Confined Water Dominates Ion/Molecule Transport in Hydrogel Nanochannels.

Current artificial nanochannels rely more on charge interactions for intelligent mass transport. Nevertheless, popular charged nanochannels would lose their advantages in long-term applications. Confined water, an indispensable transport medium in biological nanochannels, dominating the transport process in the uncharged nanochannels perfectly provides a new perspective. Herein, we achieve confined-water-dominated mass transport in hydrogel nanochannels (HNCs) constructed by in situ photopolymerization of acrylic acid (PAA) hydrogel in anodic alumina (AAO) nanochannels. HNCs show selectivity to Na+ transport and a high transport rate of molecules after introducing Na+/Li+, compared with other alkali metal ions like Cs+/K+. The mechanism given by ATR-FTIR shows that the hydrogen-bonding structure of confined water in HNCs is destabilized by Na+/Li+, which facilitates mass transport, but is constrained by Cs+/K+, resulting in transport inhibition. This work elucidates the relationship between confined water and mass transport in uncharged nanochannels while also presenting a strategy for designing functional nanochannel devices.

Target Recognition of Coal and Gangue Based on Improved YOLOv5s and Spectral Technology.

Aiming at the problems of long detection time and low detection accuracy in the existing coal gangue recognition, this paper proposes a method to collect the multispectral images of coal gangue using spectral technology and match with the improved YOLOv5s (You Only Look Once Version-5s) neural network model to apply it to coal gangue target recognition and detection, which can effectively reduce the detection time and improve the detection accuracy and recognition effect of coal gangue. In order to take the coverage area, center point distance and aspect ratio into account at the same time, the improved YOLOv5s neural network replaces the original GIou Loss loss function with CIou Loss loss function. At the same time, DIou NMS replaces the original NMS, which can effectively detect overlapping targets and small targets. In the experiment, 490 sets of multispectral data were obtained through the multispectral data acquisition system. Using the random forest algorithm and the correlation analysis of bands, the spectral images of the sixth, twelfth and eighteenth bands from twenty-five bands were selected to form a pseudo RGB image. A total of 974 original sample images of coal and gangue were obtained. Through two image noise reduction methods, namely, Gaussian filtering algorithm and non-local average noise reduction, 1948 images of coal gangue were obtained after preprocessing the dataset. This was divided into a training set and test set according to an 8:2 ratio and trained in the original YOLOv5s neural network, improved YOLOv5s neural network and SSD neural network. By identifying and detecting the three neural network models obtained after training, the results can be obtained, the loss value of the improved YOLOv5s neural network model is smaller than the original YOLOv5s neural network and SSD neural network, the recall rate is closer to 1 than the original YOLOv5s neural network and SSD neural network, the detection time is the shortest, the recall rate is 100% and the average detection accuracy of coal and gangue is the highest. The average precision of the training set is increased to 0.995, which shows that the improved YOLOv5s neural network has a better effect on the detection and recognition of coal gangue. The detection accuracy of the improved YOLOv5s neural network model test set is increased from 0.73 to 0.98, and all overlapping targets can also be accurately detected without false detection or missed detection. At the same time, the size of the improved YOLOv5s neural network model after training is reduced by 0.8 MB, which is conducive to hardware transplantation.

A Facile Strategy To Construct Au@VxO2x+1 Nanoflowers as a Multicolor Electrochromic Material for Adaptive Camouflage.

Transition metal oxides (TMOs) are promising inorganic electrochromic materials (ECMs) that can be widely used in electronic displays and adaptive camouflage. However, there are still huge challenges for TMOs to simultaneously achieve multicolor transformation capability and good cycling stability. Herein, we assemble Au-modified (0.01 wt %) VxO2x+1 (x > 2) nanoflowers (Au@VxO2x+1 NFs) composed of two-dimensional porous nanosheets containing two valences states of vanadium (V4+ and V5+). The Au@VxO2x+1 NFs exhibits outstanding electrochromic performance with five reversible color transformations (orange, yellow, green, gray, and blue) at a voltage less than 1.5 V and excellent cycling stability (2000 cycles without significant decay). To the best of our knowledge, this is the first time that a single vanadium oxide ECM, rather than a device, realizes five color changes. This work provides a feasible way for the efficient preparation of multicolor electrochromic TMOs. The newly developed Au@VxO2x+1 NFs demonstrate the potential application in adaptive camouflage.

