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The modulations of visual awareness on the processing of emotional faces have been mainly investigated in masking experiments where stimuli were presented for different durations as an integral process to the masking technique. In this visual crowding study, we manipulated the perceivability of a central crowded face (either a fearful or a neutral face) by varying the similarity between the central face and the surrounding flanker stimuli. We presented participants with pairs of visual clutters and recorded their electroencephalography during an emotion judgement task. In an upright flanker condition where both the target face and flanker faces were upright faces (high similarity), participants were less likely to report seeing the central target face, and their P300 was weakened, compared to a scrambled flanker condition where scrambled face images were used as flankers (low similarity). Additionally, at around 120ms post-stimulus, a posterior negativity was found for the upright flanker condition, compared to the scrambled flanker condition, however only for fearful face targets. We concluded that early neural responses seem to be affected by the perceptual characteristics of both target and flanker stimuli whereas neural activity at a later stage is associated with post-perceptual evaluation of the stimuli in this visual crowding paradigm.

Thin-film composite polyesteramide nanofiltration membrane prepared from carboxylated chitosan and trimesoyl chloride was carefully tailored during its fabrication process of interfacial polymerization. The effects of post-curing, mid-drying and the addition of phase transfer catalysts on the membrane properties were studied using single-factor experiment. Post-curing was found to be effective in improving the separation performance of the membrane containing high concentrations of carboxylated chitosan, and the optimal post-curing effect was achieved at a temperature of 40 °C and curing time range of 15−20 min. Mid-drying enhanced the permeation flux and salt rejection of the membrane containing low concentrations of carboxylated chitosan, and the optimum temperature range for the mid-drying process was obtained at 30−40 °C. Furthermore, the membrane properties were susceptible to the addition of phase transfer catalysts, and a uniformly distributed phase transfer catalyst is vital to its influence on membrane properties. The addition of 0.025 wt% benzyltriethylammonium chloride to the aqueous solution exhibited the best effect towards the improvement of the separation performance of the produced membrane.

Strong electron correlation plays an important role in transition-metal and heavy-metal chemistry, magnetic molecules, bond breaking, biradicals, excited states, and many functional materials, but it provides a significant challenge for modern electronic structure theory. The treatment of strongly correlated systems usually requires a multireference method to adequately describe spin densities and near-degeneracy correlation. However, quantitative computation of dynamic correlation with multireference wave functions is often difficult or impractical. Multiconfiguration pair-density functional theory (MC-PDFT) provides a way to blend multiconfiguration wave function theory and density functional theory to quantitatively treat both near-degeneracy correlation and dynamic correlation in strongly correlated systems; it is more affordable than multireference perturbation theory, multireference configuration interaction, or multireference coupled cluster theory and more accurate for many properties than Kohn-Sham density functional theory. This perspective article provides a brief introduction to strongly correlated systems and previously reviewed progress on MC-PDFT followed by a discussion of several recent developments and applications of MC-PDFT and related methods, including localized-active-space MC-PDFT, generalized active-space MC-PDFT, density-matrix-renormalization-group MC-PDFT, hybrid MC-PDFT, multistate MC-PDFT, spin-orbit coupling, analytic gradients, and dipole moments. We also review the more recently introduced multiconfiguration nonclassical-energy functional theory (MC-NEFT), which is like MC-PDFT but allows for other ingredients in the nonclassical-energy functional. We discuss two new kinds of MC-NEFT methods, namely multiconfiguration density coherence functional theory and machine-learned functionals.

Spin-orbit coupling is especially critical for the description of magnetic anisotropy, electron paramagnetic resonance spectroscopy of inorganic radicals and transition-metal complexes, and intersystem crossing. Here, we show how spin-orbit coupling may be included in multiconfiguration pair-density functional theory (MC-PDFT), and we apply the resulting formulation to the calculation of magnetic g tensors (which govern the Zeeman effect) of molecules containing transition metals. MC-PDFT is an efficient method for including static and dynamic electronic correlation in the quantum mechanical treatment of molecules; here, we apply it with spin-orbit coupling by using complete active space self-consistent field (CASSCF) and complete active space configuration interaction (CASCI) wave functions and on-top density functionals. We propose a systematic CASCI scheme for the g tensor calculation of the ground state of the systems under consideration, and we show its superiority over the conventional CASSCF scheme. State interaction, which is important for degenerate and nearly degenerate states, is included by extended multi-state PDFT (XMS-PDFT). Applications are reported for the ground doublet states of 25 transition-metal complexes with d1, d5, d7, and d9 configurations. The MC-PDFT methods are shown to be both efficient and accurate as compared with complete active space second-order perturbation theory.

