Although flexible and multifunctional textiles are promising for wearable electronics and portable device applications, the main issue is to endow textiles with multifunctionalities while maintaining ...their innate flexible and porous features. Herein, a vacuum‐assisted layer‐by‐layer assembly technique is demonstrated to conformally deposit electrically conductive substances on textiles for developing multifunctional and flexible textiles with superb electromagnetic interference (EMI) shielding performances, superhydrophobicity, and highly sensitive humidity response. The formed leaf‐like nanostructure is composed of silver nanowires (AgNWs) as the highly conductive skeleton (vein) and transition metal carbide/carbonitride (MXene) nanosheets as the lamina. The presence of MXene protects AgNWs from oxidation and enhances the combination of AgNWs with the fabric substrate, and the transformation of its functional groups leads to self‐derived hydrophobicity. The flexible and multifunctional textile exhibits a low sheet resistance of 0.8 Ω sq−1, outstanding EMI shielding efficiency of 54 dB in the X‐band at a small thickness of 120 µm, and highly sensitive humidity responses, while retaining its satisfactory porosity and permeability. The self‐derived hydrophobicity with a large contact angle of >140° is achieved by aging the hydrophilic MXene coated silk. The wearable multifunctional textiles are highly promising for applications in intelligent garments, humidity sensors, actuators, and EMI shielding.
A biomimetic leaf‐like nanostructure composed of a 1D AgNWs skeleton (vein) and 2D MXene as the lamina is fabricated via vacuum‐assisted layer‐by‐layer assembly for electromagnetic interference (EMI) shielding, humidity monitoring, and self‐derived hydrophobicity. The (MA1)10 silk presents an exceptional EMI shielding effectiveness of ≈90 dB at 12.4 GHz at a thickness of 480 µm, and the MXene‐coated textile induces a hydrophilic‐to‐hydrophobic transition, generating a large contact angle of >140°.
Graphene fibers with integrated mechanical and multifunctional properties are highly required for various potential applications. However, it remains a challenge to efficiently produce ...high‐performance graphene fibers because of the imperfect structures and harsh graphitization conditions. Herein, a scalable additive‐free wet‐spinning methodology is demonstrated for producing strong, tough, and conductive pristine graphene fibers by optimizing the surface chemistry of graphene oxide (GO) sheets and controlling their spinning and assembly behavior. Benefiting from GO with fewer surface terminations and low structural defects (f‐GO), the pristine f‐GO fibers possess a compact and ordered microstructure and strong interlayer interactions, giving a record high tensile strength of 791.7 MPa and high toughness of 24.0 MJ m−3 due to the stretching‐induced toughening behavior. After the mild chemical reduction, reduced f‐GO fibers inherit the optimized microstructure and present an outstanding tensile strength of 875.9 MPa and high toughness of 13.3 MJ m−3. Furthermore, the repairable structural defects on the f‐GO sheets allow the instant restoration of intrinsic conjugated structures, affording a superb electrical conductivity of 1.06 × 105 S m−1. Therefore, this study provides a facile, efficient, and scalable methodology for the fabrication of high‐performance and multifunctional graphene fibers and flexible wearable devices.
An additive‐free wet‐spinning methodology is proposed for producing graphene fibers by using graphene oxide with fewer terminations and low structural defects (f‐GO), exhibiting high tensile strength of 791.7 MPa and toughness of 24.0 MJ m−3. The unique chemical structure allows the reduced f‐GO fibers to present a tensile strength of 875.9 MPa and a conductivity of 1.06 × 105 S m−1.
Green innovation plays an important role in reducing pollution and promoting sustainable development. However, not all managers, as decision-makers of enterprises, have a long-term vision to ...implement green innovation. The impact of managerial myopia on firm green innovation has not been examined by existing literature. Drawn on time-oriented theory in social psychology and upper echelon theory in management, this paper puts forward the relationship between managerial myopia and firm green innovation, and then constructs a managerial myopia index by text analysis and machine learning. Using a sample of publicly listed Chinese firms from 2009 to 2020, this paper finds that managerial myopia is significantly negatively associated with firms’ green innovation. Every one standard deviation increase in managerial myopia makes enterprise green innovation decrease by about 1.9% standard deviation. Further analysis reveals that equity incentives and institutional investors both weaken the negative effect of managerial myopia on green innovation. This study contributes to the literature on green innovation and upper echelons theory and it also has several implications for firms’ engagement in green innovation.
