Mechanism of Ni-Catalyzed Photochemical Halogen Atom-Mediated C(sp3)-H Arylation.

Within the context of Ni photoredox catalysis, halogen atom photoelimination from Ni has emerged as a fruitful strategy for enabling hydrogen atom transfer (HAT)-mediated C(sp3)-H functionalization. Despite the numerous synthetic transformations invoking this paradigm, a unified mechanistic hypothesis that is consistent with experimental findings on the catalytic systems and accounts for halogen radical formation and facile C(sp2)-C(sp3) bond formation remains elusive. We employ kinetic analysis, organometallic synthesis, and computational investigations to decipher the mechanism of a prototypical Ni-catalyzed photochemical C(sp3)-H arylation reaction. Our findings revise the previous mechanistic proposals, first by examining the relevance of SET and EnT processes from Ni intermediates relevant to the HAT-based arylation reaction. Our investigation highlights the ability for blue light to promote efficient Ni-C(sp2) bond homolysis from cationic NiIII and C(sp2)-C(sp3) reductive elimination from bipyridine NiII complexes. However interesting, the rates and selectivities of these processes do not account for the productive catalytic pathway. Instead, our studies support a mechanism that involves halogen atom evolution from in situ generated NiII dihalide intermediates, radical capture by a NiII(aryl)(halide) resting state, and key C-C bond formation from NiIII. Oxidative addition to NiI, as opposed to Ni0, and rapid NiIII/NiI comproportionation play key roles in this process. The findings presented herein offer fundamental insight into the reactivity of Ni in the broader context of catalysis.

A multiple technology-based and individually-tailored Sit Less program for people with cardiovascular disease: A randomized controlled trial study protocol.

Sedentary behavior, a key modifiable risk factor for cardiovascular disease, is prevalent among cardiovascular disease patients. However, few interventions target sedentary behavior in this group. This paper describes the protocol of a parallel two-group randomized controlled trial for a novel multi-technology sedentary behavior reduction intervention for cardiovascular disease patients (registered at Clinicaltrial.gov, NCT05534256). The pilot trial (n = 70) will test a 12-week "Sit Less" program, based on Habit Formation theory. The 35 participants in the intervention group will receive an instructional goal-setting session, a Fitbit for movement prompts, a smart water bottle (HidrateSpark) to promote hydration and encourage restroom breaks, and weekly personalized text messages. A control group of 35 will receive the American Heart Association's "Answers by Heart" fact sheets. This trial will assess the feasibility and acceptability of implementing the "Sit Less" program with cardiovascular disease patients and the program's primary efficacy in changing sedentary behavior, measured by the activPAL activity tracker. Secondary outcomes include physical activity levels, cardiometabolic biomarkers, and patient-centered outcomes (i.e. sedentary behavior self-efficacy, habit strength, and fear of movement). This study leverages commonly used mobile and wearable technologies to address sedentary behavior in cardiovascular disease patients, a high-risk group. Its findings on the feasibility, acceptability and primary efficacy of the intervention hold promise for broad dissemination.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

Identifying general reaction conditions by bandit optimization.

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.

Large area fractional laser treatment of mouse skin increases energy expenditure.

Fractional laser (FL) treatment is a common dermatologic procedure that generates arrays of microscopic treatment zones separated by intact tissue, promoting fast wound healing. Using a mouse model, we introduced a large area fractional laser treatment (LAFLT) method to study metabolic effects. Using two laser modalities, ablative FL (AFL) and non-ablative FL (NAFL), and exposing different percentages of mice's total body surface area (TBSA), we followed changes in metabolic parameters in real time using metabolic cages. Additionally, body composition, markers of inflammation, neurohormonal signaling, and browning of adipocytes were investigated. LAFLT, especially in high TBSA groups, had specific metabolic effects such as significantly increased average daily energy expenditure, increased fat mass loss, systemic browning of adipocytes, and inflammatory states, without compromising other organs. The ability of LAFLT to stimulate metabolism in a controlled way could develop into a promising therapeutic treatment to induce positive metabolic changes that replace or augment systemic drugs.

