Polar Molecules Regulating the Regio- and Stereoselectivity of Polymerization of Conjugated Dienes Catalyzed by CGC-Type Rare-Earth Metal Catalysts.

Herein, a concise, effective, and scalable strategy is reported that the introduction of polar molecules (PMs) (e.g., anisole (PhOMe), phenetole (PhOEt), 2-methoxynaphthalene (NaphOMe), thioanisole (PhSMe), and N,N-dimethylaniline (PhNMe2)) as continuously coordinated neutral ligand of cationic active species in situ generated from the constrain-geometry-configuration-type rare-earth metal complexes A-F/AliBu3/[Ph3C][B(C6F5)4] ternary systems can easily switch the regio- and stereoselectivity of the polymerization of conjugated dienes (CDs, including 2-subsituted CDs such as isoprene (IP) and myrcene (MY), 1,2-disubstituted CD ocimene (OC), and 1-substituted polar CD 1-(para-methoxyphenyl)-1,3-butadiene (p-MOPB)) from poor selectivities to high selectivities (for IP and MY: 3,4-selectivity up to 99%; for OC: trans-1,2-selectivity up to 93% (mm up to 90%); for p-MOPB: 3,4-syndioselectivity (3,4- up to 99%, rrrr up to 96%)). DFT calculations explain the continuous coordination roles of PMs on the regulation of the regio- and stereoselectivity of the polymerization of CDs. In comparison with the traditional strategies, this strategy by adding some common PMs is easier and more convenient, decreasing the synthetic cost and complex operation of new metal catalyst and cocatalyst. Such regio- and stereoselective regulation method by using PMs is not reported for the coordination polymerization of olefins catalyzed by rare-earth metal and early transition metal complexes.

Synergistic Effect of Small Molecular Organic Matter and Functional Groups in Coal on Methane Adsorption.

Functional groups and small-molecule organic matter are two key parts of coal. To explore the microscopic mechanism underlying the synergistic effect of both parts on methane adsorption, the oxygen-containing (-OH, -COOH, and -C=O) and nitrogen-containing (-NH2) functional groups and two common small molecular organic matter methylbenzene and tetrahydrofuran-2-alcohol in coal are selected. The quantum chemical meta-GGA functional method is used to optimize all structures. The electrostatic potential analyses, weak interaction analyses, and theory of atoms in molecules have been used to delve further into the nature of this synergistic effect. Our results show that functional groups inhibit methane adsorption by coal molecules, and the inhibition effect is enhanced in the presence of methylbenzene. Interestingly, the synergistic effects between functional groups and small molecular organic matter are changed from inhibition to promotion after introducing tetrahydrofuran-2-alcohol, wherein -COOH has the best synergistic effect. This work not only offers a theoretical foundation for exploring the synergistic effect of small molecular organic matter and functional groups on methane adsorption by coal molecules but also lays a foundation for further research on gas prevention and extraction.

Efficient degradation of tetrabromobisphenol A using peroxymonosulfate oxidation activated by a novel nano-CuFe2O4@coconut shell biochar catalyst.

In this study, a novel bimetallic complexation-curing nucleation-anaerobic calcination method was developed to synthesize a nano-CuFe2O4@coconut shell biochar (CuFe2O4@CSBC) catalyst to activate peroxymonosulfate for degradation of tetrabromobisphenol A (TBBPA). The reaction processes of the TBBPA on CuFe2O4@CSBC have been investigated using in situ characterization and metal leaching. The effects of initial reaction conditions and degradation mechanism were investigated. Greater than 99% degradation of TBBPA at 10 mg L-1 was achieved in 30 min under the condition of pH 11, a total organic carbon removal rate of up to 70.67% was achieved and the degradation efficiency was 90% after 5 cycles of CuFe2O4@CSBC use. The degradation was in a second-order reaction at a constant of 0.797 M-1 min-1 (R2 = 0.993). The degradation was attributed to the main active species (SO4·-≈·OH < 1O2), and the surface active site of CuFe2O4@CSBC was the key role. The degradation process involved three main degradation pathways. Path A: ·OH attacked the C-Br bonds (TBBPA→TriBBPA→DBBPA→MBBPA→BPA); Path B: Hydroxylation and decarboxylation; Path C: Dehydrocoupling of TBBPA. What's more, the practical application of the system was very positive, achieved >77% degradation in sewage and industrial wastewater.