The respiratory rate (RR) of dairy cows is closely related to heat stress and reproductive diseases. In this study, a method was proposed to detect the RR of standing resting dairy cows by using deep learning and video analysis. First, a Deeplab V3+ semantic segmentation model was developed using the framework of ResNet-101, and the model was trained and tested using 3,000 manually labelled images to realise the segmentation of the cows. Next, a phase-based video magnification (PBVM) algorithm was applied to amplify the weak breathing movements of the cow. Finally, the Lucas-Kanade (LK) optical flow algorithm was used to detect breathing direction, and an RR detection model was used to detect the RR. The effectiveness of the method was verified by testing a total of 67,812 frames of data collected from 70 video segments of different cows. A comparison with the original video LK optical flow method (Method 1), target segmentation video LK optical flow method (Method 2), and original video motion amplification LK optical flow method (Method 3) showed that the accuracy of the proposed method was between 80.00% and 100.00%, and the mean accuracy was 93.04%. These values were 45.82%, 17.72%, and 22.94% higher than those of the other three methods, respectively. In addition, the method was tested in 105 videos of cows under heat stressed, and the RR detection accuracy was 98.69%. The results showed that the method could provide a reference for the automatic detection of RR and remote diagnosis of respiratory-related diseases.

The salt transport in a PEBA membrane used in pervaporative desalination was studied. The concentration profile of salt in the membrane during pervaporation was investigated experimentally using a multilayer membrane. The salt was found to be sorbed in the membrane but was not removed during the pervaporative desalination process, and the salt concentration in the membrane varied linearly with position. High purity water was obtained as the permeate as long as the permeate side was kept dry under vacuum. The accumulated salt uptake in the membrane follows the order of MgCl2 > NaCl > Na2SO4. The solubility of salt in the membrane follows the order of MgCl2 > NaCl > Na2SO4. Both the permeability and diffusivity of salt in the membrane follow the order of NaCl > MgCl2 > Na2SO4. The permeability of salt in the membrane is not influenced by the feed salt concentration. It is mainly determined by the diffusion coefficients.

Thin film composite polyesteramide nanofiltration membranes were fabricated with interfacial polymerization from carboxylated chitosan and trimesoyl chloride on a microporous polysulfone support membrane. Salt rejection was improved by building a multiple-layer structure that was formed by sequentially repeating the cycles of the interfacial reactions. The chemical structure, surface morphology and charge were characterized for the polyesteramide top layer. The effects of the concentrations of the reactant solutions and the number of cycles of reactant depositions and reactions on the separation performance were investigated. The single-layer polyesteramide membrane produced from 3.5 wt% carboxylated chitosan and 0.7 wt% TMC has a negatively charged surface and shows favorable separation performance: pure water permeation flux of 7.3 L/(m2 h) at 0.6 MPa gauge feed pressure, and salt rejection of 95.0% for Na2SO4, 65.7% for MgSO4, 33.2% for MgCl2 and 66.3% for NaCl. The multiple-layer polyesteramide membranes show a more negatively charged surface and higher salt rejection than the single-layer polyesteramide membranes. The single-layer polyesteramide membrane PS-[(C-CS)1.0/TMC] with a relatively loose structure shows good retention for the reactive black 5 anionic dye. This study opens an interesting research area to explore a new type of thin film composite nanofiltration membrane.