Broad Survey of Selectivity in the Heterogeneous Hydrogenation of Heterocycles.

A broad survey of heterogeneous hydrogenation catalysts has been conducted for the reduction of heterocycles commonly found in pharmaceuticals. The comparative reactivity of these substrates is reported as a function of catalyst, temperature, and hydrogen pressure. This analysis provided several catalysts with complementary reactivity between substrates. We then explored a series of bisheterocyclic substrates that provided an intramolecular competition of heterocycle hydrogenation reactivity. In several cases, complete selectivity could be achieved for reduction of one heterocycle and isolated yields are reported. A general trend in reactivity is inferred in which quinoline is the most reactive, followed by pyrazine, then pyrrole and with pyridine being the least reactive.

Comparison of Monophosphine and Bisphosphine Precatalysts for Ni-Catalyzed Suzuki-Miyaura Cross-Coupling: Understanding the Role of the Ligation State in Catalysis.

Practical advances in Ni-catalyzed Suzuki-Miyaura cross-coupling (SMC) have been limited by a lack of mechanistic understanding of phosphine ligand effects. While bisphosphines are commonly used in these methodologies, we have observed instances where monophosphines can provide comparable or higher levels of reactivity. Seeking to understand the role of ligation state in catalysis, we performed a head-to-head comparison study of C(sp2)-C(sp2) Ni SMCs catalyzed by mono and bisphosphine precatalysts using six distinct substrate pairings. Significant variation in optimal precatalyst was observed, with the monophosphine precatalyst tending to outperform the bisphosphines with electronically deactivated and sterically hindered substrates. Mechanistic experiments revealed a role for monoligated (P1Ni) species in accelerating the fundamental organometallic steps of the catalytic cycle, while highlighting the need for bisligated (P2Ni) species to avoid off-cycle reactivity and catalyst poisoning by heterocyclic motifs. These findings provide guidelines for ligand selection against challenging substrates and future ligand design tailored to the mechanistic demands of Ni-catalyzed SMCs.

Branched-Selective Cross-Electrophile Coupling of 2-Alkyl Aziridines and (Hetero)aryl Iodides Using Ti/Ni Catalysis.

The arylation of 2-alkyl aziridines by nucleophilic ring-opening or transition-metal-catalyzed cross-coupling enables facile access to biologically relevant ß-phenethylamine derivatives. However, both approaches largely favor C-C bond formation at the less-substituted carbon of the aziridine, thus enabling access to only linear products. Consequently, despite the attractive bond disconnection that it poses, the synthesis of branched arylated products from 2-alkyl aziridines has remained inaccessible. Herein, we address this long-standing challenge and report the first branched-selective cross-coupling of 2-alkyl aziridines with aryl iodides. This unique selectivity is enabled by a Ti/Ni dual-catalytic system. We demonstrate the robustness of the method by a twofold approach: an additive screening campaign to probe functional group tolerance and a feature-driven substrate scope to study the effect of the local steric and electronic profile of each coupling partner on reactivity. Furthermore, the diversity of this feature-driven substrate scope enabled the generation of predictive reactivity models that guided mechanistic understanding. Mechanistic studies demonstrated that the branched selectivity arises from a TiIII-induced radical ring-opening of the aziridine.

Synthesis of Nickel(I)-Bromide Complexes via Oxidation and Ligand Displacement: Evaluation of Ligand Effects on Speciation and Reactivity.

