The crystallization problem is an outstanding challenge in the chemistry of porous covalent organic frameworks (COFs). Their structural characterization has been limited to modeling and solutions based on powder x-ray or electron diffraction data. Single crystals of COFs amenable to x-ray diffraction characterization have not been reported. Here, we developed a general procedure to grow large single crystals of three-dimensional imine-based COFs (COF-300, hydrated form of COF-300, COF-303, LZU-79, and LZU-111). The high quality of the crystals allowed collection of single-crystal x-ray diffraction data of up to 0.83-angstrom resolution, leading to unambiguous solution and precise anisotropic refinement. Characteristics such as degree of interpenetration, arrangement of water guests, the reversed imine connectivity, linker disorder, and uncommon topology were deciphered with atomic precision-aspects impossible to determine without single crystals.

We provided a systematical investigation to correlate the crystal structure, composition, mobility, and ion migration behavior to the ratio of MA+/FA+ in FA(1−x)MAxPbI3 single crystal.

Supplemental Digital Content is available in the text.

The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence across multiple detectors is non-negligible. These ''glitches'' can easily be mistaken for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational waves. We apply machine-learning algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Noise sources may produce artifacts in these auxiliary channels as well as the gravitational-wave channel. The number of auxiliarychannel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well suited. We demonstrate the feasibility and applicability of three different MLAs: artificial neural networks, support vector machines, and random forests. These classifiers identify and remove a substantial fraction of the glitches present in two different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth-science-run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar performance to the benchmark algorithm, the ordered veto list, which is optimized to detect pairwise correlations between transients in LIGO auxiliary channels and glitches in the gravitational-wave data. This suggests that most of the useful information currently extracted from the auxiliary channels is already described by this model. Future performance gains are thus likely to involve additional sources of information, rather than improvements in the classification algorithms themselves. We discuss several plausible sources of such new information as well as the ways of propagating it through the classifiers into gravitational-wave searches.

Crystal size effect is of vital importance in materials science by exerting significant influence on various properties of materials and furthermore their functions. Crystal size effect of covalent organic frameworks (COFs) has never been reported because their controllable synthesis is difficult, despite their promising properties have been exhibited in many aspects. Here, we report the diverse crystal size effects of two representative COFs based on the successful realization of crystal-size-controlled synthesis. For LZU-111 with rigid spiral channels, size effect reflects in pore surface area by influencing the pore integrity, while for flexible COF-300 with straight channels, crystal size controls structural flexibility by altering the number of repeating units, which eventually changes sorption selectivity. With the understanding and insight of the structure-property correlation not only at microscale but also at mesoscale for COFs, this research will push the COF field step forward to a significant advancement in practical applications.

Perovskite single crystal (PSC) possesses fewer bulk defects than the polycrystalline film counterpart, which has received extensive attention in a number of optoelectronic device applications. But the management of carrier behavior in an efficient solar cell based on PSC is not reported yet. Here, the carrier behavior within the device based on the ITO/NiOx/(FAPbI3)0.85(MAPbBr3)0.15/TiO2/Ag structure (FA = CH(NH2)2+, MA = CH3NH3+), in which the mixed PSC is successfully implemented into solar cells for the first time, is investigated. The PSC films with lateral dimension from 500 µm to 2 mm and thickness over tens of micrometers, are obtained through the polydimethylsiloxane‐assisted solvent evaporation method. The highest power conversion efficiency derived from the present device approaches 12.18%. It is revealed that the surface roughness and thickness of PSC, the operation temperature and thickness of transport layers, and the interfaces of transport layers/PSC largely impact the carrier extraction and device performance of the perovskite single‐crystal solar cell (PSCSC). This work indicates that the carrier behavior in the absorber layer and transport layer has large impact on the performance of PSCSCs, while the underlying design rule represents an important step to advance the PSCSCs and other optoelectronic devices.

Chronic obstructive pulmonary disease (COPD) has seriously impacted the health of individuals and populations. In this study, proton nuclear magnetic resonance (1H NMR)-based metabonomics combined with multivariate pattern recognition analysis was applied to investigate the metabolic signatures of patients with COPD. Serum and urine samples were collected from COPD patients (n = 32) and healthy controls (n = 21), respectively. Samples were analyzed by high resolution 1H NMR (600 MHz), and the obtained spectral profiles were then subjected to multivariate data analysis. Consistent metabolic differences have been found in serum as well as in urine samples from COPD patients and healthy controls. Compared to healthy controls, COPD patients displayed decreased lipoprotein and amino acids, including branched-chain amino acids (BCAAs), and increased glycerolphosphocholine in serum. Moreover, metabolic differences in urine were more significant than in serum. Decreased urinary 1-methylnicotinamide, creatinine and lactate have been discovered in COPD patients in comparison with healthy controls. Conversely, acetate, ketone bodies, carnosine, m-hydroxyphenylacetate, phenylacetyglycine, pyruvate and α-ketoglutarate exhibited enhanced expression levels in COPD patients relative to healthy subjects. Our results illustrate the potential application of NMR-based metabonomics in early diagnosis and understanding the mechanisms of COPD.

Recent researches on long noncoding RNAs (lncRNAs) have expanded our horizon of gene regulation and the cellular complexity. However, the number, characteristics and expression patterns of lncRNAs remain poorly characterized and how these lncRNAs biogenesis are regulated in response to drought stress in cotton are still largely unclear. In the study, using a reproducibility-based RNA-sequencing and bioinformatics strategy to analyze the lncRNAs of 9 samples under three different environment stresses (control, drought stress and re-watering, three replications), we totally identified 10,820 lncRNAs of high-confidence through five strict steps filtration, of which 9,989 were lincRNAs, 153 were inronic lncRNAs, 678 were anti-sense lncRNAs. Coding function analysis showed 6,470 lncRNAs may have the ability to code proteins. Small RNAs precursor analysis revealed that 196 lncRNAs may be the precursors to small RNAs, most of which (35.7%, 70) were miRNAs. Expression patterns analysis showed that most of lncRNAs were expressed at a low level and most inronic lncRNAs (75.95%) had a consistent expression pattern with their adjacent protein-coding genes. Further analysis of transcriptome data uncovered that lncRNAs XLOC_063105 and XLOC_115463 probably function in regulating two adjacent coding genes CotAD_37096 and CotAD_12502, respectively. Investigations of the content of plant hormones and proteomics analysis under drought stress also complemented the prediction. We analyzed the characteristics and the expression patterns of lncRNAs under drought stress and re-watering treatment, and found lncRNAs may be likely to involve in regulating plant hormones pathway in response to drought stress.