Intelligent lameness detection is important for improving cow welfare. A method based on YOLOv3 deep learning algorithm and relative step size characteristic vector is proposed to classify lame and non-lame cows. Videos were decomposed into sequence frames, and leg targets of cows in each frame were detected by YOLOv3 algorithm. Relative step sizes of cow's front and rear legs were calculated based on leg coordinates, and the relative step size characteristic vector was constructed. Finally, a trained Long Short-Term Memory (LSTM) classification model was used to classify lame and non-lame cows based on the characteristic vector. A total of 210 videos were selected for verification using LSTM, support vector machine (SVM), K-Nearest Neighbour (KNN) and decision tree classifier (DTC) algorithms. Results showed that accuracy of lameness detection based on LSTM was 98.57%, which was 2.93%, 3.88%, and 9.25% higher than SVM, KNN, and DTC, respectively. True positive rate of the LSTM was 0.97, which was 0.03, 0.04 and 0.06 higher than SVM, KNN and DTC, respectively. False positive rate of LSTM was 0.03, which was 0.03, 0.06 and 0.11 lower than SVM, KNN and DTC, respectively. A bidirectional LSTM performed slightly better than LSTM but would be more demanding on hardware. Comparison of LSTM with a purely deep learning method showed the latter performed slightly better, but was less conducive to interpretation and diagnosis of lameness. The relative step size characteristic vector proposed was effective for classification of lame and non-lame cows, and could lead to intelligent detection of lameness.

Zero-field splitting (ZFS) is a fundamental molecular property that is especially relevant for single-molecule magnets (SMMs), electron paramagnetic resonance spectra, and quantum computing. Developing a method that can accurately predict ZFS parameters can be very powerful for designing new SMMs. One of the challenges is to include external correlation in an inherently multiconfigurational open-shell species for the accurate prediction of magnetic properties. Previously available methods depend on expensive multireference perturbation theory calculations to include external correlation. In this paper, we present spin-orbit-inclusive multiconfiguration and multistate pair-density functional theory (MC-PDFT) calculations of ZFSs; these calculations have a cost comparable to complete-active-space self-consistent field (CASSCF) theory, but they include correlation external to the active space. We found that combining a multistate formulation of MC-PDFT, namely, compressed-state multistate pair-density functional theory, with orbitals optimized by weighted-state-averaged CASSCF, yields reasonably accurate ZFS results.

In this work, pristine Ti3C2Tx (MXene), as an electrocatalyst, demonstrated potential for use in electrochemical sensing applications, in which the delamination process was found to be a key factor in the electrochemical property. A pair of carbendazim redox peaks with a very low overpotential was recorded at the Ti3C2Tx modified electrode. The computational study suggested that the surface fluorine termination, which resulted from the HF etching process, could effectively trigger the electrocatalytic redox. Therefore, pristine Ti3C2Tx can be considered as an excellent electrode modifier for further electrochemical sensor design.

A Cow’s ruminant behavior can provide information about a cow’s health. Monitoring a cow’s ruminant behavior could therefore be of use when assessing its health. However, it is difficult to simultaneously monitor multiple target cows’ ruminations simultaneously without any contact-type equipment. A method for monitoring multiple target cows’ ruminant behavior based on an optical flow method and an inter-frame difference method is proposed here in. The optical flow method is used to obtain the optical flow fields of the ruminant cows’ mouth areas in the first 60 frames of a video. The optical flow values with large changes are selected from the first 60 frames of the test video and these values are superimposed to obtain the candidate ruminant cows’ mouth areas. The candidate ruminant cows’ mouth areas are magnified 1.5 times, and the inter-frame difference method is used to track the real cows’ mouth areas in the new magnified areas. According to the “center error” and “overlap rate” indexes, the tracking result for the 15 videos is evaluated. The results show that the average successful tracking rate reaches 89.12%, demonstrating that the algorithm could be applied for the automatic monitoring of multiple target ruminant cows’ mouth areas.

