<i>In Situ </i>Studies of Arylboronic Acids/Esters and R₃SiCF₃ Reagents: Kinetics, Speciation, and Dysfunction at the Carbanion–Ate Interface

[Image: see text] Reagent instability reduces the efficiency of chemical processes, and while much effort is devoted to reaction optimization, less attention is paid to the mechanistic causes of reagent decomposition. Indeed, the response is often to simply use an excess of the reagent. Two reaction classes with ubiquitous examples of this are the Suzuki–Miyaura cross-coupling of boronic acids/esters and the transfer of CF(3) or CF(2) from the Ruppert–Prakash reagent, TMSCF(3). This Account describes some of the overarching features of our mechanistic investigations into their decomposition. In the first section we summarize how specific examples of (hetero)arylboronic acids can decompose via aqueous protodeboronation processes: Ar–B(OH)(2) + H(2)O → ArH + B(OH)(3). Key to the analysis was the development of a kinetic model in which pH controls boron speciation and heterocycle protonation states. This method revealed six different protodeboronation pathways, including self-catalysis when the pH is close to the pK(a) of the boronic acid, and protodeboronation via a transient aryl anionoid pathway for highly electron-deficient arenes. The degree of “protection” of boronic acids by diol-esterification is shown to be very dependent on the diol identity, with six-membered ring esters resulting in faster protodeboronation than the parent boronic acid. In the second section of the Account we describe (19)F NMR spectroscopic analysis of the kinetics of the reaction of TMSCF(3) with ketones, fluoroarenes, and alkenes. Processes initiated by substoichiometric “TBAT” ([Ph(3)SiF(2)][Bu(4)N]) involve anionic chain reactions in which low concentrations of [CF(3)](−) are rapidly and reversibly liberated from a siliconate reservoir, [TMS(CF(3))(2)][Bu(4)N]. Increased TMSCF(3) concentrations reduce the [CF(3)](−) concentration and thus inhibit the rates of CF(3) transfer. Computation and kinetics reveal that the TMSCF(3) intermolecularly abstracts fluoride from [CF(3)](−) to generate the CF(2), in what would otherwise be an endergonic α-fluoride elimination. Starting from [CF(3)](−) and CF(2), a cascade involving perfluoroalkene homologation results in the generation of a hindered perfluorocarbanion, [C(11)F(23)](−), and inhibition. The generation of CF(2) from TMSCF(3) is much more efficiently mediated by NaI, and in contrast to TBAT, the process undergoes autoacceleration. The process involves NaI-mediated α-fluoride elimination from [CF(3)][Na] to generate CF(2) and a [NaI·NaF] chain carrier. Chain-branching, by [(CF(2))(3)I][Na] generated in situ (CF(2) + TFE + NaI), causes autoacceleration. Alkenes that efficiently capture CF(2) attenuate the chain-branching, suppress autoacceleration, and lead to less rapid difluorocyclopropanation. The Account also highlights how a collaborative approach to experiment and computation enables mechanistic insight for control of processes

Kinetic analysis of bioorthogonal reaction mechanisms using Raman microscopy

Raman spectroscopy is well-suited to the study of bioorthogonal reaction processes because it is a non-destructive technique, which employs relatively low energy laser irradiation, and water is only very weakly scattered in the Raman spectrum enabling live cell imaging. In addition, Raman spectroscopy allows species-specific label-free visualisation; chemical contrast may be achieved when imaging a cell in its native environment without fixatives or stains. Combined with the rapid advances in the field of Raman imaging over the last decade, particularly in stimulated Raman spectroscopy (SRS), this technique has the potential to revolutionise our mechanistic understanding of the biochemical and medicinal chemistry applications of bioorthogonal reactions. Current approaches to the kinetic analysis of bioorthogonal reactions (including heat flow calorimetry, UV-vis spectroscopy, fluorescence, IR, NMR and MS) have a number of practical shortcomings for intracellular applications. We highlight the advantages offered by Raman microscopy for reaction analysis in the context of both established and emerging bioorthogonal reactions, including the copper(i) catalysed azide-alkyne cycloaddition (CuAAC) click reaction and Glaser-Hay coupling

Expansion of the ligand knowledge base for chelating P,P-donor ligands (LKB-PP)

[Image: see text] We have expanded the ligand knowledge base for bidentate P,P- and P,N-donor ligands (LKB-PP, Organometallics2008, 27, 1372–1383) by 208 ligands and introduced an additional steric descriptor (nHe(8)). This expanded knowledge base now captures information on 334 bidentate ligands and has been processed with principal component analysis (PCA) of the descriptors to produce a detailed map of bidentate ligand space, which better captures ligand variation and has been used for the analysis of ligand properties

Difluorocarbene Generation from TMSCF3: Kinetics and Mechanism of NaI-Mediated and Si-Induced Anionic Chain Reactions

The mechanism of CF2 transfer from TMSCF3 ( 1 ), mediated by TBAT (2–12 mol %) or by NaI (5–20 mol %), has been investigated by in situ/stopped-flow 19F NMR spectroscopic analysis of the kinetics of alkene difluorocyclopropanation and competing TFE/c-C3F6/homologous perfluoroanion generation, 13C/2H KIEs, LFERs, CF2 transfer efficiency and selectivity, the effect of inhibitors, and density functional theory (DFT) calculations. The reactions evolve with profoundly different kinetics, undergoing autoinhibition (TBAT) or quasi-stochastic autoacceleration (NaI) and cogenerating perfluoroalkene side products. An overarching mechanism involving direct and indirect fluoride transfer from a CF3 anionoid to TMSCF3 ( 1 ) has been elucidated. It allows rationalization of why the NaI-mediated process is more effective for less-reactive alkenes and alkynes, why a large excess of TMSCF3 ( 1 ) is required in all cases, and why slow-addition protocols can be of benefit. Issues relating to exothermicity, toxicity, and scale-up are also noted.PostprintPeer reviewe