Nickel's +1 oxidation state has received much interest due to its varied and often enigmatic behavior in increasingly popular catalytic methods. In part, the lack of understanding about NiI results from common synthetic strategies limiting the breadth of complexes that are accessible for mechanistic study and catalyst design. We report an oxidative approach using tribromide salts that allows for the generation of a well-defined precursor, [NiI(COD)Br]2, as well as several new NiI complexes. Included among them are complexes bearing bulky monophosphines, for which structure-speciation relationships are established and catalytic reactivity in a Suzuki-Miyaura coupling (SMC) is investigated. Notably, these routes also allow for the synthesis of well-defined monomeric t-Bubpy-bound NiI complexes, which has not previously been achieved. These complexes, which react with aryl halides, can enable previously challenging mechanistic investigations and present new opportunities for catalysis and synthesis.

Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning.

We report a human-in-the-loop implementation of the multi-objective experimental design via a Bayesian optimization platform (EDBO+) towards the optimization of butylpyridinium bromide synthesis under continuous flow conditions. The algorithm simultaneously optimized reaction yield and production rate (or space-time yield) and generated a well defined Pareto front. The versatility of EDBO+ was demonstrated by expanding the reaction space mid-campaign by increasing the upper temperature limit. Incorporation of continuous flow techniques enabled improved control over reaction parameters compared to common batch chemistry processes, while providing a route towards future automated syntheses and improved scalability. To that end, we applied the open-source Python module, nmrglue, for semi-automated nuclear magnetic resonance (NMR) spectroscopy analysis, and compared the acquired outputs against those obtained through manual processing methods from spectra collected on both low-field (60 MHz) and high-field (400 MHz) NMR spectrometers. The EDBO+ based model was retrained with these four different datasets and the resulting Pareto front predictions provided insight into the effect of data analysis on model predictions. Finally, quaternization of poly(4-vinylpyridine) with bromobutane illustrated the extension of continuous flow chemistry to synthesize functional materials.

A General Photocatalytic Strategy for Nucleophilic Amination of Primary and Secondary Benzylic C-H Bonds.

We report a visible-light photoredox-catalyzed method that enables nucleophilic amination of primary and secondary benzylic C(sp3)-H bonds. A novel amidyl radical precursor and organic photocatalyst operate in tandem to transform primary and secondary benzylic C(sp3)-H bonds into carbocations via sequential hydrogen atom transfer (HAT) and oxidative radical-polar crossover. The resulting carbocation can be intercepted by a variety of N-centered nucleophiles, including nitriles (Ritter reaction), amides, carbamates, sulfonamides, and azoles, for the construction of pharmaceutically relevant C(sp3)-N bonds under unified reaction conditions. Mechanistic studies indicate that HAT is amidyl radical-mediated and that the photocatalyst operates via a reductive quenching pathway. These findings establish a mild, metal-free, and modular protocol for the rapid diversification of C(sp3)-H bonds to a library of aminated products.

Novel assessment of lip redness and microcirculation using optical coherence tomography after dermal filler injection.

OBJECTIVES: Lip filler injections are one of the most popular procedures in esthetic dermatology. In this study, we used three-dimensional colorimetric photography to assess lip color and optical coherence tomography-angiography (OCT-A), a noninvasive alternative to histopathology, to evaluate microcirculation after hyaluronic acid (HA) injection. The pain of the injection procedure was also assessed. METHODS: An average of 0.85cc of the total volume of HA with lidocaine was injected into the upper and lower lip of eighteen young (<30yo) and nine postmenopausal healthy women. OCT-A, two-dimensional, and three-dimensional images were acquired immediately before (visit 1) and 15 days after injection (visit 2). Custom-made software was used to analyze the imaging data to detect vessel morphology and redness changes. The Wong-Baker FACES pain rating scale (0-10) was used to score the subject procedural pain. RESULTS: For young and old subjects, three-dimensional lip volume was greater than the injected volume. OCT-A images of the lips showed higher vessel density and thickness, reaching statistical significance in the younger cohort. The overall trend of increased redness assessed by three-dimensional colorimetric imaging and increased vascularity evaluated by OCT-A imaging were similar. However, the correlation was not statistically significant for standard two-dimensional digital photography. The average pain score after the first needle insertion and overall procedure were 2.9 and 3.5, respectively. CONCLUSIONS: The results suggest an increased microvasculature network observed in OCT-A images in young females. The increased blood vessel density and thickness observed by OCT-A after HA lip filler injection is associated with increased lip redness and volume as assessed by colorimetric three-dimensional photography; however, more research is needed to confirm these findings. This study presents OCT-A as a novel noninvasive tool to investigate changes in lip microvascularity after HA filler injection and indicates that HA filler procedures may affect lip vascularity.