Noncovalent intermolecular interactions are very important in many research areas. Therefore, it is vital to understand the extent to which approximate density functionals give a proper description of noncovalent interactions. Previous research has demonstrated that some approximate density functionals can predict usefully accurate interaction energies for many noncovalent systems; however, most of that work is limited to small and moderate-sized molecules. Very recently though, accurate benchmarks have become available for some very large molecules. The present work applies 21 approximate density functionals to compute the binding energies of seven large molecular systems that have a number of atoms ranging from 200 to 910. The results are judged by comparison to the recently published CIM-DLPNO-CCSD(T) results, which are assumed to provide a reliable benchmark. The five most accurate methods among those tested are found to be PW6B95-D4, PW6B95-D3(BJ), revM11, M06-L, and MN15.

The concentration of PM2.5 is closely related to air, environmental quality and human health. In this study, an hourly prediction method of PM2.5 concentration in Beijing based on Bi-LSTM neural network was proposed. Firstly, Python crawlers were used to crawl hourly PM2.5 concentration and meteorological information for data fusion, and then Bi-LSTM prediction model was constructed to realize the prediction of PM2.5 concentration. To verify the validity of the proposed method, 2628 h of test data from the test set were used for testing and compared with two different cyclic neural network prediction models based on GRU and Bi-LSTM. At the same time, the validity of introducing meteorological features into the method was discussed. The test results show that the MAE of this research method for PM2.5 concentration prediction was 14.15, which was lower than the other two methods 49.91 and 31.51; the RMSE was 17.20, which was lower than the two methods 60.03 and 36.70; The R2 was 0.95, which was 0.10 and 0.03 higher than the two comparison methods respectively. Moreover, the method introduced meteorological features, which effectively reduced the prediction error of PM2.5 concentration. The results showed that it was feasible to use this method to predict hourly PM2.5 concentration, and it could provide some reference for air quality assessment and environmental protection.

Polyelectrolyte composite nanofiltration membranes were prepared using layer-by-layer self-assembly of chitosan and poly(acrylic acid) on a microporous polyethersulfone substrate. Membranes with a layer-by-layer structure were formed by sequential depositions of cationic chitosan and anionic poly(acrylic acid) alternately. The impacts of parameters involved in the membrane preparation process on the separation performance of the resulting membrane were investigated. The membranes showed good separation performance with an over 90% rejections to sulfate salts (i.e., MgSO4 and Na2SO4) and nearly 60% rejections to chloride salts (i.e., MgCl2 and NaCl). In spite of commonly observed trade-off relationship between salt rejection and water permeability, the membrane fabrication conditions can be tailored to enhance the overall performance of the nanofiltration membranes. The LbL self-assembled membranes may not afford a long-term use for nanofiltration of aqueous solutions due to extensive membrane swelling over a prolonged period of operation, and thermal and chemical post-treatments (i.e., heat treatment and chemical crosslinking) can be used to improve the membrane stability.

Improving the hydrophilicity of titania nanotubular arrays (TNAs) could promote their interfacial reaction rate when they interact under aqueous conditions. In this work, we proposed a simple hydrothermal method for Si and Sr doping. A significant hydrophilicity improvement could be observed in TNAs after the doping process. Therefore, Si@Sr/TNAs exhibited a superior urease loading and showed excellent chronocoulometric performance towards urea interaction.

Desalination of high salinity water by pervaporation using hydrophilic poly(ether-block-amide) membranes was investigated. A flux of 1680 g/(m2 h) and almost complete salt rejection (>99.9%) were achieved at 65 °C. Increasing salt concentration from 1 to 20 wt% resulted in a 50% reduction in water flux, whereas the salt rejection was not influenced. The salt rejection was not influenced by the salt type (i.e., NaCl, MgCl2 or Na2SO4) either. With an increase in temperature, the water flux through the membrane increased in spite of a decrease in the water permeability coefficient. The temperature dependence of water flux obeyed an Arrhenius type of relationship. Batch operation over a period of 10 h showed that the water flux decline could be recovered by washing the membrane with deionized water and there was no irreversible fouling during the pervaporative desalination process.

