to realize the integration of the optimization algorithm and module scheduling. AROPS relies on a customized Bayesian optimizer to solve multi-reactor/analyzer reaction optimization problems with three different scheduling modes to arrange tasks for various experimental modules. In addition, a mechanism based on probability of improvement (PI) for discarding unpromising ongoing experiments was developed to facilitate freeing up valuable experimental resources in parallelized optimization. We tested the performance of AROPS using a hardware emulator on three representative benchmark reactions encountered in organic synthesis, illustrating that AROPS can trade off optimization time and cost according to the chemists' preference.

Multivariate curve resolution for kinetic modeling and scale-up prediction

Abstract

An imine synthesis was investigated in a nearly isothermal oscillating segmented flow microreactor at different temperatures using non-invasive Raman spectroscopy. Multivariate curve resolution provided a calibration-free approach for obtaining kinetic parameters. The two different multivariate curve resolution approaches, soft and hard modeling, were applied and contrasted, leading to similar results. Taking heat and mass balance into account, the proposed kinetic model was applied for a model-based scale-up prediction. Finally, the reaction was performed in a 0.5 L semi-batch reactor, followed by in-line Raman spectroscopy and off-line gas chromatography analysis. The successful scale-up was demonstrated with a good agreement between measured and predicted concentration profiles.

Highlights

• Oscillation segmented flow reactor with inline Raman spectroscopy.

• Multivariate Curve Resolution with hard and soft constraints.

• High quality kinetic model for scale-up predictions.

Graphical abstract

Machine-Learning-Guided Discovery of Electrochemical Reactions

The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry.

Emerging Trends in Cross-Coupling: Twelve-Electron-Based L1Pd(0) Catalysts, Their Mechanism of Action, and Selected Applications

Monoligated palladium(0) species, L1Pd(0), have emerged as the most active catalytic species in the cross-coupling cycle. Today, there are methods available to generate the highly active but unstable L1Pd(0) catalysts from stable precatalysts. While the size of the ligand plays an important role in the formation of L1Pd(0) during in situ catalysis, the latter can be precisely generated from the precatalyst by various technologies. Computational, kinetic, and experimental studies indicate that all three steps in the catalytic cycle─oxidative addition, transmetalation, and reductive elimination─contain monoligated Pd. The synthesis of precatalysts, their mode of activation, application studies in model systems, as well as in industry are discussed. Ligand parametrization and AI based data science can potentially help predict the facile formation of L1Pd(0) species.

Automation and Microfluidics for the Efficient, Fast, and Focused Reaction Development of Asymmetric Hydrogenation Catalysis

Automation and microfluidic tools potentially enable efficient, fast, and focused reaction development of complex chemistries, while minimizing resource- and material consumption. The introduction of automation-assisted workflows will contribute to the more sustainable development and scale-up of new and improved catalytic technologies. Herein, the application of automation and microfluidics to the development of a complex asymmetric hydrogenation reaction is described. Screening and optimization experiments were performed using an automated microfluidic platform, which enabled a drastic reduction in the material consumption compared to conventional laboratory practices. A suitable catalytic system was identified from a library of Ru^II -diamino precatalysts. In situ precatalyst activation was studied with ¹ H/³¹ P nuclear magnetic resonance (NMR), and the reaction was scaled up to multigram quantities in a batch autoclave. These reactions were monitored using an automated liquid-phase sampling system. Ultimately, in less than a week of total experimental time, multigram quantities of the target enantiopure alcohol product were provided by this automation-assisted approach.

Autonomous Multi-Step and Multi-Objective Optimization Facilitated by Real-Time Process Analytics

Autonomous flow reactors are becoming increasingly utilized in the synthesis of organic compounds, yet the complexity of the chemical reactions and analytical methods remains limited. The development of a modular platform which uses rapid flow NMR and FTIR measurements, combined with chemometric modeling, is presented for efficient and timely analysis of reaction outcomes. This platform is tested with a four variable single-step reaction (nucleophilic aromatic substitution), to determine the most effective optimization methodology. The self-optimization approach with minimal background knowledge proves to provide the optimal reaction parameters within the shortest operational time. The chosen approach is then applied to a seven variable two-step optimization problem (imine formation and cyclization), for the synthesis of the active pharmaceutical ingredient edaravone. Despite the exponentially increased complexity of this optimization problem, the platform achieves excellent results in a relatively small number of iterations, leading to >95% solution yield of the intermediate and up to 5.42 kg L-1 h-1 space-time yield for this pharmaceutically relevant product.

