Notes

Spatial Distribution of your Porphyrin-Based Photosensitizer Reveals Device of Photodynamic Inactivation involving Vaginal yeast infections.
We develop a mesoscopic model to study the plastic behavior of an amorphous material under cyclic loading. The model is depinning-like and driven by a disordered thresholds dynamics that is coupled by long-range elastic interactions. We propose a simple protocol of "glass preparation" that allows us to mimic thermalization at high temperatures as well as aging at vanishing temperature. Various levels of glass stabilities (from brittle to ductile) can be achieved by tuning the aging duration. The aged glasses are then immersed into a quenched disorder landscape and serve as initial configurations for various protocols of mechanical loading by shearing. The dependence of the plastic behavior upon monotonous loading is recovered. find more The behavior under cyclic loading is studied for different ages and system sizes. The size and age dependence of the irreversibility transition is discussed. A thorough characterization of the disorder-landscape is achieved through the analysis of the transition graphs, which describe the plastic deformation pathways under athermal quasi-static shear. In particular, the analysis of the stability ranges of the strongly connected components of the transition graphs reveals the emergence of a phase-separation like process associated with the aging of the glass. Increasing the age and, hence, the stability of the initial glass results in a gradual break-up of the landscape of dynamically accessible stable states into three distinct regions one region centered around the initially prepared glass phase and two additional regions characterized by well-separated ranges of positive and negative plastic strains, each of which is accessible only from the initial glass phase by passing through the stress peak in the forward and backward, respectively, shearing directions.Maintaining stability of single-molecular junctions (SMJs) in the presence of current flow is a prerequisite for their potential device applications. However, theoretical understanding of nonequilibrium heat transport in current-carrying SMJs is a challenging problem due to the different kinds of nonlinear interactions involved, including electron-vibration and anharmonic vibrational coupling. Here, we overcome this challenge by accelerating Langevin-type current-induced molecular dynamics using machine-learning potential derived from density functional theory. We show that SMJs with graphene electrodes generate an order of magnitude less heating than those with gold electrodes. This is rooted in the better phonon spectral overlap of graphene with molecular vibrations, rendering harmonic phonon heat transport being dominant. In contrast, in a spectrally mismatched junction with gold electrodes, anharmonic coupling becomes important to transport heat away from the molecule to surrounding electrodes. Our work paves the way for studying current-induced heat transport and energy redistribution in realistic SMJs.Parahydrogen induced polarization (PHIP) provides a powerful tool to enhance inherently weak nuclear magnetic resonance signals, particularly in biologically relevant compounds. The initial source of PHIP is the non-equilibrium spin order of parahydrogen, i.e., dihydrogen, where the two protons make up a singlet spin state. Conversion of this spin order into net magnetization of magnetic heteronuclei, e.g., 13C, provides one of the most efficient ways to exploit PHIP. We propose a facile route to increase the performance of PHIP transfer in experiments with adiabatic sweeps of the ultralow magnetic field. To date, this technique yields the highest efficiency of PHIP transfer, yet, it has been mostly utilized with linear field sweeps, which does not consider the underlying spin dynamics, resulting in sub-optimal polarization. This issue was previously addressed by using the "constant" adiabaticity method, which, however, requires extensive calculations for large spin systems. In this work, the field sweep is optimized by utilizing the field dependence of the average 13C polarization. Both the experimental detection and the numerical simulation of this dependence are straightforward, even for complex multi-spin systems. This work provides a comprehensive survey of PHIP transfer dynamics at ultralow fields for two molecular systems that are relevant for PHIP, namely, maleic acid and allyl pyruvate. The proposed optimization allowed us to increase the resulting 13C polarization in 13C-allyl pyruvate from 6.8% with a linear profile to 8.7% with an "optimal" profile. Such facile optimization routines are valuable for adiabatic experiments in complex spin systems undergoing rapid relaxation or chemical exchange.Using infrared predissociation spectroscopy of cryogenic ions, we revisit the vibrational spectra of alkali metal ion (Li+, Na+, K+) di- and triglycine complexes. We assign their most stable conformation, which involves metal ion coordination to all C=O groups and an internal NH⋯NH2 hydrogen bond in the peptide backbone. An analysis of the spectral shifts of the OH and C=O stretching vibrations across the different metal ions and peptide chain lengths shows that these are largely caused by the electric field of the metal ion, which varies in strength as a function of the square of the distance. The metal ion-peptide interaction also remotely modulates the strength of internal hydrogen bonding in the peptide backbone via the weakening of the amide C=O bond, resulting in a decrease in internal hydrogen bond strength from Li+ > Na+ > K+.Machine learning techniques have received growing attention as an alternative strategy for developing general-purpose density functional approximations, augmenting the historically successful approach of human-designed functionals derived to obey mathematical constraints