Chemi-Mechanically Peeling the Unstable Surface States of α-FAPbI3.

Surface states are one of the crucial factors determining the phase stability of formamidinium-based perovskites. Compared with other compositions, exclusive lattice strain in FAPbI3 perovskite generates defects at the surface more readily, making them more vulnerable at the surface and easier to trigger the phase transition from α-phase to the non-perovskite δ-phase. In order to regulate the surface quality, here, a chemi-mechanical cleavage approach is reported, i.e., tape peel-zone (PZ), implemented by attaching and peeling off the ordinary Kapton Tapes. The PZ approach can simultaneously eliminate the surface defects of perovskite and siliconize the film surface with hydrophobic silicone compounds. These two functionalities endow α-FAPbI3 perovskite with a robust hydrophobic surface, which can sustain for 30 days under a relative humidity of 60% and withstand the high temperature up to 240 °C. The unencapsulated PZ-treated cells show 80.3% of initial performance after 90 h of continuous operation in ambient air, which is 31.4 times more stable than the pristine cell.

Feasibility of N2 Reduction on the V Anchored 1T-MoS2 Monolayer: A Density Functional Theory Study.

Developing efficient electrocatalysts for nitrogen reduction reaction (NRR) at ambient conditions is crucial for NH3 synthesis. In this manuscript, the NRR performance of the transition metal anchored MoS2 monolayer with 1T atomic structure (1T-MoS2 ) is systematically evaluated by density functional theory computations. Our results reveal that the V decorated 1T-MoS2 exhibits the outstanding catalytic activity toward NRR via distal mechanism where the corresponding onset potential is 0.66 V, being superior to the commercial Ru material. Furthermore, the powerful binding energy between the V atom and the 1T-MoS2 provides the good resistance against clustering of the V dopant, indicating its stability. Overall, this work provides a potential alternative for the application of NH3 synthesis.

Feasibility of N2 Reduction on the V Anchored 1T-MoS2 Monolayer: A Density Functional Theory Study.

The front cover artwork is provided by the groups of Prof. BeiBei, Xiao and Lei, Yang (Jiangsu University of Science and Technology, China) as well as Dr. ErHong, Song (Shanghai Institute of Ceramics, Chinese Academy of Sciences, China). The image shows that the environmental-friendly N2 -to-NH3 conversion is achieved by the V decorated MoS2 monolayer with the 1T atomic configuration, featured by the effective electrocatalysis in combination with the good electronic conductivity. Read the full text of the Article at 10.1002/cphc.202000147.

Exploring the ring-opening reactions of imidazo[1,5-a]quinolines for the synthesis of imides under photochemical conditions.

The ring-opening reaction of imidazo[1,5-a]quinolines under photoredox conditions has been described. With Eosin Y as the organophotoredox catalyst, synthetically useful and medicinally important imides were obtained in moderate to excellent yields under mild reaction conditions.

Effects of co-overexpression of secretion helper factors on the secretion of a HSA fusion protein (IL2-HSA) in pichia pastoris.

Pichia pastoris is generally considered as an expression host for heterologous proteins with the coding gene under control of the alcohol oxidase 1 (AOX1) promoter. The secretion of heterologous proteins in P. pastoris can be potentially affected by many factors. Based on our previous results, the secretion levels of human albumin (HSA) fusion protein IL2-HSA were only around 500 mg/L or less in fermentor cultures, which decreased more than 50% compared with that of HSA (>1 g/L). In this study, we selected five potential secretion helper factors, in which Ero1, Pdi1 and Kar2 were involved in protein folding and Sec1 and Sly1 were involved in vesicle trafficking. We evaluated the possible effects of individual overexpression of these secretion helper factors on the secretion of IL2-HSA in P. pastoris. Constitutive overexpression of the five selected secretion factors did not have an obvious negative effect on cell growth of the IL2-HSA secreting strain. Individual co-overexpression of Ero1, Kar2, Pdi1, Sec1 and Sly1 improved the secretion level of IL2-HSA to ~2.3-, 1.9-, 2.2-, 2.5- and 1.9-fold that in the control strain respectively in shake flasks. We evaluated the changes in mRNA and protein levels of the intracellular IL2-HSA, as well as the secretion helper factor genes in the co-overexpressing strains. Our results indicated that manipulating the expression level of ER resident protein Pdi1, Ero1, Kar2 and SM protein Sec1 and Sly1 could improve the secretion level of IL2-HSA fusion protein in P. pastoris, which provided new candidates for combinatorial engineering in future study. Copyright © 2016 John Wiley & Sons, Ltd.

