Inorganic semiconducting metal oxide electron transporting layers (ETLs) with tailored nanostructures have the potential to yield efficient perovskite solar cells (PSCs) with improved stability and ...reduced hysteresis. Here, a simple and facile hydrothermal protocol is demonstrated for the growth of crystalline SnO2 nanosheet (SNS) thin films with excellent optical and photoelectrical properties. When applied as ETLs, the SNS enhances light harvesting, boosts charge collection, reduces hysteresis and improves the ambient stability. Moreover, optimization of the interfacial properties between the SNS and the perovskite layer by the introduction of a C60 interlayer results in better energy level alignment, decreasing charge recombination and thus prolonging the electron lifetime and improving the open‐circuit voltage (Voc). Therefore, the efficient conventional n‐i‐p PSCs based on C60‐modified SNS ETLs have almost hysteresis‐free behavior, with a champion power conversion efficiency, PCE, of 18.31% and a stabilized PCE over 18.00%. The SNS‐based PSC exhibits respectable ambient stability retaining over 90% of its initial efficiency after being stored in air at room temperature for 500 h without encapsulation. Such robust SNS ETLs pave the way to low‐cost and stable PSCs with high efficiency, less hysteresis, and long‐term stability.
A power conversion efficiency of 18.31% and a remarkably good ambient stability have been achieved in perovskite solar cells using thin films of tin oxide nanosheets. These films are fabricated by a facile hydrothermal route giving promising semiconducting properties. The cells with an optimized SnO2 nanosheet/C60 electron transporting layer exhibit superior light harvesting, facilitated charge extraction, and suppressed charge recombination.
•Mycorrhizal hyphae have the role in water absorption from soil.•Hyphal water absorption rate was 0.126–1.973mg H2O/h/mm in trifoliate orange.•Drought significantly elevated hyphal water absorption ...rate by 2.3–6.6 times.•Hyphal water contribution to host plants is more important under DS than under WW.
Mycorrhizal hyphae have the functioning on water absorption from soil, while the information regarding hyphal water absorption rate is not fully known. In this study, 37-μm nylon meshes were positioned in a pot bottom to allow mycorrhizal extraradical hyphae, but not roots, to pass through the mesh. The whole pot was placed in a beaker, where distilled water was supplied, reaching a 0.5-cm air gap between the pot bottom and water surface of the beaker. Citrus rootstock, trifoliate orange (Poncirus trifoliata) seedlings of pots were inoculated with Funneliformis mosseae and Paraglomus occultum and also exposed to well-watered (WW) and drought stress (DS). The 71-days soil DS significantly inhibited root mycorrhizal colonization and hyphal length in soil and mesh, regardless of mycorrhizal fungal species. The hyphal water absorption rate was 0.607–1.973mg H2O/h/mm for F. mosseae and 0.126–0.963mg H2O/h/mm for P. occultum, respectively. The DS treatment significantly elevated hyphal water absorption rate by 2.3–6.6 times. The increase of leaf water potential by mycorrhization was higher under DS than under WW. Our results provide a quantitative estimation of water absorption rate by mycorrhizal extraradical hyphae and also suggest more important water contribution of hyphae to the host plant under DS than under WW.
Considering the high random and non-static property of the rainfall-runoff process, lots of models are being developed in order to learn about such a complex phenomenon. Recently, Machine learning ...techniques such as the Artificial Neural Network (ANN) and other networks have been extensively used by hydrologists for rainfall-runoff modelling as well as for other fields of hydrology. However, deep learning methods such as the state-of-the-art for LSTM networks are little studied in hydrological sequence time-series predictions. We deployed ANN and LSTM network models for simulating the rainfall-runoff process based on flood events from 1971 to 2013 in Fen River basin monitored through 14 rainfall stations and one hydrologic station in the catchment. The experimental data were from 98 rainfall-runoff events in this period. In between 86 rainfall-runoff events were used as training set, and the rest were used as test set. The results show that the two networks are all suitable for rainfall-runoff models and better than conceptual and physical based models. LSTM models outperform the ANN models with the values of R 2 and N S E beyond 0.9, respectively. Considering different lead time modelling the LSTM model is also more stable than ANN model holding better simulation performance. The special units of forget gate makes LSTM model better simulation and more intelligent than ANN model. In this study, we want to propose new data-driven methods for flood forecasting.
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► A fully resolved phylogeny of the major lineages of grasses was obtained. ► The (B,P)E hypotheses was strongly supported. ► The diversification of the BEP clade into three ...subfamilies happened within ∼4MY. ► The short internal branches in BEP clade caused incongruent phylogenies in studies.
