Search

In this paper, theories on anisotropic crystal growth and crystallization of atropisomers are reviewed and a model for anisotropic crystal growth from solution containing slow inter-converting conformers is presented. The model applies to systems with growth-dominated crystallization from solutions and assumes that only one conformation participates in the solute integration step and is present in the crystal lattice. Other conformers, defined as the wrong conformers, must convert to the right conformer before they can assemble to the crystal lattice. The model presents a simple implicit method for evaluating the growth inhibition effect by the wrong conformers. The crystal growth model applies to anisotropic growth in two main directions, namely a slow-growing face and a fast-growing face and requires the knowledge of solute crystal face integration coefficients in both directions. A parameter estimation algorithm was derived to extract those coefficients from data about temporal concentration and crystal size during crystallization and was designed to have a short run time, while providing a high-resolution estimation. The model predicts a size-dependent growth rate and simulations indicated that for a given seed size and solvent system and for an isothermal anti-solvent addition crystallization, the seed loading and the supersaturation at seeding are the main factors impacting the final aspect ratio. The model predicts a decrease of the growth inhibition effect by the wrong conformer with increasing temperature, likely due to faster equilibration between conformers and/or a decrease of the population of the wrong conformer, if of low energy, at elevated temperatures. Finally, the model predicts that solute surface integration becomes the rate-limiting mechanism for high solute integration activation energies, resulting in no impact of the WC on the overall crystal growth process. [ABSTRACT FROM AUTHOR]

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol -1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases., (open access.)

A novel approach to computationally enhance the sampling of molecular crystal structures is proposed and tested. This method is based on the use of extended variables coupled to a Monte Carlo based crystal polymorph generator. Inspired by the established technique of quasi-random sampling of polymorphs using the rigid molecule constraint, this approach represents molecular clusters as extended variables within a thermal reservoir. Polymorph unit-cell variables are generated using pseudo-random sampling. Within this framework, a harmonic coupling between the extended variables and polymorph configurations is established. The extended variables remain fixed during the inner loop dedicated to polymorph sampling, enforcing a stepwise propagation of the extended variables to maintain system exploration. The final processing step results in a polymorph energy landscape, where the raw structures sampled to create the extended variable trajectory are re-optimized without the thermal coupling term. The foundational principles of this approach are described and its effectiveness using both a Metropolis Monte Carlo type algorithm and modifications that incorporate replica exchange is demonstrated. A comparison is provided with pseudo-random sampling of polymorphs for the molecule coumarin. The choice to test a design of this algorithm as relevant for enhanced sampling of crystal structures was due to the obvious relation between molecular structure variables and corresponding crystal polymorphs as representative of the inherent vapor to crystal transitions that exist in nature. Additionally, it is shown that the trajectories of extended variables can be harnessed to extract fluctuation properties that can lead to valuable insights. A novel thermodynamic variable is introduced: the free energy difference between ensembles of Z' = 1 and Z' = 2 crystal polymorphs., (open access.)

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures., (open access.)

Introduction: The objective of this study was to evaluate retinal sensitivity in subfields and its association with the novel quantitative contrast sensitivity function (qCSF) in patients with early age-related macular degeneration (eAMD), in patients with intermediate AMD (iAMD), and in healthy controls., Methods: In this prospective longitudinal study, retinal sensitivity of a customized 24-point grid was assessed by microperimetry Macular Integrity Assessment (MAIA, CenterVue, Padova, Italy) and divided into different subfields. The Multiple Contrast Vision Meter (Adaptive Sensory Technology, San Diego, CA, USA) was used for qCSF testing. Linear models were used to test the association of functional metrics with variables of interest., Results: 92 study eyes from 92 participants were analyzed (13 eAMD, 31 iAMD, and 48 controls). Microperimetry subfield comparison showed significant differences (p < 0.0001) in the control group between superior and inferior hemifield as well as between central and peripheral subfields. For eAMD, significant differences were found between central and peripheral subfields (p < 0.001) and specific subfields (p < 0.05) and finally for iAMD between specific quadrants (p < 0.05) and specific squares (p < 0.05). Significant associations of retinal sensitivity with qCSF metrics were found for the area underneath the logarithmic contrast sensitivity function, contrast acuity and for the contrast sensitivity at specific spatial frequencies., Conclusions: This study showed significant differences in the evaluated retinal sensitivity subfields, providing localized natural history data for retinal sensitivity in healthy controls and patients with eAMD and iAMD., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

A low-temperature structural phase transition has been observed for form (II) of benzocaine (BZC). Lowering the temperature doubles the b-axis repeat and changes the space group from P2(1)2(1)2(1) to P112(1) with gamma now 99.37 degrees. The structure is twinned, the twin rule corresponding to a 2(1) screw rotation parallel to a. The phase transition is associated with a sequential displacement parallel to a of zigzag bi-layers of ribbons perpendicular to b*. No similar phase transition was observed for form (I) and this was attributed to the different packing symmetries of the two room-temperature polymorphic forms.

The title compound, [La(C(6)H(2)N(3)O(7))(3)(C(2)H(6)O)(3)]·C(15)H(12)N(4), has two mol-ecular building blocks, namely the neutral mononuclear adduct of lanthanum picrate with ethanol [i.e. La(pic)(3):EtOH (1:3); La(pic)(3) = lanthanum picrate and EtOH = ethanol] and the oligodentate aromatic nitro-gen base tri-2-pyridylamine (tpa). The asymmetric unit contains two formula units. The compound was prepared during an investigation of the stereochemistry of lanthanoid picrate complexes with O-donor ligands. The metal-ligand adduct adopts a nine-coordinate tricapped trigonal-prismatic metal atom environment. The stereochemical arrangement of the ligands about the metal core is typical of a fac-isomer with stoichiometry M(bidentate)(3)(monodentate)(3). Face-to-face hydrogen bonds are found between the tpa mol-ecule and the ethanol ligands. One ethanol ligand is disordered over two positions, with site occupancy factors of ca 0.7 and 0.3. The oxygen atoms of a nitro group are also disordered over two positions; the site occupancy factors are ca 0.6 and 0.4.