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Interrogating Explicit Solvent Effects on the Mechanism and Site-Selectivity of Aryl Halide Oxidative Addition to L2Pd(0)
No full textWe report a study of solvent effects on the rate, selectivity, and mechanism of (hetero)aryl (pseudo)halide oxidative addition to Pd(PCy3)2 as an exemplar of L2Pd(0) species. First, 2-chloro-3-aminopyridine is observed to undergo faster oxidative addition in toluene compared to more polar solvents, which is not consistent with the trend we observe with many other 2-halopyridines. We attribute this to solvent basicity hydrogen-bonding between solvent and substrate. Greater hydrogen-bond donation from the substrate leads to a more electron-rich aromatic system, and therefore slower oxidative addition. We demonstrate how this affects rate and site-selectivity for hydrogen-bond donating substrates. Second, electron-deficient multihalogenated pyridines exhibit improved site-selectivity in polar solvents, which we attribute to different C–X sites undergoing oxidative addition by two different mechanisms. The C–X site that favours the more polar nucleophilic displacement transition state is preferred over the site that favours a less-polar 3-centered transition state. Finally, (hetero)aryl triflates consistently undergo faster oxidative addition in more polar solvents, which we attribute to highly polar nucleophilic displacement transition states. This leads to improved site-selectivity for C–OTf oxidative addition, even in the presence of highly reactive 2-pyridyl halides
Photoresponsive block copolymer nanostructures through implementation of arylazopyrazoles
No full textResponsive nanomaterials that can undergo reversible changes in morphology are interesting for the development of functional materials that interact with and respond to their environment. Amphiphilic block copolymers are well known for their ability to create a wide range of supramolecular nanostructures in solution. Arylazopyrazoles (AAPs) are versatile molecular photoswitches, which change their configuration and hydrophobicity via irradiation with UV light (365 nm, Z isomer, less hydrophobic) and green light (520 nm, E isomer, more hydrophobic). In this work, photoswitchable block copolymers containing arylazopyrazole tetraethylene glycol methacrylate (AAPMA) and oligo(ethylene glycol) methacrylate (OEGMA) forming amphiphilic POEGMA b PAAPMA with varying block lengths are prepared by RAFT polymerization. The photochemical properties of AAP persist in the polymers. Due to their amphiphilic structure, the polymers self-assemble into supramolecular morphologies in water. Remarkably, photoisomerization results in a reversible change in the self-assembly behavior. Specifically, spherical and cylindrical micelles are observed for POEGMA33-b-PAAPMA47 when illuminated under green or UV light during assembly. Furthermore, the morphology of assembled structures can be reversibly switched by subsequent irradiation with UV and green light
Potential-dependent polaron formation activates TiO2 for the hydrogen evolution reaction
No full textPolarons and defects are recognized to be crucial in semiconductor (photo)electrocatalysis, yet their precise impact on charge transfer and electrocatalytic activity has remained elusive. In this study, we investigated the influence of potential-dependent polaron and defect formation on a prototypical TiO2 semiconductor for a hydrogen evolution reaction (HER), combining grand canonical ensemble density functional theory (GCE-DFT) calculations with multiple (spectro)electrochemical experiments. Our joint experimental and theoretical exploration unveiled notable changes in the TiO2 electronic structure with the application of an electrode potential resulting in the reduction of Ti4+ to Ti3+ surface polarons at reducing electrode potentials. We demonstrate that potential-dependent polaron formation creates highly active sites for HER, enhances conductivity, and breaks the Butler-Volmer-like kinetics highlighting the qualitatively different behavior between semiconducting TiO2 and metallic electrodes. Overall, our findings furnish compelling evidence and atomistic understanding of the pivotal role that potential-dependent polaron formation plays in semiconductor (photo)electrocatalysis
Electrically-polarized nanoscale surfaces generate reactive oxygenated and chlorinated species for deactivation of microorganisms