Low volumetric energy density is a bottleneck for the application of lithium-sulfur (Li-S) battery. The low-density sulfur cooperated with the light-weight carbon substrate realizes electrochemical cycle stability, but leads to worse volumetric energy density. Here, nickel ferrite (NiFe2O4) nanofibers as novel substrate for sulfur not only anchor lithium polysulfides to enhance the cycle stability of sulfur cathode, but also contribute to the high volumetric capacity of the S/nickel ferrite composite. Specifically, the S/nickel ferrite composite presents an initial volumetric capacity of 1,281.7 mA h cm−3-composite at 0.1 C rate, 1.9 times higher than that of S/carbon nanotubes, due to the high tap density of the S/nickel ferrite composite.

A series of novel blend ultrafiltration (UF) membranes have been successfully prepared from commercial poly (ether sulfone), lab-synthesized sulfonated poly (aryl ether sulfone) (SPAES, 1 wt%) and copper nanoparticles (0 ∼ 0.4 wt%) via immersion precipitation phase conversion. The micro-structure and separation performance of the membranes were characterized by field emission scanning electron microscopy (SEM) and cross-flow filtration experiments, respectively. Sodium alginate, bovine serum albumin and humic acid were chosen as model organic foulants to investigate the antifouling properties, while E. coil was used to evaluate the antibacterial property of the fabricated membranes. By the incorporation with SPAES and copper nanoparticles, the hydrophilicity, antifouling and antibacterial properties of the modified UF membranes have been profoundly improved. At a copper nanoparticles content of 0.4 wt%, the PES/SPAES/nCu(0.4) membrane exhibited a high pure water flux of 193.0 kg/m2 h, reaching the smallest contact angle of 52°, highest flux recovery ratio of 79% and largest antibacterial rate of 78.9%. Furthermore, the stability of copper nanoparticles inside the membrane matrix was also considerably enhanced, the copper nanoparticles were less than 0.08 mg/L in the effluent during the whole operation.

We propose K1–xMo3P2O14 as a promising support for single-atom catalysts. A novel single-atom catalyst, Pt/K1–xMo3P2O14, was then designed for CO oxidation and its catalytic activity was investigated by means of first-principles computations. The agglomeration of Pt atoms is effectively inhibited by K1–xMo3P2O14. Eley–Rideal (ER), Langmuir–Hinshelwood (LH), and oxygen vacancy mechanism were considered for CO oxidation, and the results indicate that Pt/K1–xMo3P2O14 exhibits ultrahigh activity with a low energy barrier of 0.49 eV for low-temperature CO oxidation via LH mechanism. The computations would shed new light on the design of novel single-atom catalysts and widen the applications of mixed-valent molybdenum monophosphates.

Searching and designing two-dimensional (2D) materials with unique topological structures and physical and chemical properties is extremely significant in the field of materials science. Here we report the design of 2D MnBx (x = 1, 2, 3, 6) monolayers containing quasi-planar hypercoordinate motifs by means of density functional theory (DFT) computations and particle swarm optimization technique. These systems exhibit high cohesive energy as well as good kinetic and thermal stability, indicating that the MnBx monolayers can be prepared in experiments. Bond order analyses suggest that the abundance of multicenter bonds contributes to the stability of MnBx monolayers. Among them, MnB3 monolayer has nonmagnetic ground state while others exhibit ferromagnetic metallic properties. Moreover, MnB6 monolayer exhibits high spin polarization and negative Poisson’s ratio, which endows MnB6 with many special applications to cushioning and nanoelectronics.

Rechargeable Li-CO2 batteries represent a novel approach towards clean recycling and utilization of CO2. Nevertheless, there are still many obstacles to be overcome. Here we report the first introduction of an electrolyte redox mediator (LiBr) to rechargeable Li-CO2 batteries, and the electrochemical performances were significantly improved, especially cyclic stability and rate capability. The proposed mechanism is that LiBr is beneficial for the formation of desired morphology of discharge products; moreover, the electrochemically generated Br2 promotes the decomposition of Li2CO3 chemically during the charging process.