Map-based cloning reveals the complex organization of the BnRf locus and leads to the identification of BnRf(b), a male sterility gene, in Brassica napus.

KEY MESSAGE: Sequencing of BAC clones reveals the complex organization of the BnRf locus and allowed us to clone BnRf (b) , which encodes a nucleus-localized chimeric protein BnaA7.mtHSP70-1-like. The male sterility in an extensively used genic male sterility (GMS) line (9012A) in Brassica napus was regarded to be conferred by BnMs3/Bnms3 and the multiallelic BnRf locus including three alleles. We previously mapped BnRf to a 13.8 kb DNA fragment on the B. napus chromosome A7. In the present study, we isolated bacterial artificial chromosome clones individually covering the restorer allele BnRf (a) and the male-sterile allele BnRf (b) , and revealed that the candidate regions of BnRf (a) and BnRf (b) show complex structural variations relative to the maintainer allele BnRf (c). By analyzing the recombination events and the newly developed markers, we delimited BnRf (a) to a 35.9 kb DNA fragment that contained seven predicted open-reading frames (ORFs). However, genetic transformation of the ORF G14 from both the male-sterile and restorer lines into wild-type Arabidopsis plants led to a stable male-sterile phenotype matching a 9012A-derived GMS line (RG206A); moreover, the male sterility caused by G14 could be fully recovered by the restorer gene BnMs3. These facts indicate that BnRf (b) corresponds to G14 while BnRf (a) likely associates with another flanking ORF. G14 encodes a nucleus-localized chimeric protein designated as BnaA7.mtHSP70-1-like. Ectopic expression of G14 in Arabidopsis negatively regulates some vital genes responsible for tapetum degeneration, and delayed programmed cell death of tapetum and led to the developmental arrest of tetrads. Our work not only presents new insights on the hereditary model of sterility control but also lays a solid foundation for dissecting the molecular basis underlying male sterility and restoration in 9012A.

A triple-crosslinking strategy for high-performance regenerated cellulose fibers derived from waste cotton textiles.

Regenerated cellulose fibers has attracted increasing attention for high-grade textile raw materials and industrial textiles, but the low mechanical property caused by differences in regenerated raw materials and production levels limits its commercial application in the product diversity. Herein, we proposed a novel triple-crosslinking strategy by coupling with hydrogen bonds, chemical crosslinking, and internal mineralization from multiple pulsed vapor phase infiltration (MPI) to improve the mechanical performance of regenerated cellulose fibers. A binary solvent composed of ionic liquid (IL) and dimethyl sulfoxide (DMSO) is used to dissolve waste cotton textile and then wet spinning. Dual-crosslinking is firstly achieved by coupling glutaraldehyde (GA) and cellulose reaction. Subsequently, a metal oxide is intentionally infiltrated into inner cellulosic through MPI technology to form a third form of crosslinking, accompanied by the ultra-thin metal oxide nano-layer onto the surface of regenerated cellulose fibers. Results showed that the triple-crosslinking strategy has increased the tensile stress of the fiber by 43.57 % to 287.03 MPa. In all, triple-crosslinking strategy provides a theoretical basis and technical approach for the reinforcement of weak fibers in waste cotton recycling, which is expected to accelerate the development of the waste textile recycling industry and promote of the added-value of regenerated products.

Advances in regenerated cellulosic aerogel from waste cotton textile for emerging multidimensional applications.