Protodeboronation of heteroaromatic, vinyl and cyclopropyl boronic acids: pH-rate profiles, auto-catalysis and disproportionation

pH–rate profiles for aqueous–organic protodeboronation of 18 boronic acids, many widely viewed as unstable, have been studied by NMR and DFT. Rates were pH-dependent, and varied substantially between the boronic acids, with rate maxima that varied over 6 orders of magnitude. A mechanistic model containing five general pathways (<i>k</i>₁–<i>k</i>₅) has been developed, and together with input of [B]_tot, <i>K</i>_W, <i>K</i>_a, and <i>K</i>_aH, the protodeboronation kinetics can be correlated as a function of pH (1–13) for all 18 species. Cyclopropyl and vinyl boronic acids undergo very slow protodeboronation, as do 3- and 4-pyridyl boronic acids (<i>t</i>_0.5 > 1 week, pH 12, 70 °C). In contrast, 2-pyridyl and 5-thiazolyl boronic acids undergo rapid protodeboronation (<i>t</i>_0.5 ≈ 25–50 s, pH 7, 70 °C), via fragmentation of zwitterionic intermediates. Lewis acid additives (e.g., Cu, Zn salts) can attenuate (2-pyridyl) or accelerate (5-thiazolyl and 5-pyrazolyl) fragmentation. Two additional processes compete when the boronic acid <i>and</i> the boronate are present in sufficient proportions (pH = p<i>K</i>_a ± 1.6): (i) self-/autocatalysis and (ii) sequential disproportionations of boronic acid to borinic acid and borane

Sex dimorphism in the myocardial response to aortic stenosis

Objectives: The goal of this study was to explore sex differences in myocardial remodeling in aortic stenosis (AS) by using echocardiography, cardiac magnetic resonance (CMR), and biomarkers. Background: AS is a disease of both valve and left ventricle (LV). Sex differences in LV remodeling are reported in AS and may play a role in disease phenotyping. Methods: This study was a prospective assessment of patients awaiting surgical valve replacement for severe AS using echocardiography, the 6-min walking test, biomarkers (high-sensitivity troponin T and N-terminal pro-brain natriuretic peptide), and CMR with late gadolinium enhancement and extracellular volume fraction, which dichotomizes the myocardium into matrix and cell volumes. LV remodeling was categorized into normal geometry, concentric remodeling, concentric hypertrophy, and eccentric hypertrophy

Parameterization Effects in the analysis of AMI Sunyaev-Zel'dovich Observations

Most Sunyaev--Zel'dovich (SZ) and X-ray analyses of galaxy clusters try to constrain the cluster total mass and/or gas mass using parameterised models and assumptions of spherical symmetry and hydrostatic equilibrium. By numerically exploring the probability distributions of the cluster parameters given the simulated interferometric SZ data in the context of Bayesian methods, and assuming a beta-model for the electron number density we investigate the capability of this model and analysis to return the simulated cluster input quantities via three rameterisations. In parameterisation I we assume that the T is an input parameter. We find that parameterisation I can hardly constrain the cluster parameters. We then investigate parameterisations II and III in which fg(r200) replaces temperature as a main variable. In parameterisation II we relate M_T(r200) and T assuming hydrostatic equilibrium. We find that parameterisation II can constrain the cluster physical parameters but the temperature estimate is biased low. In parameterisation III, the virial theorem replaces the hydrostatic equilibrium assumption. We find that parameterisation III results in unbiased estimates of the cluster properties. We generate a second simulated cluster using a generalised NFW (GNFW) pressure profile and analyse it with an entropy based model to take into account the temperature gradient in our analysis and improve the cluster gas density distribution. This model also constrains the cluster physical parameters and the results show a radial decline in the gas temperature as expected. The mean cluster total mass estimates are also within 1 sigma from the simulated cluster true values. However, we find that for at least interferometric SZ analysis in practice at the present time, there is no differences in the AMI visibilities between the two models. This may of course change as the instruments improve.Comment: 19 pages, 13 tables, 24 figure

Sunyaev–Zel’dovich observations with AMI of the hottest galaxy clusters detected in the XMM–Newton Cluster Survey

We have obtained deep Sunyaev–Zel’dovich (SZ) observations towards 15 of the hottest XMM Cluster Survey (XCS) clusters that can be observed with the Arcminute Microkelvin Imager (AMI). We use a Bayesian analysis to quantify the significance of our SZ detections. We detect the SZ effect at high significance towards three of the clusters and at lower significance for a further two clusters. Towards the remaining 10 clusters, no clear SZ signal was measured. We derive cluster parameters using the XCS mass estimates as a prior in our Bayesian analysis. For all AMI-detected clusters, we calculate large-scale mass and temperature estimates while for all undetected clusters we determine upper limits on these parameters. We find that the large-scale mean temperatures derived from our AMI SZ measurements (and the upper limits from null detections) are substantially lower than the XCS-based core-temperature estimates. For clusters detected in the SZ, the mean temperature is, on average, a factor of 1.4 lower than temperatures from the XCS. Our upper limits on the cluster temperature of undetected systems are lower than the mean XCS derived temperature