No full textDue to dwindling supply of new antibiotics, recent outbreaks of infectious diseases, and the emergence of antibiotic-resistant microorganisms, it is imperative to develop new effective strategies for deactivating a broad-spectrum of microorganisms and viruses. We have implemented electrically polarized nanoscale metallic coatings (ENM) that deactivate a wide range of microorganisms including Gram-negative and Gram-positive bacteria with greater than six-log reduction, in less than ten minutes of treatment. The electrically-polarized devices were also effective in deactivating lentivirus and C. albicans. The key to high deactivation effectiveness of ENM devices is electrochemical production of micromolar cuprous ions, which mediated reduction of oxygen to hydrogen peroxide. Formation of highly damaging species, hydroxyl radicals and hypochlorous acid, from hydrogen peroxide contributed to antimicrobial properties of the ENM devices. The electric polarization of nanoscale coatings represents an unconventional tool for deactivating a broad-spectrum of microorganisms through in-situ production of reactive oxygenated and chlorinated species
HSQCid: A Powerful Tool for Paving the Way to High-throughput Structural Dereplication of Natural Products Based on Fast NMR Experiments
No full textStructural dereplication is an essential step of the studies of natural products (NPs). The number of found NPs is so large that efficient dereplication is highly desirable. NMR spectroscopy is still the gold standard of structural identification. 13C NMR spectra is an effective molecular fingerprint but its acquisition is time-consuming, especially for mass-limited NPs. Several alternative meth-ods or tools have been proposed but never reached general use for some reasons. Here, a new artificial intelligence tool using con-trastive learning between 1H-13C HSQC spectra and structures, HSQCid, is proposed for effective structural identification. Two structure encoders are compared and Graph neural network is preferred over Transformer. In this way, 80% and 20% of about 400K predicted data could be used for training and testing, respectively. Besides, with 18K experimental data as external test data, top-1 and top-5 accuracy reaches 74.9% and 92.2%, respectively. Top-1 accuracy increases by at least 12% when combined with other easily obtainable structure features, such as total number of hydrogens connected to carbons from 1H NMR spectra. Further data analysis shows that the filters by structure features nearly eliminate the influence (>10%) of the difference between predicted and experimental data. Surprisingly the influence of the number or the ratio of quaternary carbons on the identification accuracy is only significant in specific and rare cases (less than 3%). Furthermore, benchmark method by matching 13C peaks is compared and markedly inferior to the proposed method. HSQCid will be available online in the near future for free public use. It is believed that HSQCid contributes to paving the way to high throughput or highly effective structural dereplication for NP
Accelerating Density Matrix Embedding with Stochastic Density Fitting Theory – An Application to Hydrogen Bonded Clusters
No full textIn this work, we demonstrate how using semi-stochastic Density Fitting (ss-DF) can accelerate self-consistent Density Matrix Embedding Theory (DMET) calculations by reducing the number of auxiliary orbitals in the three-indexed DF integrals. This reduction results in significant time savings when building the Hartree Fock (HF) Coulomb and Exchange Matrices and in transforming integrals from the Atomic Orbital (AO) basis to the Embedding Orbital (EO) basis. We apply ss-DF to a range of hydrogen-bonded clusters to showcase its effectiveness. First, we examine how the amount of deterministic space impacts the quality of the calculation in a cluster. Next, we test the computational efficiency of ss-DF compared to deterministic DF (d-DF) in water clusters containing 6 to 30 water molecules using a triple- basis set. Finally, we perform numerical structural optimizations on water and hydrogen fluoride clusters, revealing that DMET can recover weak interactions using a back-transformed energy formula. This work demonstrates the potential of using stochastic resolution of identity in quantum embedding theories and highlights its capability to recover weak interactions effectively
Chemical Networks from Scratch with Reaction Prediction and Kinetics-Guided Exploration
