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Porous crystals made to order Controlling and predicting the structural properties of porous molecular crystals would have important implications in gas adsorption, separation and catalysis applications, but remain an unmet goal. This paper introduces a new concept of modular assembly at the molecular level for the formation of porous crystalline solids. Different large chiral molecules with intrinsic nanosize pores, or porous modules, self-assemble through chiral recognition during co-crystallization to produce solid porous frameworks. The three-dimensional structure of the final material can be predicted theoretically. The paper explores four different, albeit analogous, porous modules, which form four different porous solids. Nanoporous molecular frameworks 1 , 2 , 3 , 4 , 5 , 6 , 7 are important in applications such as separation, storage and catalysis. Empirical rules exist for their assembly but it is still challenging to place and segregate functionality in three-dimensional porous solids in a predictable way. Indeed, recent studies of mixed crystalline frameworks suggest a preference for the statistical distribution of functionalities throughout the pores 7 rather than, for example, the functional group localization found in the reactive sites of enzymes 8 . This is a potential limitation for ‘one-pot’ chemical syntheses of porous frameworks from simple starting materials. An alternative strategy is to prepare porous solids from synthetically preorganized molecular pores 9 , 10 , 11 , 12 , 13 , 14 , 15 . In principle, functional organic pore modules could be covalently prefabricated and then assembled to produce materials with specific properties. However, this vision of mix-and-match assembly is far from being realized, not least because of the challenge in reliably predicting three-dimensional structures for molecular crystals, which lack the strong directional bonding found in networks. Here we show that highly porous crystalline solids can be produced by mixing different organic cage modules that self-assemble by means of chiral recognition. The structures of the resulting materials can be predicted computationally 16 , 17 , allowing in silico materials design strategies 18 . The constituent pore modules are synthesized in high yields on gram scales in a one-step reaction. Assembly of the porous co-crystals is as simple as combining the modules in solution and removing the solvent. In some cases, the chiral recognition between modules can be exploited to produce porous organic nanoparticles. We show that the method is valid for four different cage modules and can in principle be generalized in a computationally predictable manner based on a lock-and-key assembly between modules.

Predicting structure from the attributes of a material's building blocks remains a challenge and central goal for materials science. Isolating the role of building block shape for self-assembly provides insight into the ordering of molecules and the crystallization of colloids, nanoparticles, proteins, and viruses. We investigated 145 convex polyhedra whose assembly arises solely from their anisotropic shape. Our results demonstrate a remarkably high propensity for thermodynamic self-assembly and structural diversity. We show that from simple measures of particle shape and local order in the fluid, the assembly of a given shape into a liquid crystal, plastic crystal, or crystal can be predicted.

Cellulose samples are routinely analyzed by X-ray diffraction to determine their crystal type (polymorph) and crystallinity. However, the connection is seldom made between those efforts and the crystal structures of cellulose that have been proposed with synchrotron X-radiation and neutron diffraction over the past decade or so. In part, this desirable connection is thwarted by the use of different conventions for description of the unit cells of the crystal structures. In the present work, powder diffraction patterns from cellulose Iα, Iβ, II, IIII, and IIIII were calculated based on the published atomic coordinates and unit cell dimensions contained in modified “crystal information files” (.cif) that are supplied in the Supplementary Information. The calculations used peak widths at half maximum height of both 0.1 and 1.5° 2θ, providing both highly resolved indications of the contributions of each contributing reflection to the observable diffraction peaks as well as intensity profiles that more closely resemble those from practical cellulose samples. Miller indices are shown for each contributing peak that conform to the convention with c as the fiber axis, a right-handed relationship among the axes and the length of a < b. Adoption of this convention, already used for crystal structure determinations, is also urged for routine studies of polymorph and crystallinity. The calculated patterns are shown with and without preferred orientation along the fiber axis. Diffraction intensities, output by the Mercury program from the Cambridge Crystallographic Data Centre, have several uses including comparisons with experimental data. Calculated intensities from different polymorphs can be added in varying proportions using a spreadsheet program to simulate patterns such as those from partially mercerized cellulose or various composites.

HighlightsMultidimensional detection of intensity, wavelength, polarization, and angle of the incidence light significantly accelerates the development of optical information technology and artificial intelligence fields.The first comprehensive overview of the advancement of multifunctional photodetectors for perovskite semiconductors ranging from polarized light detection, spectral detection, and angle-sensing detection to self-powered detection is summarized.The existing problems and perspectives are discussed which can inspire more researchers to rationally design new perovskite materials and micro/nano-structure for high-performance multifunctional photodetectors.Multifunctional photodetectors boost the development of traditional optical communication technology and emerging artificial intelligence fields, such as robotics and autonomous driving. However, the current implementation of multifunctional detectors is based on the physical combination of optical lenses, gratings, and multiple photodetectors, the large size and its complex structure hinder the miniaturization, lightweight, and integration of devices. In contrast, perovskite materials have achieved remarkable progress in the field of multifunctional photodetectors due to their diverse crystal structures, simple morphology manipulation, and excellent optoelectronic properties. In this review, we first overview the crystal structures and morphology manipulation techniques of perovskite materials and then summarize the working mechanism and performance parameters of multifunctional photodetectors. Furthermore, the fabrication strategies of multifunctional perovskite photodetectors and their advancements are highlighted, including polarized light detection, spectral detection, angle-sensing detection, and self-powered detection. Finally, the existing problems of multifunctional detectors and the perspectives of their future development are presented.