Wet scrubbing of NOx was accomplished by hexamminecobalt(II) solution in a laboratory setting, and the spent absorbent was regenerated by thermal decomposition and acid treatment. The spent absorbent contained precipitates (cobalt nitrosyl) and soluble cobalt in the solution. Thermogravimetric analysis (TGA) showed that the spent sorbent could be decomposed to CoO at temperatures above 900 °C. Caution needed to be exercised to ensure that the samples were not exposed to oxygen when cooling down to ambient temperature in order to prevent oxidation of CoO to Co3O4. The regenerated cobalt(II) oxide was dissolved in HCl for preparation of hexamminecobalt(II) solutions, and such a regenerated cobalt-based sorbent for NO removal performed at the same level as the fresh one. This study suggests a practical method for the regeneration of a cobalt-based absorbent involved in NO absorption.

Photocatalytic reduction of carbon dioxide (CO2) into hydrocarbons could promote the CO2 utilization and retard the greenhouse effect, which has gained much attention. Due to high surface–bulk ratio, two-dimensional materials can be promising candidates for catalysis. In this study, by means of first-principles computations, we have investigated the reduction of CO2 at the oxygen vacancy on MXene monolayers. Among Ti2CO2, V2CO2 and Ti3C2O2, Ti2CO2 has exhibited the best catalytic performance for the reduction of CO2. The reaction pathway CO2 → HCOO → HCOOH was found to be favorable with an energy barrier of 0.53 eV. The energy barriers of the reaction pathways for other single-carbon organic products were much higher, indicating high selectivity for HCOOH. Moreover, we have proposed that CO and H2 can introduce sufficient oxygen vacancies on O-terminated MXene. This study provides new insights into the design of catalysts for the reduction of CO2 and further widens the applications of MXene-based materials.

Here we propose a series of novel two-dimensional tetragonal-structured metal nitride (t-MN, M = Ti, Zr or Hf) monolayers mainly bonded with strong hybridization of N-p and M-d orbitals. These monolayers exhibit metallic properties which make them promising candidates as electrode materials for Li-ion batteries. The computations disclosed that t-TiN has a high theoretical Li-storage capacity of 432 mA h g−1 and a low Li ion diffusion barrier of 0.25 eV. After halogenation, the t-MNX (X = Cl, Br and I) monolayers transform to semiconductors with high carrier mobility and direct band gaps ranging from 0.41 to 3.26 eV. Moreover, the 2D nature and strong light absorption endow them with wide applications to photocatalysis. The appropriate band edge position indicates that t-ZrNX and t-HfNX can be applied as 2D photohydrolytic catalysts. Finally, a top-down method followed by high-temperature treatment is proposed to prepare t-MN monolayers.

By first-principles computations, we propose Na2Mn3O7 as a high-rate cathode material for sodium ion batteries. Na2Mn3O7 has a voltage window of 3.6–3.1 V and a theoretical reversible capacity of 124 mA h g−1. Na2Mn3O7 is a semiconductor and turns metallic after Na extraction; moreover, the calculated Na migration barrier in Na2Mn3O7 is 0.18 eV, ensuring ideal conductivity and rate capability.

Photocatalytic water splitting is a promising route to achieve renewable hydrogen production. In this work, a series of A 5 B 4 O 15 (A=Ca, Sr, Ba; B=V, Nb, Ta) layered perovskite materials, were investigated through density functional theory computations. We revealed that changing B ions can modify the band gap, and A-ion substitution can tune the band-edge position. Therefore, through A/B ion combination, we can realize the band engineering of A 5 B 4 O 15 materials. Electron/hole effective mass and water adsorption of A 5 B 4 O 15 materials were also explored. Further investigations suggest that Ca 5 V 4 O 15 nanosheet, with an appropriate band gap and an optimal band-edge position, would be a promising photocatalyst for visible-light water splitting.

