Crystallization of Semiconductor Nanorods into Perfectly Faceted Hexagonal Superstructures

Super crystallization of ligand‐capped nanocrystals into defined periodic solids from solution is the definitive demonstration of their self‐organizing properties. To date, this has been mainly limited to spherical nanocrystals where organization emulates atom or molecule packing in regular crystals with the most thermodynamically stable arrangement being eventually preferred. Here, the crystallization of wurtzite CdS nanorods into micrometer‐sized CdS superstructures with regular hexagonal symmetry is demonstrated by fine‐tuning the nanorod dispersibility over time. It is shown that the supercrystals have a long nucleation stage to form monolayer hexagons followed by a relatively faster growth stage both occurring rod by rod (in‐plane) and layer by layer (out of plane). The perfectly symmetrical hexagon shape of the final structure is mapped from the wurtzite crystal structure of each individual nanorod where they pack in side by side and end to end arrangements. These well‐defined superstructures are highly attractive for applications that collectively exploit electronic or optical properties that are synthetically tunable through the size and shape of each nanorod building block.     

Torsionally broken symmetry assists infrared excitation of biomimetic charge-coupled nuclear motions in the electronic ground state

The concerted interplay between reactive nuclear and electronic motions in molecules actuates chemistry. Here, we demonstrate that out-of-plane torsional deformation and vibrational excitation of stretching motions in the electronic ground state modulate the charge-density distribution in a donor-bridge-acceptor molecule in solution. The vibrationally-induced change, visualised by transient absorption spectroscopy with a mid-infrared pump and a visible probe, is mechanistically resolved by ab initio molecular dynamics simulations. Mapping the potential energy landscape attributes the observed charge-coupled coherent nuclear motions to the population of the initial segment of a double-bond isomerization channel, also seen in biological molecules. Our results illustrate the pivotal role of pre-twisted molecular geometries in enhancing the transfer of vibrational energy to specific molecular modes, prior to thermal redistribution. This motivates the search for synthetic strategies towards achieving potentially new infrared-mediated chemistry.

Channelling vibrational excitation energy to achieve ground-state charge-transfer (CT)-assisted isomerization in a donor-bridge-acceptor molecule in solution.     

Drevogenin A und Drevogenin B,zusätzlicher Strukturbeweis. Glykoside und Aglykone, 306. Mitteilung

Drevogenin B is shown to be 11-O-acetyl-drevogenin P. The earlier derived structure for drevogenin A (11-O-acetyl-drevogenin P) is further confirmed by conversion of 3-O-acetyl-drevogenin A.     

Structural study of borided layers obtained on synthetic Fe−Ni alloys

Carbon free Fe?Ni alloys (12 and 20 wt.% Ni) have been analysed by X-ray diffraction and surface Mössbauer spectroscopy (CXMS and CEMS) after boriding treatment at 1273 K for 20 hours. Some (Fe _x Ni _l-x )₂B and Fe _x Ni _l-x B samples, with different values ofx, obtained by heating at 1073 K mixtures of elements in powder form, were used as reference. Besides (Fe _x Ni _l-x )₂B and Fe _x Ni _l-x B, a third boride phase rich in boron has been detected in the outer borided layers of the alloy specimens. A third phase appears also in the corresponding X-ray patterns. A comparison between CXMS and CEMS data show that (Fe _x Ni _l-x )₂B is present only in the deeper part of the borided layers and that the outer surface layer is a mixture of a nickel and boron rich boride and of a superparamagnetic oxide.     

Compass: A shape-based machine learning tool for drug design

Summary Building predictive models for iterative drug design in the absence of a known target protein structure is an important challenge. We present a novel technique, Compass, that removes a major obstacle to accurate prediction by automatically selecting conformations and alignments of molecules without the benefit of a characterized active site. The technique combines explicit representation of molecular shape with neural network learning methods to produce highly predictive models, even across chemically distinct classes of molecules. We apply the method to predicting human perception of musk odor and show how the resulting models can provide graphical guidance for chemical modifications.     

Orthogonal arrays: An introduction and their application in optimizing underrelaxation factors in ‘simple’-based algorithm

The primary aim of this paper is to demonstrate how the ‘design-of-experiments’ techniques which are successful in physical experiments could also be adapted to a numerical simulation code. As an example this technique is applied to a general finite difference code used for predicting three-dimensional turbulent recirculating flows. Here the equations for velocities and continuity are solved using the algorithm called SIMPLE, which stands for semi-implicit method for pressure-linked equations. Physical modelling of turbulence is taken care of by means of kinetic energy and turbulence dissipation rate equations. The objective is to optimize the underrelaxation factors of primary and secondary flow variables so that the number of iterations required for convergence is minimum. This is done by the orthogonal array technique (a particular type of design-of-experiment technique). The geometry considered for this purpose is that of a simple gas turbine can combustor and the study is restricted to the isothermal non-reacting condition. Tests are carried out on three different grid configurations. In each case the underrelaxation factor for velocities contributed most to speed up the rate of convergence. Also, for each grid configuration the underrelaxation factor settings for minimum iterations for convergence was found to be same. Hence it is proposed that when doing grid independence tests for any similar flow situation, all the underrelaxation factors could be optimized on coarse grids.     

Scoring functions for protein-ligand docking

Virtual screening by molecular docking has become established as a method for drug lead discovery and optimization. All docking algorithms make use of a scoring function in combination with a method of search. Two theoretical aspects of scoring function performance dominate operational performance. The first is the degree to which a scoring function has a global extremum within the ligand pose landscape at the proper location. The second is the degree to which the magnitude of the function at the extremum is accurate. Presuming adequate search strategies, a scoring function's location performance will dominate behavior with respect to docking accuracy: the degree to which a predicted pose of a ligand matches experimental observation. A scoring function's magnitude performance will dominate behavior with respect to screening utility: enrichment of true ligands over non-ligands. Magnitude estimation also controls pure scoring accuracy: the degree to which bona fide ligands of a particular protein may be correctly ranked. Approaches to the development of scoring functions have varied widely, with a number of functions yielding similarly high levels of performance relating to the location issue. However, even among functions performing equally well on location, widely varying performance is observed on the question of magnitude. In many cases, performance is good enough to yield high enrichments of true ligands versus non-ligands in screening across a wide variety of protein types. Generally, performance is not good enough to correctly rank among true ligands. Strategies for improvement are discussed.