Rapid development of society and the improvement of people's living standards have stimulated people's keen interest in fashion clothing. This trend has led to the acceleration of new product innovation and the shortening of the lifespan for cotton fabrics, which has resulting in the accumulation of waste cotton textiles. Although cotton fibers can be degraded naturally, direct disposal not only causes a serious resource waste, but also brings serious environmental problems. Hence, it is significant to explore a cleaner and greener waste textile treatment method in the context of green and sustainable development. To realize the high-value utilization of cellulose II aerogel derived from waste cotton products, great efforts have been made and considerable progress has been achieved in the past few decades. However, few reviews systematically summarize the research progress and future challenges of preparing high-value-added regenerated cellulose aerogels via dissolving cotton and other cellulose wastes. Therefore, this article reviews the regenerated cellulose aerogels obtained through solvent methods, summarizes their structure, preparation strategies and application, aimed to promote the development of the waste textile industry and contributed to the realization of carbon neutrality.

Protective effect of sesaminol from Sesamum indicum Linn. against oxidative damage in PC12 cells.

Sesaminol is one component of sesame oil and has been widely used as the stabilizer to extend the storage period of food oil in China. In this study, we tried to investigate the antioxidant activity of sesaminol on rat pheochromocytoma (PC12) cells oxidative damaged by H2 O2 . Cell viability, LDH level and apoptosis of the PC12 cells were assayed after treatment with sesaminol for 3 h and exposure to H2 O2 . Furthermore, superoxide (SOD), catalase (CAT), glutathione peroxidase (GSH-Px) and intracellular ROS were assayed after exposure of the PC12 cells to H2 O2 . The results showed that pre-treatment with sesaminol prior to H2 O2 exposure significantly elevated cell survival rate and SOD, CAT and GSH-Px activity. Meanwhile, sesaminol declined the secreted LDH level, apoptosis rate and ROS level of H2 O2 exposed cells. Thus, sesaminol may protect PC12 against oxidative injury.

Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM.

Transformer fault diagnosis is a necessary operation to ensure the stable operation of a power system. In view of the problems of the low diagnostic rate and long time needed in traditional methods, such as the dissolved gas in oil method, a laser-induced fluorescence (LIF) spectral technology is proposed in this paper, which incorporated an improved aquila optimizer (IAO) and light gradient boosting machine (LightGBM), to predict the types of transformer faults. The original AO was improved using the Nelder Mead (NM) simple search method and opposition-based learning (OBL) mechanism, which could improve the parameter optimization ability of the model. Normal oil, thermal fault oil, local moisture oil, and electrical fault oil were selected as experimental samples. First, the spectral images of the four oil samples were obtained by LIF technology, and the fluorescence spectral curves obtained were preprocessed by multivariate scattering correction (MSC) and normalization (normalize), while kernel-based principle component analysis (KPCA) was used for dimensional reduction. The dimensionality-reduced data were then imported into the LightGBM model for training, and the IAO algorithm was used to optimize the parameters of the LightGBM. Finally, the experiment showed that the LIF technology demonstrated good recognition of the fault types for transformer fault diagnosis; the data purity after MSC preprocessing was higher than that of other processing methods; the prediction effect of the LightGBM model was superior to other prediction models; the LightGBM model optimized by IAO had better convergence, parameter optimization ability, and prediction accuracy than the LightGBM model optimized by the original AO and particle swarm optimization (PSO). Among the models, the MSC-IAO-LightGBM model had the best effect on fault prediction, with the mean square error (MSE) reaching 9.0643 × 10-7, mean absolute error (MAE) reaching 8.7439 × 10-4, and goodness of fit (R2) approaching 1. It can be implemented as a new diagnostic method in transformer fault detection, which is of great significance to ensure the stable and safe operation of power systems.

Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum.