No full textAlgorithmic reaction explorations based on transition state searches can now routinely predict relatively short reaction sequences involving small molecules. However, applying these algorithms to deeper chemical reaction network (CRN) exploration still requires the development of more efficient and accurate exploration policies. Here, an exploration al- gorithm, which we name Yet Another Kinetic Strategy (YAKS), is demonstrated that uses microkinetic simulations of the nascent network to achieve cost-effective and deep network exploration. Key features of the algorithm are the automatic incorporation of bimolecular reactions between network intermediates, compatibility with short-lived but kinetically important species, and the incorporation of rate uncertainty into the exploration policy. In validation case studies of glucose pyrolysis, the algorithm rediscovers reaction pathways previously discovered by heuristic exploration policies and also elucidates new reaction pathways to experimentally obtained products. The resulting CRN is the first to connect all major experimental pyrolysis products to glucose. Additional case studies are presented that investigate the role of reaction rules, rate uncertainty, and bimolecular reactions. These case studies show that naive exponential growth estimates can vastly overestimate the actual number of kinetically relevant pathways in physical reaction networks. In light of this, further improvements in exploration policies and reaction prediction algorithms make it feasible that CRNs might soon be routinely predictable in many contexts
Electrochemical Heteroarylation and Amidation of Alkanes using Activated Glassy Carbon Electrodes without Mediators
No full textThe functionalization of challenging unactivated C(sp3)-H bonds was achieved electrocatalytically via hydrogen atom transfer and without mediators. This was possible through the sole activation of the surface of the Glassy Carbon Electrode in an electrochemical fashion using a phosphate buffer. This activation produced oxygenated functional groups on the surface, capable of abstracting these hydrogen atoms from C(sp3)-H of alkanes. Minisci and Ritter-type reactions were achieved using this procedure. Extensive characterization of the AGCE and preliminary mechanistic studies allow us to propose plausible reaction mechanisms. Furthermore, a regular battery can be used within this protocol to achieve the desired substituted alkanes under inexpensive and user-friendly conditions
Non-Linear Damage Response to Voltage Revealed by Operando X-ray Tomography in Polycrystalline NMC811
No full textTo understand the fracture behaviour in battery materials, X-ray computed tomography (X-ray CT) has been employed widely and has become the primary technique for non-destructive particle analysis. Cracking is known to cause decline in cell performance of polycrystalline NMC811 as it exposes new surfaces for parasitic electrolyte reactions and disconnects active material from the electrode matrix. First cycle crack formation has been documented previously; however, definitive electrochemically induced particle fracture can be difficult to assess due to complicated sample preparation methods and the availability of high-resolution X-ray imaging. Here we present an operando X-ray CT technique that enables accurate observation of fracture behaviour as the particles are de-/lithiated. We uncover a non-linear relationship between fracture behaviour and the cell voltage, and evidence of particle reformation as re-lithiation occurs, we also see the effect multiple cycles have on the severity of cracks. Using a grey level analysis algorithm for fracture detection, we have expedited the damage evaluation of thousands of particles throughout the electrochemical process to successfully understand crack initiation, propagation, and eventual partial particle restoration on a large statistical scale. In addition we explored the effects of continued volumetric hysteresis on particles’ damage fingerprints. For the first time, we demonstrate the complex plurality of fracture behaviour in commercial lithium ion battery materials, that will help to design mitigation strategies to combat the effects of particle fracture
A Flat-bottom Elastic Network Model for Generating Improved Plausible Reaction Paths
No full textRapid generation of a plausible reaction path connecting a given reactant and product in advance is crucial for the efficient computation of precise reaction paths or transition states. We propose a computationally efficient potential energy based on molecular structure to generate such paths. This potential energy has a flat bottom consisting of structures without atomic collisions while preserving non-reactive chemical bonds, bond angles, and partial planar structures. By combining this potential energy with the direct MaxFlux method, a recently developed reaction path/transition state search method, we can find the shortest plausible path passing within the bottom. Numerical results show that this combination yields lower-energy paths compared to the paths obtained by the well-known image-dependent pair potential. We also theoretically investigate the differences between these two potential energies. The proposed potential energy and path generation routine are implemented in our Python version of the direct MaxFlux method, available on GitHub