Twisting, untwisting, and retwisting of elastic Co-based nanohelices.

The reversible transformation of a nanohelix is one of the most exquisite and important phenomena in nature. However, nanomaterials usually fail to twist into helical crystals. Considering the irreversibility of the previously studied twisting forces, the reverse process (untwisting) is more difficult to achieve, let alone the retwisting of the untwisted crystalline nanohelices. Herein, we report a new reciprocal effect between molecular geometry and crystal structure which triggers a twisting-untwisting-retwisting cycle for tri-cobalt salicylate hydroxide hexahydrate. The twisting force stems from competition between the condensation reaction and stacking process, different from the previously reported twisting mechanisms. The resulting distinct nanohelices give rise to unusual structure elasticity, as reflected in the reversible change of crystal lattice parameters and the mutual transformation between the nanowires and nanohelices. This study proposes a fresh concept for designing reversible processes and brings a new perspective in crystallography.

Triggering and recovery of earthquake accelerated landslides in Central Italy revealed by satellite radar observations.

Earthquake triggered landslides often pose a great threat to human life and property. Emerging research has been devoted to documenting coseismic landslides failed during or shortly after earthquakes, however, the long-term seismic effect that causes unstable landslides only to accelerate, moderately or acutely, without immediate failures is largely neglected. Here we show the activation and recovery of these earthquake accelerated landslides (EALs) in Central Italy, based on satellite radar observations. Unlike previous studies based on single or discrete landslides, we established a large inventory of 819 EALs and statistically quantified their spatial clustering features against a set of conditioning factors, thus finding that EALs did not rely on strong seismic shaking or hanging wall effects to occur and larger landslides were more likely to accelerate after earthquakes than smaller ones. We also discovered their accelerating-to-recovering sliding dynamics, and how they differed from the collapsed 759 coseismic landslides. These findings contribute to a more comprehensive understanding of the earthquake-triggering landslide mechanism and are of great significance for long-term landslide risk assessment in seismically active areas.

Kinetics of zero-valent iron-activated persulfate for methylparaben degradation and the promotion of Cl.

Methylparaben (MP) is an emerging pollutant, and the optimal conditions and kinetics of MP degradation using nano-zero-valent iron-activated persulfate (nZVI/PDS) need to be further investigated. This paper firstly investigated the response surface methodology (RSM) analysis of MP degradation by the heterogeneous system nZVI/PDS and concluded that the initial pH had the most significant effect on MP degradation. The optimal experimental conditions predicted by the RSM were as follows: initial pH 2.75, [nZVI]0 = 2.87 mM, [PDS]0 = 2.18 mM (MP degradation level of 95.30%). First- and second-order kinetic fits were performed for different initial pH levels and different concentrations of MP, nZVI, and PDS. It was determined that k = 0.0365 min-1 (R2 = 0.984) when the initial pH was 3, [PDS]0 = 2 mM, [MP]0 = 20 mg L-1, and [nZVI]0 = 3 mM (MP degradation level of 94.25%). The rest of the conditions were more closely fitted to the second-order reactions. The effects of different concentrations of anions and humic acid (HA) on the MP degradation level and k were examined, and it was found that Cl- could promote MP degradation to 97.69% (increased by 3.65%) and increase the k in accordance with the first-order reaction kinetics (0.0780 min-1, R2 = 0.991). Finally, the analysis of intermediates revealed 5 reaction pathways and 7 reaction intermediates, which inferred a possible reaction mechanism with the recycling performance of nZVI. In this paper, the superiority of nZVI/PDS for the purposes of activating MP degradation was affirmed. The presence of Cl- can enhance the level of MP degradation was confirmed, which provides a new direction for future practical engineering applications.

Model simulation and mechanism of Fe(0/II/III) cycle activated persulfate degradation of methylparaben based on hydroxylamine enhanced nano-zero-valent iron.