Despite the considerable efforts to reconstruct the phylogeny of grasses, the relationships among the subfamilies Bambusoideae, Pooideae and Ehrhartoideae in the BEP clade remain unresolved. Here we completely sequenced three chloroplast genomes of representative species from Bambusoideae and Ehrhartoideae and obtained 19 additional chloroplast genome sequences of other grasses from GenBank. Using sequences of 76 chloroplast protein-coding genes from the 22 grass species, we fully resolved the phylogeny of the BEP clade. Our results strongly supported the (B,P)E hypothesis, i.e., Bambusoideae and Pooideae are more closely related than Ehrhartoideae. This result was not biased by systematic or sampling errors and was impervious to phylogenetic methods or model specification. The divergence time estimate suggests that the initial diversification of the BEP clade into three subfamilies happened within a short time period (∼4MY). The presence of these short internal branches may explain the inability of previous studies to achieve a confident resolution of the BEP clade. The combination of the sequences of the entire chloroplast genomes provided sufficient phylogenetic information to resolve the BEP phylogeny fully. These results provide a valuable evolutionary framework for comparative and functional genomic studies using the grass family as a model system.
α‐Haloboronates are useful organic synthons that can be converted to a diverse array of α‐substituted alkyl borons. Methods to α‐haloboronates are limiting and often suffer from harsh reaction ...conditions. Reported herein is a photochemical radical C‐H halogenation of benzyl N‐methyliminodiacetyl (MIDA) boronates. Fluorination, chlorination, and bromination reactions were effective by using this protocol. Upon reaction with different nucleophiles, the C−Br bond in the brominated product could be readily transformed to a series of C−C, C−O, C−N, C−S, C−P, and C−I bonds, some of which are difficult to forge with α‐halo sp2‐B boronate esters. An activation effect of B(MIDA) moiety was found.
A photochemical radical C−H halogenation of benzyl N‐methyliminodiacetyl (MIDA) boronates is described. Fluorination, chlorination, and bromination reactions were all effective using this method. The brominated products serve for the synthesis of α‐functionalized alkyl boron compounds. By virtue of coordination saturation of the MIDA boron moiety, some synthetically challenging α‐substituted alkyl borons are also accessible.
Monosaccharides play significant roles in daily metabolism in living organisms. Although various devices have been constructed for monosaccharide identification, most rely on the specificity of the ...natural enzyme. Herein, inspired by natural ionic channels, an asymmetrical MOF-in-nanochannel architecture is developed to discriminate monosaccharide enantiomers based on cascade reactions by combining oxidase-mimicking and Fenton-like catalysis in homochiral mesoporous CuMOF pockets. The identification performance is remarkably enhanced by the increased oxidase-mimicking activity of Au nanoparticles under a local surface plasmon resonance (LSPR) excitation. The apparent steady-state kinetic parameters and nano-fluidic simulation indicate that the different affinities induced by Au-LSPR excitation and the confinement effect from MOF pockets precipitate the high chiral sensitivity. This study offers a promising strategy for designing an enantiomer discrimination device and helps to gain insight into the origin of stereoselectivity in a natural enzyme.
An asymmetrical architecture is developed to discriminate monosaccharide enantiomers based on enzyme-like cascade reactions in homochiral CuMOF pockets. The increased enzyme-like activity under LSPR excitation enhanced the identification performance.
Micro-/nanofluidics has received considerable attention over the past two decades, which allows efficient biomolecule trapping and preconcentration due to ion concentration polarization (ICP) within ...nanostructures. The rich scientific content related to ICP has been widely exploited in different applications including protein concentration, biomolecules sensing and detection, cell analysis, and water purification. Compared to pure microfluidic devices, micro-/nanofluidic devices show a highly efficient sample enrichment capacity and nonlinear electrokinetic flow feature. These two unique characterizations make the micro-/nanofluidic systems promising in high-performance bioanalysis. This review provides a comprehensive description of the ICP phenomenon and its applications in bioanalysis. Perspectives are also provided for future developments and directions of this research field.
Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for a labeling large number of images (i.e., annotation), the amount of ...available training data (i.e., real data) is always limited. To produce more data for training a deep network, generative adversarial network can be used to generate artificial sample data (i.e., generated data). However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to pre-defined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this paper is a set of weight-based values each individual of which is a number in (0,1 called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets, respectively, and outperforms state-of-the-art methods.
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