Accurate diagnosis of transformer faults can effectively improve the enduring reliability of power grid operation. Aiming at overcoming the problems of long time consumption and low diagnostic rate in the past diagnosis methods, this article designs a laser-induced fluorescence (LIF) detection system, which can be combined with a multi-scale one-dimensional convolution neural network (MS1DCNN) to diagnose transformer fault categories. The structural parameters of MS1DCNN are optimized using the improved wild horse optimizer (IWHO). Electrical fault oil, thermal fault oil, normal oil and locally damped oil are used as raw materials for the experiment. First, the LIF spectral data of the four kinds of oil samples are obtained, and the spectral data obtained are pretreated by standard normal variate (SNV) and multiple scattering correction (MSC), and the dimensions are reduced by linear discriminant analysis (LDA) and kernel principal component analysis (KPCA). Then the dimensionality reduced data are imported into the MS1DCNN algorithm for learning, and the parameters of MS1DCNN are optimized using the IWHO algorithm. Finally, the experiment shows that the efficiency and precision of LIF technology for raw data extraction are higher than for traditional methods; in comparison with the same type of algorithm, MSC has a better preprocessing effect, KPCA has a better dimensionality reduction effect, MS1DCNN has a better prediction effect, and IWHO has a better optimization effect. Compared to them, the MSC-KPCA-IWHO-MS1DCNN model has the best diagnostic ability, with a mean square error (MSE) of 4.9037 × 10-4, mean absolute error (MAE) of 0.0179, and goodness of fit (R2) of 0.9996. Transformer fault intelligent diagnosis is necessary for the sustained and stable operation of power networks.

Absence of Yps7p, a putative glycosylphosphatidylinositol-linked aspartyl protease in Pichia pastoris, results in aberrant cell wall composition and increased osmotic stress resistance.

Recently, studies performed on Saccharomyces cerevisiae and Candida albicans have confirmed the importance of fungal glycosylphosphatidylinositol (GPI)-anchored aspartyl proteases (yapsins) for cell-wall integrity. Genome sequence annotation of Pichia pastoris also revealed seven putative GPI-anchored aspartyl protease genes. The five yapsin genes assigned as YPS1, YPS2, YPS3, YPS7 and MKC7 in P. pastoris were disrupted. Among these putative GPI-linked aspartyl proteases, disruption of PpYPS7 gene confers the Ppyps7Δ mutant cell increased resistance to cell wall perturbing reagents congo red, calcofluor white (CW) and sodium dodecyl sulfate. Quantitative analysis of cell wall components shows lower content of chitin and increased amounts of ß-1,3-glucan. Further staining of the cell with CW demonstrates that disruption of PpYPS7 gene causes a reduction of the chitin content in lateral cell wall. Consistently, transmission electron micrographs show that the inner layer of mutant cell wall, mainly composed of chitin and ß-1, 3-glucan, is much thicker than that in parental strain GS115. Additionally, Ppyps7Δ mutant also exhibits increased osmotic resistance compared with parental strain GS115. This could be due to the dramatically elevated intracellular glycerol level in Ppyps7Δ mutant. These results suggest that PpYPS7 is involved in cell wall integrity and response to osmotic stress.

Ultrasensitive Airflow Sensors Based on Suspended Carbon Nanotube Networks.

High-performance airflow sensors are in great demand in numerous fields but still face many challenges, such as slow response speed, low sensitivity, large detection threshold, and narrow sensing range. Carbon nanotubes (CNTs) exhibit many advantages in fabricating airflow sensors due to their nanoscale diameters, excellent mechanical and electrical properties, and so on. However, the intrinsic extraordinary properties of CNTs are not fully exhibited in previously reported CNT-based airflow sensors due to the mixed structures of macroscale CNT assemblies. Herein, this article presents suspended CNT networks (SCNTNs) as high-performance airflow sensors, which are self-assembled by ultralong CNTs and short CNTs in a one-step floating catalyst chemical vapor deposition process. The SCNTN-based airflow sensors achieved a record-breaking short response time of 0.021 s, a high sensitivity of 0.0124 s m-1 , a small detection threshold of 0.11 m s-1 , and a wide detection range of ≈0.11-5.51 m s-1 , superior to most of the state-of-the-art airflow sensors. To reveal the sensing mechanism, an acoustic response testing system and a mathematical model are developed. It is found that the airflow-caused intertube stress change resulted in the resistance variation of SCNTNs.