known for the exact exchange-correlation functional. More recently, efforts have been made to reconcile the two techniques, integrating machine learning and exact-constraint satisfaction. We continue this integrated approach, designing a deep neural network that exploits the exact constraint and appropriate norm philosophy to de-orbitalize the strongly constrained and appropriately normed (SCAN) functional. The deep neural network is trained to replicate the SCAN functional from only electron density and local derivative information, avoiding the use of the orbital-dependent kinetic energy density. The performance and transferability of the machine-learned functional are demonstrated for molecular and periodic systems.We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential formalism and is based on the smooth overlap of atomic positions descriptor with sparsification in terms of a reduced number of local environments using mini-batch k-means. The model is implemented in the Atomistic Global Optimization X framework and used as a partial replacement of the local relaxations in basin hopping structure search. The approach is shown to be robust for a wide range of atomistic systems, including molecules, nanoparticles, surface supported clusters, and surface thin films. The benefits in a structure search context of a local surrogate model are demonstrated. This includes the ability to benefit from transfer learning from smaller systems as well as the possibility to perform concurrent multi-stoichiometry searches.Time-dependent photodetachment action spectra for the linear hydrocarbon anions C4H- and C6H- are investigated using the cryogenic Double ElectroStatic Ion Ring ExpEriment. The radiative cooling characteristics of these ions on the millisecond to seconds timescale are characterized by monitoring changes in their spectra as the ions cool by spontaneous infrared (IR) emission. The average cooling rates, extracted using Non-negative Matrix Factorization, are fit with 1/e lifetimes of 19 ± 2 and 3.0 ± 0.2 s for C4H- and C6H-, respectively. The cooling rates are successfully reproduced using a simple harmonic cascade model of IR emission. The ultraslow radiative cooling dynamics determined in this work provide important data for understanding the thermal cooling properties of linear hydrocarbon anions and for refining models of the formation and destruction mechanisms of these anions in astrochemical environments.We present ab initio calculations of the collisional broadening of the R(0) pure rotational line in CO (at 115 GHz) perturbed by O2. Our calculations are done in a fully quantum way by solving close-coupling quantum-scattering equations without any approximations. We also report a new, highly accurate CO-O2 potential energy surface on which we did the quantum-scattering calculations. The calculated collisional broadening agrees with the available experimental data in a wide temperature range. The calculated collisional shift is negligible compared to the broadening, which is also consistent with the experimental data. We combine this result with our previous calculations for the same line in CO perturbed by N2 [Jóźwiak et al., J. Chem. Phys. 154, 054314 (2021)]; the obtained air-perturbed broadening of the R(0) pure rotational line in CO and its temperature dependence perfectly agree with the HITRAN database. This result constitutes an important step toward developing a methodology for providing accurate ab initio reference data on spectroscopic collisional line-shape parameters for molecular systems relevant to the Earth's atmosphere and for populating spectroscopic line-by-line databases.The "quasi-constant" smooth overlap of atomic position and atom-centered symmetry function fingerprint manifolds recently discovered by Parsaeifard and Goedecker [J. Chem. Phys. 156, 034302 (2022)] are closely related to the degenerate pairs of configurations, which are known shortcomings of all low-body-order atom-density correlation representations of molecular structures. Configurations that are rigorously singular-which we demonstrate can only occur in finite, discrete sets and not as a continuous manifold-determine the complete failure of machine-learning models built on this class of descriptors. The "quasi-constant" manifolds, on the other hand, exhibit low but non-zero sensitivity to atomic displacements. As a consequence, for any such manifold, it is possible to optimize model parameters and the training set to mitigate their impact on learning even though this is often impractical and it is preferable to use descriptors that avoid both exact singularities and the associated numerical instability.Acetylene and ammonia are important constituents of the interstellar medium, and their coupled chemistry induced by high-energy radiation may be responsible for the formation of a variety of prebiotically important organic-nitrogen compounds. In this work, we first comprehensively characterized the vibrational spectrum of the 11 C2H2⋯NH3 complex obtained by deposition of the C2H2/NH3/Ng (Ng = Ar, Kr, or Xe) gaseous mixtures at 5 K using Fourier transform infrared spectroscopy and ab initio calculations at the CCSD(T)/L2a_3 level of theory and examined its radiation-induced transformations. The parent complex adopts a C3v symmetric top molecular structure with C2H2 acting as a proton donor. The x-ray-induced transformations of this complex result in the formation of the C2H2⋯NH2 ∙ complex and various CN-containing species (CH2CNH, CH3NC, CH2NCH, CH2NC∙, CCN∙, and CNC∙). The radical-molecule complex was identified based on comparison of experimental data with the results of the UCCSD(T)/L2a_3 computations. It is characterized by distinct features in the region of acetylene CHasym str mode, red-shifted from the corresponding absorptions of non-complexed acetylene by -72.
Read More: https://www.selleckchem.com/products/talabostat.html