On the use of real-world datasets for reaction yield prediction.

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki-Miyaura and Buchwald-Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.

Interrogating the Mechanistic Features of Ni(I)-Mediated Aryl Iodide Oxidative Addition Using Electroanalytical and Statistical Modeling Techniques.

While the oxidative addition of Ni(I) to aryl iodides has been commonly proposed in catalytic methods, an in-depth mechanistic understanding of this fundamental process is still lacking. Herein, we describe a detailed mechanistic study of the oxidative addition process using electroanalytical and statistical modeling techniques. Electroanalytical techniques allowed rapid measurement of the oxidative addition rates for a diverse set of aryl iodide substrates and four classes of catalytically relevant complexes (Ni(MeBPy), Ni(MePhen), Ni(Terpy), and Ni(BPP)). With >200 experimental rate measurements, we were able to identify essential electronic and steric factors impacting the rate of oxidative addition through multivariate linear regression models. This has led to a classification of oxidative addition mechanisms, either through a three-center concerted or halogen-atom abstraction pathway based on the ligand type. A global heat map of predicted oxidative addition rates was created and shown applicable to a better understanding of the reaction outcome in a case study of a Ni-catalyzed coupling reaction.

Strategies for Nucleophilic C(sp3)-(Radio)Fluorination.

This Perspective surveys the progress and current limitations of nucleophilic fluorination methodologies. Despite the long and rich history of C(sp3)-F bond construction in chemical research, the inherent challenges associated with this transformation have largely constrained nucleophilic fluorination to a privileged reaction platform. In recent years, the Doyle group─along with many others─has pursued the study and development of this transformation with the intent of generating deeper mechanistic understanding, developing user-friendly fluorination reagents, and contributing to the invention of synthetic methods capable of enabling radiofluorination. Studies from our laboratory are discussed along with recent developments from others in this field. Fluoride reagent development and the mechanistic implications of reagent identity are highlighted. We also outline the chemical space inaccessible by current synthetic technologies and a series of future directions in the field that can potentially fill the existing dark spaces.

A Machine Learning Approach to Model Interaction Effects: Development and Application to Alcohol Deoxyfluorination.

The application of machine learning (ML) techniques to model high-throughput experimentation (HTE) datasets has seen a recent rise in popularity. Nevertheless, the ability to model the interplay between reaction components, known as interaction effects, with ML remains an outstanding challenge. Using a simulated HTE dataset, we find that the presence of irrelevant features poses a strong obstacle to learning interaction effects with common ML algorithms. To address this problem, we propose a two-part statistical modeling approach for HTE datasets: classical analysis of variance of the experiment to identify systematic effects that impact reaction yield across the experiment followed by regression of individual effects using chemistry-informed features. To illustrate this methodology, we use our previously published alcohol deoxyfluorination dataset comprising 740 reactions to build a compact, interpretable generalized additive model that accounts for each significant effect observed in the dataset. We achieve a sizeable performance boost compared to our previously published random forest model, reducing mean absolute error from 18 to 13% and root-mean-squared error from 22 to 17% on a newly generated validation set. Finally, we demonstrate that this approach can facilitate the generation of new mechanistic hypotheses, which, when probed experimentally, can lead to a deeper understanding of chemical reactivity.

Dataset Design for Building Models of Chemical Reactivity.

