Reversed de Wolff figure of merit and its application to powder indexing solutions

Hydrogen bonding patterns and C-H...π interactions in the structure of the antiparkinsonian drug (R)-rasagiline mesylate determined using laboratory and synchrotron X-ray powder diffraction data

The structure of (R)-rasagiline mesylate [(R)-RasH+·Mes-], an active pharmaceutical ingredient used to treat Parkinson's disease, is presented. The structure was determined from laboratory and synchrotron powder diffraction data, refined using the Rietveld method, and validated and optimized using dispersion-corrected DFT calculations. The unit-cell parameters obtained in both experiments are in good agreement and the refinement with both datasets converged to good agreement factors. The final parameters obtained from laboratory data were a = 5.4905 (8), b = 6.536 (2), c = 38.953 (3) Å, V = 1398.0 (4) Å3 and from synchrotron powder data were a = 5.487530 (10) Å, b = 6.528939 (12) Å, c = 38.94313 (9) Å, V = 1395.245 (5) Å3 with Z = 4 and space group P212121. Preferred orientation was properly accounted for using the synchrotron radiation data, leading to a March-Dollase parameter of 1.140 (1) instead of the 0.642 (1) value obtained from laboratory data. In the structure, (R)-RasH+ moieties form layers parallel to the ab plane connected by mesylate ions through N-H...O and C-H...O hydrogen bonds. These layers stack along the c axis and are further connected by C-H...π interactions. Hirshfeld surface analysis and fingerprint plot calculations indicate that the main interactions are: H...H (50.9%), H...C/C...H (27.1%) and H...O/O...H (21.1%).

Landscape pattern change simulations in Tibet based on the combination of the SSP-RCP scenarios

Monitoring landscape pattern change can provide spatial explicit basis for future landscape management. The future socioeconomic and climate change drivers should be systematically combined in landscape pattern monitoring, while they are often regarded as independent parameters in landscape monitoring models. This study sought to project the detailed landscape pattern change based on landscape composition and configuration in Tibet by 2030, and combined the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). The results showed area of the unused land and forest will reduce by a minimum standard of 11.42 × 10⁴ and 9.04 × 10⁴ km² from 2010 to 2030, respectively. Other land use types will increase, and the highest increase in grassland will be 9.30 × 10⁵ km². Combined SSP1 and RCP2.6 scenario show high landscape aggregation and low edge density on cultivated land, urban land and grassland in Tibet as a whole. However, in typical cultivated and urban landscape, the abovementioned rule is appeared in the combined SSP4 and RCP6.0 scenario. These findings stress the importance of systematically modeling the socioeconomic demand and climate change in landscape pattern monitoring, and using both landscape composition and configuration indexes for scenario evaluation.

Toward autonomous design and synthesis of novel inorganic materials

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.

Design and control of gas diffusion process in a nanoporous soft crystal

Design of the gas-diffusion process in a porous material is challenging because a contracted pore aperture is a prerequisite, whereas the channel traffic of guest molecules is regulated by the flexible and dynamic motions of nanochannels. Here, we present the rational design of a diffusion-regulatory system in a porous coordination polymer (PCP) in which flip-flop molecular motions within the framework structure provide kinetic gate functions that enable efficient gas separation and storage. The PCP shows substantial temperature-responsive adsorption in which the adsorbate molecules are differentiated by each gate-admission temperature, facilitating kinetics-based gas separations of oxygen/argon and ethylene/ethane with high selectivities of ~350 and ~75, respectively. Additionally, we demonstrate the long-lasting physical encapsulation of ethylene at ambient conditions, owing to strongly impeded diffusion in distinctive nanochannels.

Self-assembly of lattices with high structural complexity from a geometrically simple molecule

Here we report an anomalous porous molecular crystal built of C-H···N-bonded double-layered roof-floor components and wall components of a segregatively interdigitated architecture. This complicated porous structure consists of only one type of fully aromatic multijoint molecule carrying three identical dipyridylphenyl wedges. Despite its high symmetry, this molecule accomplishes difficult tasks by using two of its three wedges for roof-floor formation and using its other wedge for wall formation. Although a C-H···N bond is extremely labile, the porous crystal maintains its porosity until thermal breakdown of the C-H···N bonds at 202°C occurs, affording a nonporous polymorph. Though this nonporous crystal survives even at 325°C, it can retrieve the parent porosity under acetonitrile vapor. These findings show how one can translate simplicity into ultrahigh complexity.

New functions and graphical user interface attached to powder indexing software CONOGRAPH

The present status of powder indexing software CONOGRAPH as an integrated system with a graphical user interface is reported. The improvements to the peak search method and the other available functions are introduced. Computational results and times for CONOGRAPH are compared with those for ITO13, N-TREOR and DICVOL14 to clarify their differences.

Method to generate all the geometrical ambiguities of powder indexing solutions

A new algorithm is reported that returns all the lattices with the same set ofdspacings as that of a given lattice. The algorithm is useful for quickly checking lattice parameters obtained for a considered powder diffraction pattern and assessing the reliability of the results of powder indexing. The proposed algorithm was implemented as a new function in the powder indexing softwareConograph(http://sourceforge.jp/projects/conograph/; http://research.kek.jp/people/rtomi/ConographGUI/web_page.html). Examples of real cases are reported to explain how the algorithm can be used.

Robust powder auto-indexing using many peaks

A new powder auto-indexing method for theCONOGRAPHsoftware [Oishi-Tomiyasu (2013).Acta Cryst.A69, 603–610] can carry out exhaustive powder auto-indexing in a short time, even if theqvalues of many peaks are used, with sufficient consideration given to their observational errors. This article explains that the use of manyqvalues is essential to make powder auto-indexing robust against dominant zones and missing or false peaks in the input. Results fromCONOGRAPHfor 25 real diffraction patterns, including difficult cases, are presented. Owing to a sorting criterion for zones defined in the previous article, the computation times were reduced by a factor of between 18 and 250, and exhaustive powder auto-indexing was completed in 5 min at most.