The development of Industrial Internet of Things (IIoT) has been limited due to the shortage of spectrum resources. Based on cognitive radio, the cognitive IIoT (CIIoT) has been proposed to improve ...spectrum utilization via sensing and accessing the idle spectrum. To improve sensing and transmission performance of the CIIoT, a cluster-based CIIoT is proposed, in this article, wherein the cluster heads perform cooperative spectrum sensing to get available spectrum, and the nodes transmit via nonorthogonal multiple access (NOMA). The frame structure of the CIIoT is designed, and the spectrum access probability and average total throughput of the CIIoT are deduced. A joint resource optimization for sensing time, node powers, and the number of clusters is formulated to maximize the average total throughput. The optimal solution is obtained via sensing and power optimization. The clustering algorithm and cluster head alternation are proposed to improve transmission performance and ensure energy balance, respectively. The simulations have indicated that the NOMA for the cluster-based CIIoT can better guarantee the transmission performance of each node, especially the node decoded first, than the traditional NOMA and orthogonal multiple access.
Highlights
Super-tough and ultra-strong ANF@MXene fibers are wet-spun by a coaxial technique.
High toughness of ~ 48.1 MJ m
−3
and strength of ~ 502.9 MPa are achieved.
The fibers exhibit superb ...chemical stability under extreme environmental conditions.
Although electrically conductive and hydrophilic MXene sheets are promising for multifunctional fibers and electronic textiles, it is still a challenge to simultaneously enhance both conductivity and mechanical properties of MXene fibers because of the high rigidity of MXene sheets and insufficient inter-sheet interactions. Herein, we demonstrate a core–shell wet-spinning methodology for fabricating highly conductive, super-tough, ultra-strong, and environmentally stable Ti
3
C
2
T
x
MXene-based core–shell fibers with conductive MXene cores and tough aramid nanofiber (ANF) shells. The highly orientated and low-defect structure endows the ANF@MXene core–shell fiber with super-toughness of ~ 48.1 MJ m
−3
, high strength of ~ 502.9 MPa, and high conductivity of ~ 3.0 × 10
5
S m
−1
. The super-tough and conductive ANF@MXene fibers can be woven into textiles, exhibiting an excellent electromagnetic interference (EMI) shielding efficiency of 83.4 dB at a small thickness of 213 μm. Importantly, the protection of the ANF shells provides the fibers with satisfactory cyclic stability under dynamic stretching and bending, and excellent resistance to acid, alkali, seawater, cryogenic and high temperatures, and fire. The oxidation resistance of the fibers is demonstrated by their well-maintained EMI shielding performances. The multifunctional core–shell fibers would be highly promising in the fields of EMI shielding textiles, wearable electronics and aerospace.
Polycomb repressive complex 2 (PRC2) catalyzes histone H3K27 trimethylation (H3K27me3), a hallmark of gene silencing. Here we report the crystal structures of an active PRC2 complex of 170 ...kilodaltons from the yeast Chaetomium thermophilum in both basal and stimulated states, which contain Ezh2, Eed, and the VEFS domain of Suz12 and are bound to a cancer-associated inhibiting H3K27M peptide and a S-adenosyl-l-homocysteine cofactor. The stimulated complex also contains an additional stimulating H3K27me3 peptide. Eed is engulfed by a belt-like structure of Ezh2, and Suz12(VEFS) contacts both of these two subunits to confer an unusual split active SET domain for catalysis. Comparison of PRC2 in the basal and stimulated states reveals a mobile Ezh2 motif that responds to stimulation to allosterically regulate the active site.
We study the age of the subducting Pacific slab beneath East Asia using a high-resolution model of P-wave tomography and paleo-age data of ancient seafloor. Our results show that the lithosphere age ...of the subducting slab becomes younger from the Japan Trench (∼130 Ma) to the slab's western edge (∼90 Ma) beneath East China, and the flat (stagnant) slab in the mantle transition zone (MTZ) is the subducted Pacific plate rather than the proposed Izanagi plate which should have already collapsed into the lower mantle. The flat Pacific slab has been in the MTZ for no more than ∼10–20 million years, considerably less than the age of the big mantle wedge beneath East Asia (>110 million years). Hence, the present flat Pacific slab in the MTZ has contributed to the Cenozoic destruction of the East Asian continental lithosphere with extensive intraplate volcanism and back-arc spreading, whereas the destruction of the North China Craton during the Early Cretaceous (∼140–110 Ma) was caused by the subduction of the Izanagi (or the Paleo-Pacific) plate.