Models can codify our understanding of chemical reactivity and serve a useful purpose in the development of new synthetic processes via, for example, evaluating hypothetical reaction conditions or in silico substrate tolerance. Perhaps the most determining factor is the composition of the training data and whether it is sufficient to train a model that can make accurate predictions over the full domain of interest. Here, we discuss the design of reaction datasets in ways that are conducive to data-driven modeling, emphasizing the idea that training set diversity and model generalizability rely on the choice of molecular or reaction representation. We additionally discuss the experimental constraints associated with generating common types of chemistry datasets and how these considerations should influence dataset design and model building.

Radical Redox Annulations: A General Light-Driven Method for the Synthesis of Saturated Heterocycles.

We introduce here a two-component annulation strategy that provides access to a diverse collection of five- and six-membered saturated heterocycles from aryl alkenes and a family of redox-active radical precursors bearing tethered nucleophiles. This transformation is mediated by a combination of an Ir(III) photocatalyst and a Brønsted acid under visible-light irradiation. A reductive proton-coupled electron transfer generates a reactive radical which undergoes addition to an alkene. Then, an oxidative radical-polar crossover step leading to carbocation formation is followed by ring closure through cyclization of the tethered nucleophile. A wide range of heterocycles are easily accessible, including pyrrolidines, piperidines, tetrahydrofurans, morpholines, δ-valerolactones, and dioxanones. We demonstrate the scope of this approach through broad structural variation of both reaction components. This method is amenable to gram-scale preparation and to complex fragment coupling.

Structure-Reactivity Relationships of Buchwald-Type Phosphines in Nickel-Catalyzed Cross-Couplings.

The dialkyl-ortho-biaryl class of phosphines, commonly known as Buchwald-type ligands, are among the most important phosphines in Pd-catalyzed cross-coupling. These ligands have also been successfully applied to several synthetically valuable Ni-catalyzed cross-coupling methodologies and, as demonstrated in this work, are top performing ligands in Ni-catalyzed Suzuki Miyaura Coupling (SMC) and C-N coupling reactions, even outperforming commonly employed bisphosphines like dppf in many circumstances. However, little is known about their structure-reactivity relationships (SRRs) with Ni, and limited examples of well-defined, catalytically relevant Ni complexes with Buchwald-type ligands exist. In this work, we report the analysis of Buchwald-type phosphine SRRs in four representative Ni-catalyzed cross-coupling reactions. Our study was guided by data-driven classification analysis, which together with mechanistic organometallic studies of structurally characterized Ni(0), Ni(I), and Ni(II) complexes allowed us to rationalize reactivity patterns in catalysis. Overall, we expect that this study will serve as a platform for further exploration of this ligand class in organonickel chemistry as well as in the development of new Ni-catalyzed cross-coupling methodologies.

A Multi-Objective Active Learning Platform and Web App for Reaction Optimization.

We report the development of an open-source experimental design via Bayesian optimization platform for multi-objective reaction optimization. Using high-throughput experimentation (HTE) and virtual screening data sets containing high-dimensional continuous and discrete variables, we optimized the performance of the platform by fine-tuning the algorithm components such as reaction encodings, surrogate model parameters, and initialization techniques. Having established the framework, we applied the optimizer to real-world test scenarios for the simultaneous optimization of the reaction yield and enantioselectivity in a Ni/photoredox-catalyzed enantioselective cross-electrophile coupling of styrene oxide with two different aryl iodide substrates. Starting with no previous experimental data, the Bayesian optimizer identified reaction conditions that surpassed the previously human-driven optimization campaigns within 15 and 24 experiments, for each substrate, among 1728 possible configurations available in each optimization. To make the platform more accessible to nonexperts, we developed a graphical user interface (GUI) that can be accessed online through a web-based application and incorporated features such as condition modification on the fly and data visualization. This web application does not require software installation, removing any programming barrier to use the platform, which enables chemists to integrate Bayesian optimization routines into their everyday laboratory practices.