A "Pool and Split" Approach to the Optimization of Challenging Pd-Catalyzed C-N Cross-Coupling Reactions

A screening method for the rapid identification of catalytic conditions for Pd-catalyzed C-N cross-coupling reactions is reported. The strategy evaluates mixtures of precatalysts, ligands, and bases to identify productive conditions that are subsequently optimized through two deconvolution steps, which uncover the active catalyst and identify the optimal solvent and base for the catalytic system. The efficacy of this approach was demonstrated through application to a previously reported reaction, whereby both the literature conditions and additional solutions were retrieved. The same approach to Ni-catalyzed C-N cross-coupling was investigated in parallel but was found to be less successful due to limited activity of the evaluated reagent combinations. Finally, the utility of this method was showcased by identifying effective conditions for the Pd-catalyzed cross-coupling of complex molecules, which not only revealed nonobvious solutions for the processes under evaluation but also resulted in the discovery of new chemical reactions.

Flow Chemistry: A Sustainable Voyage Through the Chemical Universe en Route to Smart Manufacturing

Microfluidic devices and systems have entered many areas of chemical engineering, and the rate of their adoption is only increasing. As we approach and adapt to the critical global challenges we face in the near future, it is important to consider the capabilities of flow chemistry and its applications in next-generation technologies for sustainability, energy production, and tailor-made specialty chemicals. We present the introduction of microfluidics into the fundamental unit operations of chemical engineering. We discuss the traits and advantages of microfluidic approaches to different reactive systems, both well-established and emerging, with a focus on the integration of modular microfluidic devices into high-efficiency experimental platforms for accelerated process optimization and intensified continuous manufacturing. Finally, we discuss the current state and new horizons in self-driven experimentation in flow chemistry for both intelligent exploration through the chemical universe and distributed manufacturing. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Developing flow photo-thiol–ene functionalizations of cinchona alkaloids with an autonomous self-optimizing flow reactor

Continuous flow photo-thiol–ene reactions on cinchona alkaloids with a variety of organic thiols have been developed using enabling technologies such as a self-optimizing flow photochemical reactor.

Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids

Research-scale fully automated flow platform for reaction self-optimization with solids handling facilitates identification of optimal conditions for continuous manufacturing of pharmaceuticals while reducing amounts of raw materials consumed.

Metal-catalyzed reactions for the C(sp2)–N bond formation: achievements of recent years

The review deals with the main catalytic methods for the C(sp2)–N bond formation, including Buchwald–Hartwig palladium-catalyzed amination of aryl and heteroaryl halides, renaissance of the Ullmann chemistry, i.e., the application of catalysis by copper complexes to form the carbon–nitrogen bond, and Chan–Lam reactions of (hetero)arylboronic acids with amines. Also, oxidative amination with C–H activation, which has been booming during the last decade, is addressed. Particular attention is paid to achievements in the application of heterogenized catalysts.

The bibliography includes 350 references.

Metal-Free Synthesis of Heteroaryl Amines or Their Hydrochlorides via an External-Base-Free and Solvent-Free C-N Coupling Protocol

Herein, a metal-free and solvent-free protocol was developed for the C-N coupling of heteroaryl halides and amines, which afforded numerous heteroaryl amines or their hydrochlorides without any external base. Further investigations elucidated that the basicity of amines and specific interactions derived from the X-ray crystallography analysis of 3j'·HCl played pivotal roles in the reactions. Moreover, this protocol was scalable to gram scales and applicable to drug molecules, which demonstrated its practical value for further applications.

Rapid Optimization of Photoredox Reactions for Continuous-Flow Systems Using Microscale Batch Technology

Photoredox catalysis has emerged as a powerful and versatile platform for the synthesis of complex molecules. While photocatalysis is already broadly used in small-scale batch chemistry across the pharmaceutical sector, recent efforts have focused on performing these transformations in process chemistry due to the inherent challenges of batch photocatalysis on scale. However, translating optimized batch conditions to flow setups is challenging, and a general approach that is rapid, convenient, and inexpensive remains largely elusive. Herein, we report the development of a new approach that uses a microscale high-throughput experimentation (HTE) platform to identify optimal reaction conditions that can be directly translated to flow systems. A key design point is to simulate the flow-vessel pathway within a microscale reaction plate, which enables the rapid identification of optimal flow reaction conditions using only a small number of simultaneous experiments. This approach has been validated against a range of widely used photoredox reactions and, importantly, was found to translate accurately to several commercial flow reactors. We expect that the generality and operational efficiency of this new HTE approach to photocatalysis will allow rapid identification of numerous flow protocols for scale.