•Age distribution of the subducting Pacific slab beneath East Asia is estimated.•The current flat slab in the MTZ beneath East Asia is the subducting Pacific plate.•The big mantle wedge has existed much longer than any slabs in the MTZ.
Data visualization plays a crucial role in illustrating results and sharing knowledge among researchers. Though many types of visualization tools are widely used, most of them require enough coding ...experience or are designed for specialized usages, or are not free. Here, we present ImageGP, a specialized visualization platform designed for biology and chemistry data illustration. ImageGP could generate generalized plots like lines, bars, scatters, boxes, sets, heatmaps, and histograms with the most common input content in a user‐friendly interface. Normally plotting using ImageGP only needs a few mouse clicks. For some plots, one only needs to just paste data and click submit to get the visualization results. Additionally, ImageGP supplies up to 26 parameters to meet customizable requirements. ImageGP also contains specialized plots like volcano plot, functional enrichment plot for most omics‐data analysis, and other four specialized functions for microbiome analysis. Since 2017, ImageGP has been running for nearly 5 years and serving 336,951 visits from all over the world. Together, ImageGP (http://www.ehbio.com/ImageGP/) is an effective and efficient tool for experimental researchers to comprehensively visualize and interpret data generated from wet‐lab and dry‐lab.
Representative visualization results of ImageGP. ImageGP supports 16 types of images (including heatmap, volcano plot, enrichment bubble plot) and four types of online analysis with up to 26 parameters for customization.
Highlights
Publication‐quality visualization results.
Easy to use and customize.
Reproducible results with scripts.
A new chitosan biosorbent modified with zirconium ions (ZCB) was prepared and employed for the adsorption of phosphate ions from aqueous solution. Batch of experiments were performed to investigate ...the effects of various conditions on the phosphate adsorption, including contact time, the initial concentration of phosphate ions, temperature and pH. The experimental data were interpreted elaborately by the different models of adsorption kinetics and isotherms. The results showed that the kinetic data were well fitted to the pseudo first-order and second-order models, indicating that the adsorption behaviors were mainly ascribed to both physic-sorption and chemic-sorption. The further analysis of kinetics indicated that the intra-particle diffusion was the rate controlling process. The equilibrium data were best described by the Langmuir isotherm model with maximum monolayer adsorption capacity of 60.6mgg−1. The values of △G°, △S° and △H° revealed that the nature of adsorption was feasible, spontaneous and endothermic. FTIR, EDS, XPS and zeta potential studies suggested that the adsorption mechanism was possibly attributed to the electrostatic attraction and ion exchange reaction between ZCB and phosphate ions.
Display omitted
•Chitosan beads modified with zirconium (ZCB) were prepared and used for P sorption.•Various models and isotherms were employed to describe the adsorption process.•Isoelectric points of ZCB and ZCB-P kept the same charge at pH of 3.5.•Mechanism for P adsorption was electrostatic attraction and ion exchange reaction.
Sentiment analysis is one of the important tasks of online opinion analysis and an important means to guide the direction of online opinion and maintain social stability. Due to the multiple ...characteristics of linguistic expressions, ambiguity, multiple meanings of words, and the increasing speed of new words, it is a great challenge for the task of text sentiment analysis. Commonly used machine learning methods suffer from inadequate text feature extraction, and the emergence of deep learning has brought a turnaround for this purpose. In this paper, we investigate the problem of text sentiment analysis using methods related to deep learning. In order to incorporate user and product information in a more diverse way in the model, this paper proposes a model based on a deep bidirectional long-and short-term memory network-self-attention mechanism-custom classifier. The model first identifies contextual associations and acquires deep text features through a deep bidirectional long- and short-term memory network and then captures important features in the text using a self-attentive mechanism. The model finally combines user information and product information to build a custom classifier module and uses context-aware attention mechanisms to assign specific parameters to user information and product information, which improves the performance of the model on public datasets compared with current common models. The results show that the accuracy of the algorithm in this paper is high, and it is about 5% lower than the traditional algorithm. The method can reduce the number of iterations and the running time of the algorithm.
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