High‐throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of sufficient and diverse materials in current materials repositories such as the open quantum materials database (OQMD). Recent progress in deep learning have enabled generative strategies that learn implicit chemical rules for creating hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generative design of novel cubic materials. When trained on 375 749 ternary materials from the OQMD database, the authors show that the model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such materials have been verified by phonon dispersion calculation. Considering the importance of cubic materials in wide applications such as solar panels, the GAN model provides a promising approach to significantly expand existing materials repositories, enabling the discovery of new functional materials via screening. The new crystal structures discovered are freely accessible at www.carolinamatdb.org. A deep neural network based generative model for high‐throughput generation of cubic materials is proposed. By creating 20 million virtual materials and applying machine learning and DFT based fast screenings, their model has not only rediscovered most of known cubic materials, but generated large number of new cubic structures with new prototypes: 506 new‐prototype stable materials have been verified computationally.

Schaftoside and isoschaftoside are bioactive natural products widely distributed in higher plants including cereal crops and medicinal herbs. Their biosynthesis may be related with plant defense. However, little is known on the glycosylation biosynthetic pathway of these flavonoid di-C-glycosides with different sugar residues. Herein, we report that the biosynthesis of (iso)schaftosides is sequentially catalyzed by two C-glycosyltransferases (CGTs), i.e., CGTa for C-glucosylation of the 2-hydroxyflavanone aglycone and CGTb for C-arabinosylation of the mono-C-glucoside. The two enzymes of the same plant exhibit high homology but remarkably different sugar acceptor and donor selectivities. A total of 14 CGTa and CGTb enzymes were cloned and characterized from seven dicot and monocot plants, including Scutellaria baicalensis, Glycyrrhiza uralensis, Oryza sativa ssp. japonica, and Zea mays, and the in vivo functions for three enzymes were verified by RNA interference and overexpression. Through transcriptome analysis,we found homologous genes in 119 other plants, indicating this pathway is general for the biosynthesis of (iso)schaftosides. Furthermore, we resolved the crystal structures of five CGTs and realized the functional switch of SbCGTb to SbCGTa by structural analysis and mutagenesis of key amino acids. The CGT enzymes discovered in this paper allow efficient synthesis of (iso)schaftosides, and the general glycosylation pathway presents a platform to study the chemical defense mechanisms of higher plants.

Bacteriorhodopsin (bR) is a light-driven proton pump and a model membrane transport protein. We used time-resolved serial femtosecond crystallography at an x-ray free electron laser to visualize conformational changes in bR from nanoseconds to milliseconds following photoactivation. An initially twisted retinal chromophore displaces a conserved tryptophan residue of transmembrane helix F on the cytoplasmic side of the protein while dislodging a key water molecule on the extracellular side. The resulting cascade of structural changes throughout the protein shows how motions are choreographed as bR transports protons uphill against a transmembrane concentration gradient.

The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.

The solution and crystal structures of a bacterial phytochrome photosensory core in both its resting and activated states are determined; switching between closed (resting) and open (activated) forms is found to be mediated by a conserved ‘tongue’, and the structures indicate that smaller changes in the vicinity of the chromophore are amplified in scale as they are transmitted through the tongue and beyond. Signal amplification by light-receptor proteins The crystal structure of the photosensory core of a bacterial phytochrome in both the resting and the active (illuminated) state has now been solved. Working with the phytochrome from the extremophile Deinococcus radiodurans , Sebastian Westenhoff and colleagues demonstrate that toggling between resting and active forms is mediated by a conserved 'tongue' that contacts the chromophore. Atomic-scale structural changes in the vicinity of the chromophore are amplified as they are transmitted through the tongue and beyond, culminating in a nanometre-scale conformational signal that feeds into the rest of the cellular signalling network. Sensory proteins must relay structural signals from the sensory site over large distances to regulatory output domains. Phytochromes are a major family of red-light-sensing kinases that control diverse cellular functions in plants, bacteria and fungi 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 . Bacterial phytochromes consist of a photosensory core and a carboxy-terminal regulatory domain 10 , 11 . Structures of photosensory cores are reported in the resting state 12 , 13 , 14 , 15 , 16 , 17 , 18 and conformational responses to light activation have been proposed in the vicinity of the chromophore 19 , 20 , 21 , 22 , 23 . However, the structure of the signalling state and the mechanism of downstream signal relay through the photosensory core remain elusive. Here we report crystal and solution structures of the resting and activated states of the photosensory core of the bacteriophytochrome from Deinococcus radiodurans . The structures show an open and closed form of the dimeric protein for the activated and resting states, respectively. This nanometre-scale rearrangement is controlled by refolding of an evolutionarily conserved ‘tongue’, which is in contact with the chromophore. The findings reveal an unusual mechanism in which atomic-scale conformational changes around the chromophore are first amplified into an ångstrom-scale distance change in the tongue, and further grow into a nanometre-scale conformational signal. The structural mechanism is a blueprint for understanding how phytochromes connect to the cellular signalling network.

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.