Fast In-Situ Optical Visualization of Carbon Nanotubes Assisted by Smoke.

The fast visualization and manipulation of individual carbon nanotubes (CNTs) has always been a significant technology for their fundamental research. Here, a fast and facile approach is proposed to realize the optical observation of individual suspended CNTs and CNT networks under conventional optical microscopes with the assistance of tar nanodroplets from smoke. The nanodroplets deposited on CNTs render them with strong light scattering to visible light, thus making the CNTs visible under optical microscopes. This visualization method is controllable, environmentally friendly, low-cost, and can be completed in just a few seconds in ambient conditions. Besides, the tar nanodroplets can be easily removed from the CNTs by laser irradiation. More importantly, the smoke sources are widely available and there are no strict requirements for operating conditions. This smoke-assisted visualization method shows great potential in the fundamental research of CNTs.

Superdurable and fire-retardant structural coloration of carbon nanotubes.

Carbon nanotubes (CNTs) are promising candidates for numerous cutting-edge fields because of their excellent properties. However, the inherent black color of CNTs cannot satisfy the aesthetic/fashion requirement, and the flammability of CNTs severely restricts their application in high-temperature environments with oxygen. Here, we realized a structural coloration of CNTs by coating them with amorphous TiO2 layers. By tuning the TiO2 coating thickness, both CNT fibers and membranes exhibited controllable and brilliant colors, which exhibited remarkable superdurability that could endure 2000 cycles of laundering tests and more than 10 months of high-intensity ultraviolet irradiation. The TiO2-coated CNTs exhibited a notable fire-retardant performance and could endure 8 hours of fire burning. The structural coloration of CNTs with excellent fire retardance substantially improves their performance and broadens their applications.

Light-driven directional ion transport for enhanced osmotic energy harvesting.

Light-driven ion (proton) transport is a crucial process both for photosynthesis of green plants and solar energy harvesting of some archaea. Here, we describe use of a TiO2/C3N4 semiconductor heterojunction nanotube membrane to realize similar light-driven directional ion transport performance to that of biological systems. This heterojunction system can be fabricated by two simple deposition steps. Under unilateral illumination, the TiO2/C3N4 heterojunction nanotube membrane can generate a photocurrent of about 9 µA/cm2, corresponding to a pumping stream of ∼5500 ions per second per nanotube. By changing the position of TiO2 and C3N4, a reverse equivalent ionic current can also be realized. Directional transport of photogenerated electrons and holes results in a transmembrane potential, which is the basis of the light-driven ion transport phenomenon. As a proof of concept, we also show that this system can be used for enhanced osmotic energy generation. The artificial light-driven ion transport system proposed here offers a further step forward on the roadmap for development of ionic photoelectric conversion and integration into other applications, for example water desalination.

Bio-inspired structural colors and their applications.

Structural colors, generated by the interaction of interference, diffraction, and scattering between incident light and periodic nanostructured surfaces with features of the same scale with incident visible light wavelengths, have recently attracted intense interest in a wide range of research fields, due to their advantages such as various brilliant colors, long-term stability and environmental friendliness, low energy consumption, and mysterious biological functions. Tremendous effort has been made to design structural colors and considerable progress has been achieved in the past few decades. However, there are still significant challenges and obstacles, such as durability, portability, compatibility, recyclability, mass production of structural-color materials, etc., that need to be solved by rational structural design and novel manufacturing strategies. In this review, we summarize the recent progress of bio-inspired structural colors and their applications. First, we introduce several typical natural structural colors displayed by living organisms from fundamental optical phenomena, including interference, diffraction grating, scattering, photonic crystals effects, the combination of different phenomena, etc. Subsequently, we review recent progress in bio-inspired artificial structural colors generated from advanced micro/nanoscale manufacturing strategies to relevant biomimetic approaches, including self-assembly, template methods, phase conversion, magnetron sputtering, atomic layer deposition, etc. Besides, we also present the current and potential applications of structural colors in various fields, such as displays, anti-counterfeiting, wearable electronics, stealth, printing, etc. Finally, we discuss the challenges and future development directions of structural colors, aiming to push forward the research and applications of structural-color materials.