The mechanism of Fe2+-activated peroxodisulfate (PDS) by hydroxylamine (HA) has been investigated, however, nano zero-valent iron-activated persulfate (nZVI/PDS) has a more optimal effect and needs further investigation. This study investigated the addition of HA to nZVI/PDS to improve Fe2+ regeneration and accelerate methylparaben (MP) degradation by Fe (0/II/III) cycle. After 60 min of reaction, the HA-enhanced nZVI/PDS (HA/nZVI/PDS) system afforded a 21% increase in MP degradation, reaching 93.26% (1 mM HA, 1 mM nZVI, and 2 mM PDS). nZVI/PDS system was a second-order reaction, but after adding HA, the reaction was more suitable for the first-order reaction. The addition of HA effectively promoted the reduction of Fe3+ to Fe2+ to improve the effect and reaction rate of PDS degradation of MP (k increased from 0.0127 min-1 to 0.0198 min-1) and broadened the reaction pH range. The results of various characterizations of nZVI before and after the reaction revealed that nZVI changed from a spherical structure to a bundle structure and was slightly oxidized. Changes in the Fe2+ and Fe3+ concentrations as well as in the pH of the reaction systems were monitored and the possible reactions of the HA/nZVI/PDS system were derived for the first time (knZVI/PDS<3.7 × 106 M-1 s-1, kFe3+/NH2O· >4.2 min-1). 12 potential compounds were investigated and MP breakdown pathways were speculated; hydroxylation was determined to be the most important pathway of degradation. And the HA/nZVI/PDS system had universal applicability.

Deep learning based adaptive sequential data augmentation technique for the optical network traffic synthesis.

The lack of the sufficient and diverse training data is one of the main challenges limiting performances of the machine learning enabled applications in optical networks. Here, we propose a deep learning based sequential data augmentation technique for the aggregate traffic data augmentation for diverse optical network scenarios. A generative adversarial network (GAN) model is trained with the experimental traffic data to automatically extract the substantial characteristics of the experimental traffic data through the zero-sum game theory and then augment the traffic data adaptively. The statistical evaluation parameters of the augmented traffic are mean, variance and Hurst exponent. To add comparisons, two other classical generative models including the statistical parameter configuration (SPC) model and the variational autoencoder (VAE) model are also adopted to generate the traffic data that are similar to the actual traffic data. The comprehensive comparisons among the proposed GAN, the SPC and VAE show that the performances of the GAN exceed those of the SPC and the VAE obviously. The mean and the variance of the augmented traffic data from the GAN are almost equal to those of the experimental traffic data, where the average deviations are both within 2%. The Hurst exponent of the augmented traffic data from the GAN is respectively near 90% and 96% of those of the experimental traffic data in the access network and the core network. To estimate the similarity intuitively, the well-known k-mean algorithm is used to cluster the augmented traffic data according to the centroids determined by the corresponding experimental traffic data and the clustering accuracies are all higher than 95% for 6 kinds of typical traffic types in the optical networks. These results demonstrate that the proposed GAN is able to effectively generate the traffic data that is very close to the experimental traffic data and is difficult to be distinguished for diverse traffic types. Moreover, a relatively small dataset with a few hundred pieces of experimental traffic data is required and the amount of the augmented traffic data from the GAN is unlimited in theory, which can be augmented as much as we need. The proposed traffic data augmentation technique also has the potential to be utilized in other sequential data augmentation applications for the optical networks.

Enhanced photocatalytic efficiency of C3N4/BiFeO3 heterojunctions: the synergistic effects of band alignment and ferroelectricity.

As one of the most promising photocatalysts, graphitic carbon nitride (g-C3N4) shows a visible light response and great chemical stability. However, its relatively low photocatalytic efficiency is a major obstacle to actual applications. Here an effective and feasible method to dramatically increase the visible light photocatalytic efficiency by forming C3N4/BiFeO3 ferroelectric heterojunctions is reported, wherein the band alignment and piezo-/ferroelectricity have synergistic positive effects in accelerating the separation of the photogenerated carriers. At the optimum composition of 10 wt% BiFeO3, the heterojunction shows 1.4 times improved photocatalytic efficiency than that of the pure C3N4. Most importantly, mechanical pressing and electrical poling can also improve the photocatalytic efficiencies by 1.3 times and 1.8 times, respectively. The optimized photocatalytic efficiency is even comparable with that of some noble metal based compounds. These results not only prove the improved photocatalytic activity of the C3N4-ferroelectric heterojunctions, but also provide a new approach for designing high-performance photocatalysts by taking advantage of ferroelectricity.

