Bangpennington5426

Future society will need more energy storage than what the current technology can deliver and also need more efficient approaches to mitigate CO2 emission and its consequent climate change. Here we report a new concept of sodium-phenanthrenequinone (Na-PQ) battery that can capture CO2 to heighten its load voltage and specific energy upon discharge and reversibly release CO2 on recharge. A mechanistic study, combining spectroelectrochemistry and theoretical calculation, reveals that CO2 is involved in the discharge reaction by bonding to the carbonyl moieties (C═O) of the reduced PQ species (PQ2- in particular), which lowers the energy of the final discharge product PQ2-CO2(Na+)2 and therefore increases the formal potential of the redox couple PQ-Na+/PQ2-CO2(Na+)2. The CO2-assisted Na-PQ battery reported here exemplifies that electrochemical energy storage would have great potential to address one of the grand challenges (i.e., CO2 mitigation, utilization, and storage) facing human society in the 21st century and beyond.The iron-catalyzed hydroarylation of allenes was accomplished by weak phenone-assistance. The C-H activation proceeded with excellent efficacy and high ortho-regioselectivity in proximity to the weakly-coordinating carbonyl group for a range of substituted phenones and allenes. Detailed mechanistic studies, including the isolation of key intermediates, the structural characterization of an iron-metallacycle and kinetic analysis, allowed the sound elucidation of a plausible catalytic working mode. This mechanistic rationale is supported by detailed computational DFT studies, which fully address multi spin state reactivity. Furthermore, in operando NMR monitoring of the catalytic reaction provided detailed insights into the mode of action of the iron-catalyzed C-H alkylation with allenes.Coronaviruses may produce severe acute respiratory syndrome (SARS). As a matter of fact, a new SARS-type virus, SARS-CoV-2, is responsible for the global pandemic in 2020 with unprecedented sanitary and economic consequences for most countries. In the present contribution we study, by all-atom equilibrium and enhanced sampling molecular dynamics simulations, the interaction between the SARS Unique Domain and RNA guanine quadruplexes, a process involved in eluding the defensive response of the host thus favoring viral infection of human cells. Our results evidence two stable binding modes involving an interaction site spanning either the protein dimer interface or only one monomer. The free energy profile unequivocally points to the dimer mode as the thermodynamically favored one. The effect of these binding modes in stabilizing the protein dimer was also assessed, being related to its biological role in assisting the SARS viruses to bypass the host protective response. This work also constitutes a first step in the possible rational design of efficient therapeutic agents aiming at perturbing the interaction between SARS Unique Domain and guanine quadruplexes, hence enhancing the host defenses against the virus.High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. PP1 ic50 Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %Ecorr. None of the DFT-based diagnostics are nearly as predictive of %Ecorr as the best WFT-based diagnostics. To overcome the limitation of this cost-accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening.We propose a computationally lean, two-stage approach that reliably predicts self-assembly behavior of complex charged molecules on metallic surfaces under electrochemical conditions. Stage one uses ab initio simulations to provide reference data for the energies (evaluated for archetypical configurations) to fit the parameters of a conceptually much simpler and computationally less expensive force field of the molecules classical, spherical particles, representing the respective atomic entities; a flat and perfectly conducting wall represents the metallic surface. Stage two feeds the energies that emerge from this force field into highly efficient and reliable optimization techniques to identify via energy minimization the ordered ground-state configurations of the molecules. We demonstrate the power of our approach by successfully reproducing, on a semiquantitative level, the intricate supramolecular ordering observed experimentally for PQP+ and ClO4- molecules at an Au(111)-electrolyte interface, including the formation of open-porous, self-host-guest, and stratified bilayer phases as a function of the electric field at the solid-liquid interface. We also discuss the role of the perchlorate ions in the self-assembly process, whose positions could not be identified in the related experimental investigations.