Crystal structure of 5-benzoylamino-5-isopropyl-4-oxo-1,3-dioxane

C₁₄H₁₇NO₄ crystallizes in the monoclinic space groupP2₁/n (Z=4) witha=10.208(2),b=10.888(2),c=11.909(2) Å, and=90.89(2)°. The structure was solved by direct methods and refined by full-matrix LSQ to anR factor of 0.036. The 4-oxo-1,3-dioxane ring is in a slightly distorted O(1)-sofa conformation flattened at the C(4) end with the 5-isopropyl substituent in a pseudoaxial position and planar lactone group. The molecules form dimers by means of intermolecular N-HO(4) hydrogen bonds of 3.091(2) Å. The¹H NMR spectrum of the title compound shows unusual features.     

On the gas-phase reaction of C3O2+. with C3O2

The gas-phase, ion molecule reaction between C₃O₂^+. and C₃O₂ has been studied by both double-focusing and ion trap mass spectrometry, rationalized by the formation of a dimeric, odd electron cation [C₆O₄]^+. which decomposes extensively through sequential CO losses giving rise to [C₅O₃]^+. and [C₄O₂]^+. ions. The thermodynamics of this process have been investigated by means of ab initio calculation performed on the above species using different basis sets (STO-3G, 3-21G and 6-31G^*).     

Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization

Purpose: In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science. Methods: To improve the convergence efficiency of the BCM model, we combine the original plasticity rule with the optimization tools of modern deep learning. By numerical simulation on standard benchmark datasets, we prove the efficiency of the BCM model in learning, memorization capacity, and feature extraction. Results: In all the numerical simulations, the visualization of neuronal synaptic weights confirms the memorization of human-interpretable subsets of patterns. We numerically prove that the selectivity obtained by BCM neurons is indicative of an internal feature extraction procedure, useful for patterns clustering and classification. The introduction of competitiveness between neurons in the same BCM network allows the network to modulate the memorization capacity of the model and the consequent model selectivity. Conclusions: The proposed improvements make the BCM model a suitable alternative to standard machine learning techniques for both feature selection and classification tasks.     

Covalent versus electrostatic nature of the strong hydrogen bond: discrimination among single, double, and asymmetric single-well hydrogen bonds by variable-temperature X-ray crystallographic methods in beta-diketone enol RAHB systems

Beta-diketone enols are known to form intramolecular...O=C-C=C-OH... resonance-assisted hydrogen bonds (RAHBs) with O...O distances as short as 2.39-2.44 A. However, even the most accurate diffraction studies have not been able to assess with certainty whether these very strong hydrogen bonds (H-bonds) are to be described as proton-centered O...H...O bonds in a single-well (SW) potential or as the dynamic or static mixing of two O-H...O <= => O...H-O tautomers in a double-well (DW) one. This contribution reexamines the problem and shows that diffraction methods are fairly able to assess the SW or DW nature of the H-bond formed and, in the second case, its dynamic or static nature, provided a Bayesian approach is used which associates a number of experimental techniques (X-ray crystallography at variable temperature, difference Fourier maps, least-squares refinement of proton populations, Hirshfeld's rigid-bond test) with a reasonable prior, that is the full set of possible proton-transfer (PT) pathways for the O-H...O system derived from theoretical calculations. The method is first applied to three beta-diketone enols, whose crystal structures were determined in the interval of temperatures 100-295 K and then generalized to the interpretation of a much wider set of beta-diketone enol structures derived from the literature, making it possible to establish a general relationship between chemical structure (symmetric or dissymmetric substitution, steric compression or stretching, increased pi-bond delocalizability), H-bond strength, and the shape of the PT-barrier. Final results are interpreted in terms of simplified VB theory and state-correlation (or avoided-crossing) diagrams.     

Crystal structure of racemic 5-(p-nitrobenzoylamino)-5-methyl-4-oxo-1,3-dioxane

C₁₂H₁₂N₂O₆ crystallized from methanol in the monoclinic space group P2₁/c (Z=4) witha=13.333(4),b=10.154(3),c=9.597(2) Å and=102.37(2)°.M _r=280.24,V=1269.1(6) ų,D _x=1.466 g cm^–3,(Mo K)=0.71069 Å,=1.29 cm^–1,T=295 K. The structure was solved by direct methods and refined by full-matrix least-squares to a finalR (R _w)=0.049 (0.057) for 916 observed reflections. The molecules are linked by strong N-HO(4) hydrogen bonds with NO distance of 2.995(4) Å. The infinite racemic chains run in [001] direction.     

Characterization and modulation of the hierarchical self-assembly of nanostructured DNA tiles into supramolecular polymers

We present a set of DNA supramolecular architectures based on the polymerization of discrete DNA tiles having the shape of parallelograms and designed to have a one-dimensional inter-tile connectivity. Tiles bind to each other with two connections, which have different thermal stabilities. We discuss how this difference in stability implies that the same monomeric tile can yield supramolecular polymers of different shapes just by changing the polymerization conditions. We show how this system reacts to external stimuli by interconverting between some of its possible states. Concurrently, we show how performing the polymerization on a surface can influence its outcome.