Failure prediction using machine learning and time series in optical network.

In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.

Intelligent constellation diagram analyzer using convolutional neural network-based deep learning.

An intelligent constellation diagram analyzer is proposed to implement both modulation format recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using convolution neural network (CNN)-based deep learning technique. With the ability of feature extraction and self-learning, CNN can process constellation diagram in its raw data form (i.e., pixel points of an image) from the perspective of image processing, without manual intervention nor data statistics. The constellation diagram images of six widely-used modulation formats over a wide OSNR range (15~30 dB and 20~35 dB) are obtained from a constellation diagram generation module in oscilloscope. Both simulation and experiment are conducted. Compared with other 4 traditional machine learning algorithms, CNN achieves the better accuracies and is obviously superior to other methods at the cost of O(n) computation complexity and less than 0.5 s testing time. For OSNR estimation, the high accuracies are obtained at epochs of 200 (95% for 64QAM, and over 99% for other five formats); for MFR, 100% accuracies are achieved even with less training data at lower epochs. The experimental results show that the OSNR estimation errors for all the signals are less than 0.7 dB. Additionally, the effects of multiple factors on CNN performance are comprehensively investigated, including the training data size, image resolution, and network structure. The proposed technique has the potential to be embedded in the test instrument to perform intelligent signal analysis or applied for optical performance monitoring.

Hollow ZnxCd1-xS nanospheres with enhanced photocatalytic activity under visible light.

Formation of solid solutions is a good strategy to acquire materials with special properties and bring forth new type of applications or enhance the performance of currently existing devices. In this study, hollow ZnxCd1-xS nanospheres with different molar ratios were synthesized via a facile hydrothermal process. The products were fully characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, and UV-vis absorption spectroscopy. It was found that the photocatalysis performance of the as-prepared samples could be enhanced by formation of ZnxCd1-xS solid solutions. In addition, their photocatalytic activities are dependent on the Zn/Cd molar ratios and nanostructures of ZnxCd1-xS solid solutions. Hollow Zn0.2Cd0.8S spheres exhibit extremely high photocatalytic activity and good re-usability, and the photocatalytic conversion of RhB reaches as high as 96% after 50 min of irradiation.

Diagnostic value of horizontal versus vertical sections for scarring and non-scarring alopecia: a systematic review and meta-analysis.

Histopathological diagnosis of scalp biopsies remains a challenging area in dermatopathology. Published studies have described the benefits of different sectioning techniques although the application of these techniques is still under clinical investigation. To review published literature and evaluate the value of horizontal sections compared to vertical sections in the diagnosis of alopecia. Databases, such as PubMed and EMBASE, among others, were searched for published articles; to identify additional relevant studies, the literature search was performed manually. The total number of cases and diagnosed cases, and diagnostic rates, were extracted from each included study. Pooled diagnostic rates with 95% confidence intervals (CI) were used to evaluate the value of different sectioning techniques, and we tested for publication bias and heterogeneity. Most studies had a suboptimal design. With regards to non-scarring alopecia, there were eight horizontal and eight vertical section studies. The pooled diagnostic rates were 0.81 (95% CI: 0.70-0.92) and 0.76 (95% CI: 0.60-0.93), respectively, and extensive heterogeneity existed among these studies. For the diagnosis of scarring alopecia, there were three horizontal and five vertical sectioning studies. The pooled diagnostic rates were 0.86 (95% CI: 0.66-1) and 0.90 (95% CI: 0.82-0.98), respectively, and heterogeneity was also observed. Based on published studies, no significant difference exists between horizontal and vertical sectioning techniques in the diagnosis of alopecia. Whereas most studies had a suboptimal design, future studies in this area would benefit from a scientific approach and standardised measurements to explore the value of horizontal and vertical sectioning.