Multi-Step Continuous-Flow Organic Synthesis: Opportunities and Challenges

Continuous-flow multi-step synthesis takes the advantages of microchannel flow chemistry and may transform the conventional multi-step organic synthesis by using integrated synthetic systems. To realize the goal, however, innovative chemical methods and techniques are urgently required to meet the significant remaining challenges. In the past few years, by using green reactions, telescoped chemical design, and/or novel in-line separation techniques, major and rapid advancement has been made in this direction. This minireview summarizes the most recent reports (2017-2020) on continuous-flow synthesis of functional molecules. Notably, several complex active pharmaceutical ingredients (APIs) have been prepared by the continuous-flow approach. Key technologies to the successes and remaining challenges are discussed. These results exemplified the feasibility of using modern continuous-flow chemistry for complex synthetic targets, and bode well for the future development of integrated, automated artificial synthetic systems.

Evidence for “cocktail”-type catalysis in Buchwald–Hartwig reaction. A mechanistic study

The mechanism of the C–N cross-coupling reaction, catalyzed by Pd/NHC, was evaluated at the molecular and nanoscale levels. The first evidence for the involvement of a “cocktail”-type system in the Buchwald–Hartwig reaction is provided.

Design of dynamic trajectories for efficient and data-rich exploration of flow reaction design spaces

Sinusoidal variations of operative parameters in flow chemistry allows the fast exploration of chemical design spaces through inline measurements of an objective function.

Microfluidic High-Throughput Platforms for Discovery of Novel Materials

High-throughput screening is a potent technique to accelerate the discovery and development of new materials. By performing massive synthesis and characterization processes in parallel, it can rapidly discover materials with desired components, structures and functions. Among the various approaches for high-throughput screening, microfluidic platforms have attracted increasing attention. Compared with many current strategies that are generally based on robotic dispensers and automatic microplates, microfluidic platforms can significantly increase the throughput and reduce the consumption of reagents by several orders of magnitude. In this review, we first introduce current advances of the two types of microfluidic high-throughput platforms based on microarrays and microdroplets, respectively. Then the utilization of these platforms for screening different types of materials, including inorganic metals, metal alloys and organic polymers are described in detail. Finally, the challenges and opportunities in this promising field are critically discussed.

Aryl Amination Using Soluble Weak Base Enabled by a Water-Assisted Mechanism

The amination of aryl halides has become one of the most commonly practiced C-N bond-forming reactions in pharmaceutical and laboratory syntheses. The widespread use of strong or poorly soluble inorganic bases for amine activation nevertheless complicates the compatibility of this important reaction class with sensitive substrates as well as applications in flow and automated synthesis, to name a few. We report a palladium-catalyzed C-N coupling using Et₃N as a weak, soluble base, which allows a broad substrate scope that includes bromo- and chloro(hetero)arenes, primary anilines, secondary amines, and amide type nucleophiles together with tolerance for a range of base-sensitive functional groups. Mechanistic data have established a unique pathway for these reactions in which water serves multiple beneficial roles. In particular, ionization of a neutral catalytic intermediate via halide displacement by H₂O generates, after proton loss, a coordinatively unsaturated Pd-OH species that can bind amine substrate triggering intramolecular N-H heterolysis. This water-assisted pathway operates efficiently with even weak terminal bases, such as Et₃N. The use of a simple, commercially available ligand, PAd₃, is key to this water-assisted mechanism by promoting coordinative unsaturation in catalytic intermediates responsible for the heterolytic activation of strong element-hydrogen bonds, which enables broad compatibility of carbon-heteroatom cross-coupling reactions with sensitive substrates and functionality.

Flow chemistry as a tool to access novel chemical space for drug discovery

This perspective scrutinizes flow chemistry as a useful tool for medicinal chemists to expand the current chemical capabilities in drug discovery. This technology has demonstrated his value not only for the traditional reactions used in Pharma for the last 20 years, but also for bringing back to the lab underused chemistries to access novel chemical space. The combination with other technologies, such as photochemistry and electrochemistry, is opening new avenues for reactivity that will smoothen the access to complex molecules. The introduction of all these technologies in automated platforms will improve the productivity of medicinal chemistry labs reducing the cycle times to get novel and differentiated bioactive molecules, accelerating discovery cycle times.

