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The redox property of metal nanoclusters plays a pivotal role and is of particular interest in catalysis and other applications, such as aerobic oxidation, hydrogenation, and singlet oxygen generation, over intact nanoclusters. In this study, we report a one-way conversion process of the anionic [Ag16Au13L24]3- nanocluster into a charge neutral nanocluster of [Ag16Au13L24]0 via oxidation in a solution phase using H2O2 as the oxidant. Three-electron loss of [Ag16Au13L24]3- occurred during the oxidation process, which was confirmed by electron paramagnetic resonance and electrospray ionization mass spectrometry methods. check details The one-way conversion from [Ag16Au13L24]3- to [Ag16Au13L24]0 nanoclusters is in situ monitored by UV-visible spectroscopy. A nanocluster charge effect is manifested in the UV-visible spectra of nanoclusters; an ∼10 nm redshift is observed compared with the optical absorption spectrum of [Ag16Au13L24]3-.As the first potassium channel with an x-ray structure determined, and given its homology to eukaryotic channels, the pH-gated prokaryotic channel KcsA has been extensively studied. Nevertheless, questions related, in particular, to the allosteric coupling between its gates remain open. The many currently available x-ray crystallography structures appear to correspond to various stages of activation and inactivation, offering insights into the molecular basis of these mechanisms. Since these studies have required mutations, complexation with antibodies, and substitution of detergents in place of lipids, examining the channel under more native conditions is desirable. Solid-state nuclear magnetic resonance (SSNMR) can be used to study the wild-type protein under activating conditions (low pH), at room temperature, and in bacteriomimetic liposomes. In this work, we sought to structurally assign the activated state present in SSNMR experiments. We used a combination of molecular dynamics (MD) simulations, chemical shift prediction algorithms, and Bayesian inference techniques to determine which of the most plausible x-ray structures resolved to date best represents the activated state captured in SSNMR. We first identified specific nuclei with simulated NMR chemical shifts that differed significantly when comparing partially open vs fully open ensembles from MD simulations. The simulated NMR chemical shifts for those specific nuclei were then compared to experimental ones, revealing that the simulation of the partially open state was in good agreement with the SSNMR data. Nuclei that discriminate effectively between partially and fully open states belong to residues spread over the sequence and provide a molecular level description of the conformational change.Fluorescence correlation spectroscopy was used to show that the temperature-dependent diffusion coefficient of poly(ethylene oxide) (PEO) adsorbed on polystyrene and different poly(alkyl methacrylate) (PAMA) films in aqueous solution exhibited a maximum close to (but below) the surface glass transition temperature, Tgs, of the film. This elevated diffusion was observed over a small range of temperatures below Tgs for these surfaces, and at other temperatures, the diffusion was similar to that on silicon, although the diffusion coefficient for PEO on polystyrene at temperatures above Tgs did not completely decrease to that on silicon, in contrast to the PAMA surfaces. It is concluded that the enhanced surface mobility of the films near the surface glass transition temperature induces conformational changes in the adsorbed PEO. The origin of this narrow and dramatic increase in diffusion coefficient is not clear, but it is proposed that it is caused by a coupling of a dominant capillary mode in the liquid surface layer with the polymer. Friction force microscopy experiments also demonstrate an unexpected increase in friction at the same temperature as the increase in diffusion coefficient.This work proposes two deep eutectic solvents (DESs) based on lithium bis(fluorosulfonyl)imide and sodium bis(fluorosulfonyl)imide together with N-methylacetamide and formamide as electrolytes for activated carbon (AC) electrochemical double-layer capacitors (EDLCs) at 25 °C. The formulated DESs exhibit a large electrochemical window (ΔE > 2.5 V), good thermal stability (∼150 °C) and ionic conductivity (3-4 mS cm-1), and moderate viscosity (11.3 mPa s). Through the Vogel-Tamman-Vulcher fitting equation, the evolution of pseudo-energy activation was delineated with respect to the nature of the H-bond donor or alkali salt. These electrolytes present a superionic character gleaned from the Walden classification, and their ionicity exceeds that of standard organic electrolytes based on similar alkali salts. The performance of the AC-based EDLC was assessed by cyclic voltammetry, electrochemical impedance spectroscopy, and galvanostatic charge/discharge, yielding 140 F g-1 with an 8% capacity retention during 200 h of floating. Based on the physicochemical properties and electrochemical performance of these DESs, they represent a promising green-alternative electrolyte for supercapacitor applications.The description of frequency fluctuations for highly coupled vibrational transitions has been a challenging problem in physical chemistry. In particular, the complexity of their vibrational Hamiltonian does not allow us to directly derive the time evolution of vibrational frequencies for these systems. In this paper, we present a new approach to this problem by exploiting the artificial neural network to describe the vibrational frequencies without relying on the deconstruction of the vibrational Hamiltonian. To this end, we first explored the use of the methodology to predict the frequency fluctuations of the amide I mode of N-methylacetamide in water. The results show good performance compared with the previous experimental and theoretical results. In the second part, the neural network approach is used to investigate the frequency fluctuations of the highly coupled carbonyl stretch modes for the organic carbonates in the solvation shell of the lithium ion. In this case, the frequency fluctuation predicted by the neural networks shows a good agreement with the experimental results, which suggests that this model can be used to describe the dynamics of the frequency in highly coupled transitions.
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