Two-Dimensional Hollow TiO2 Nanoplates with Enhanced Photocatalytic Activity.

Two-dimensional anatase TiO2 hollow nanoplates were firstly synthesized through a facile synthesis route by using α-Fe2 O3 nanoplates as removable templates. Two-dimensional hollow TiO2 nanoplates with different ratios of anatase and rutile phases were obtained by adjusting the calcining temperature. The average diameters were around 600 nm, and the shell thickness was approximately 30 nm. The photocatalytic performance of TiO2 was investigated by decomposing rhodamine B under simulated sunlight. Among the TiO2 samples, the anatase TiO2 hollow nanoplates manifested a significant enhancement in the photocatalytic performances. The excellent catalytic performance can be attributed to the unique structure of the two-dimensional anatase TiO2 hollow nanoplates, including a large surface area and increased dye-photocatalyst contact areas as well as more active sites for photodegradation.

Generalized Low-Temperature Fabrication of Scalable Multi-Type Two-Dimensional Nanosheets with a Green Soft Template.

Two-dimensional nanosheets with high specific surface areas and fascinating physical and chemical properties have attracted tremendous interests because of their promising potentials in both fundamental research and practical applications. However, the problem of developing a universal strategy with a facile and cost-effective synthesis process for multi-type ultrathin 2 D nanostructures remains unresolved. Herein, we report a generalized low-temperature fabrication of scalable multi-type 2 D nanosheets including metal hydroxides (such as Ni(OH)2, Co(OH)2, Cd(OH)2, and Mg(OH)2), metal oxides (such as ZnO and Mn3O4), and layered mixed transition-metal hydroxides (Ni-Co LDH, Ni-Fe LDH, Co-Fe LDH, and Ni-Co-Fe layered ternary hydroxides) through the rational employment of a green soft-template. The synthesized crystalline inorganic nanosheets possess confined thickness, resulting in ultrahigh surface atom ratios and chemically reactive facets. Upon evaluation as electrode materials for pseudocapacitors, the Ni-Co LDH nanosheets exhibit a high specific capacitance of 1087 F g(-1) at a current density of 1 A g(-1), and excellent stability, with 103% retention after 500 cycles. This strategy is facile and scalable for the production of high-quality ultrathin crystalline inorganic nanosheets, with the possibility of extension to the preparation of other complex nanosheets.

The prevalence of BRCA1 and BRCA2 germline mutations in high-risk breast cancer patients of Chinese Han nationality: two recurrent mutations were identified.

To have an overview of the role of BRCA1 and BRCA2 genes among Chinese high-risk breast cancer patients, we analyzed 489 such high-risk breast cancer patients from four breast disease clinical centers in China, by using PCR-DHPLC or SSCP-DNA sequencing analysis. Allelotype analysis was done at five short tandem repeat (STR) markers in or adjacent to BRCA1 on the recurrent mutation carriers. For those analyzed both genes, 8.7% of early-onset breast cancer cases and 12.9% of familial breast cancer cases had a BRCA1 or BRCA2 mutation, as compared with the 26.1% of cases with both early-onset breast cancer and affected relatives. For those reporting malignancy family history other than breast/ovarian cancer, the prevalence of BRCA1/2 mutation is about 20.5%, and it was significantly higher than the patients only with family history of breast/ovarian cancer (P = 0.02). The family history of ovarian cancer (26.7% vs. 11.9%) and stomach cancer (23.8% vs. 11.8%) doubled the incidence of BRCA1/2, but the difference did not reach the statistical significance. Two recurrent mutations in BRCA1, 1100delAT and 5589del8, were identified. The recurrent mutations account for 34.8% BRCA1 mutations in our series. Similar allelotypes were detected in most STR status for those harboring the same mutations. The BRCA1 associated tumors were more likely to exhibit a high tumor grade, negative C-erbB-2/neu status and triple negative (ER, PgR and C-erbB-2/neu negative) status (P < 0.05). We recommended the BRCA1 and BRCA2 genetic analysis could be done for high-risk breast cancer patient in Chinese population, especially for those with both early-onset breast cancer and affected relatives. There may be some degree of shared ancestry for the two recurrent BRCA1 mutations in Chinese.