Accelerated microdroplet synthesis of benzimidazoles by nucleophilic addition to protonated carboxylic acids

We report a metal-free novel route for the accelerated synthesis of benzimidazole and its derivatives in the ambient atmosphere. The synthetic procedure involves 1,2-aromatic diamines and alkyl or aryl carboxylic acids reacting in electrostatically charged microdroplets generated using a nano-electrospray (nESI) ion source. The reactions are accelerated by orders of magnitude in comparison to the bulk. No other acid, base or catalyst is used. Online analysis of the microdroplet accelerated reaction products is performed by mass spectrometry. We provide evidence for an acid catalyzed reaction mechanism based on identification of the intermediate arylamides. Their dehydration to give benzimidazoles occurs in a subsequent thermally enhanced step. It is suggested that the extraordinary acidity at the droplet surface allows the carboxylic acid to function as a C-centered electrophile. Comparisons of this methodology with data from thin film and bulk synthesis lead to the proposal of three key steps in the reaction: (i) formation of an unusual reagent (protonated carboxylic acid) because of the extraordinary conditions at the droplet interface, (ii) accelerated bimolecular reaction because of limited solvation at the interface and (iii) thermally assisted elimination of water. Eleven examples are shown as evidence of the scope of this chemistry. The accelerated synthesis has been scaled-up to establish the substituent-dependence and to isolate products for NMR characterization.

Light-Promoted Nickel Catalysis: Etherification of Aryl Electrophiles with Alcohols Catalyzed by a NiII -Aryl Complex

A highly effective C-O coupling reaction of (hetero)aryl electrophiles with primary and secondary alcohols is reported. Catalyzed by a Ni^II -aryl complex under long-wave UV (390-395 nm) irradiation in the presence of a soluble amine base without any additional photosensitizer, the reaction enables the etherification of aryl bromides and aryl chlorides as well as sulfonates with a wide range of primary and secondary aliphatic alcohols, affording synthetically important ethers. Intramolecular C-O coupling is also possible. The reaction appears to proceed via a Ni^I -Ni^III catalytic cycle.

The Quest for the Ideal Base: Rational Design of a Nickel Precatalyst Enables Mild, Homogeneous C-N Cross-Coupling

Palladium-catalyzed amination reactions using soluble organic bases have provided a solution to the many issues associated with heterogeneous reaction conditions. Still, homogeneous C-N cross-coupling approaches cannot yet employ bases as weak and economical as trialkylamines. Furthermore, organic base-mediated methods have not been developed for Ni(0/II) catalysis, despite some advantages of such systems over those employing Pd-based catalysts. We designed a new air-stable and easily prepared Ni(II) precatalyst bearing an electron-deficient bidentate phosphine ligand that enables the cross-coupling of aryl triflates with aryl amines using triethylamine (TEA) as base. The method is tolerant of sterically congested coupling partners, as well as those bearing base- and nucleophile-sensitive functional groups. With the aid of density functional theory (DFT) calculations, we determined that the electron-deficient auxiliary ligands decrease both the pK_a of the Ni-bound amine and the barrier to reductive elimination from the resultant Ni(II)-amido complex. Moreover, we determined that the preclusion of Lewis acid-base complexation between the Ni catalyst and the base, due to steric factors, is important for avoiding catalyst inhibition.

Enantioselective Iron/Bisquinolyldiamine Ligand-Catalyzed Oxidative Coupling Reaction of 2-Naphthols

An iron-catalyzed asymmetric oxidative homo-coupling of 2-naphthols for the synthesis of 1,1'-Bi-2-naphthol (BINOL) derivatives is reported. The coupling reaction provides enantioenriched BINOLs in good yields (up to 99%) and moderate enantioselectivities (up to 81:19 er) using an iron-complex generated in situ from Fe(ClO4)2 and a bisquinolyldiamine ligand [(1R,2R)-N1,N2-di(quinolin-8-yl)cyclohexane-1,2-diamine, L1]. A number of ligands (L2-L8) and the analogs of L1, with various substituents and chiral backbones, were synthesized and examined in the oxidative coupling reactions.