The retention behavior of cobalt(II) by nanofiltration from ammoniacal solutions used for the simultaneous absorption of SO 2 /NO x was investigated. The cobalt(II) rejections in aqueous ammoniacal solutions were higher than 99% by the membrane with alkaline stability and a molecular weight cut-off of ~200 Da. The presence of small and mobile NH 4 + in the feed solution improved the cobalt(II) retention. It was found that NH 4 NO 3 had little effect on the permeation flux or cobalt(II) rejection while (NH 4 ) 2 SO 4 lowered both. The effects of initial cobalt(II) concentration (0.006–0.03 mol/L) in the feed on cobalt(II) rejection depended on the compositions of the solution. An increase in transmembrane pressure (0.4–0.8 MPa) led to higher cobalt(II) rejections for all solutions. Cobalt precipitates were generated during the filtration process due to the loss of chelating agent NH 3 ·H 2 O. The decline in permeation flux and cobalt(II) rejection could be recovered by effective cleaning. Adding NH 3 ·H 2 O during the filtration process could prevent the formation of cobalt precipitates so as to avoid deterioration in the membrane performance.

First-principles computations were performed to investigate the catalytic oxidation of CO on a Ti-anchored Ti2CO2 monolayer, a typical MXene. The Ti2CO2 monolayer could prevent the formation of Ti clusters. Both Langmuir–Hinshelwood (LH) and Eley–Rideal (ER) mechanisms were considered, and the results manifest that the Ti-anchored Ti2CO2 monolayer exhibits very high activity even comparable to many noble metal catalysts for low-temperature CO oxidation. This work contributes to designing more effective and nonprecious-metal single-atom catalysts and widening the applications of MXene-based materials.

This study deals with the removal of heavy metals (i.e., Co (II), Cu (II), Ni (II), Pb (II), Fe (III), Cd (II), Zn (II) and Mn (II)) from water with polymer-enhanced ultrafiltration using polyvinylamine (PVAm) as a complexing agent. At a PVAm dosage of 0.1 wt%, metal rejections as high as over 99% were achieved for Pb (II), Cu (II) and Fe (III). The coordination interactions between PVAm and the heavy metals were investigated using UV–vis spectral and conductometric analyses. It was shown that the performance of PEUF for metal rejection was related to the coordination interactions between PVAm and the heavy metals. It was also found that when divalent sulfate anions were present, the PVAm–metal complexes precipitated out of the solution at high metal concentrations. Thus PVAm could also be used as a flocculant for metal removal by flocculation.

First-principles computations based on density functional theory (DFT) were performed to investigate the performance of bulk and monolayer V2O5 as the cathode material for Li-, Na-, K- and Mg-ion batteries. Both the average voltage and ion migration barrier were studied. The results indicate that alkali metal ions with a large ionic radius (such as Na and K) have much lower migration barriers (0.44 and 0.39 eV for Na and K, respectively) on monolayer V2O5 than in bulk V2O5 (1.17 and 1.66 eV) without great voltage loss, while for Li polymorphs, the difference between monolayer and bulk V2O5 is minimal. However, the performance of monolayer V2O5 is not ideal enough as the cathode material for multivalent metal-ion (such as Mg) batteries. As a result, for Na- and K-ion batteries with a large ionic size, monolayer V2O5 is an attractive cathode material.

Sustainable and resourceful organic materials are of long-standing interest for lithium-ion batteries. However, the lack of structural stability and cyclic capability is still the bottleneck for their practical use. Here, two new chemically stable naphthalimide-based crystalline covalent organic frameworks (COFs) were first synthesized through a simple solvothermal route. Excellent electrochemical performances were achieved as cathode materials for Li-ion batteries, mainly due to their open ordered nanoporous framework and robust structure. Moreover, their electrochemical performance can be improved by simply altering the linker of COFs at the molecular level. This work provides a possible approach to obtain the desired performance by structural modulation of organic materials.

Accurate and rapid recognition of dairy cow’s motion behaviours is the key to intelligent perception of its health state. To achieve the recognition of cows’ lying, standing, walking, drinking and feeding behaviours, first, based on the advantage of the efficient feature extraction of EfficientNet, the spatial feature extraction of cow’s video frames was realized. Then, to fully extract the characteristics of different behaviour information of dairy cows, the BiFPN (bidirectional feature pyramid network) was used to realize the efficient fusion of characteristics in the 3–5 layers of EfficientNet. Finally, the behaviour information was sent to the BiLSTM (bidirectional long short-term memory) module, which integrates the attention mechanism to realize the aggregation of video frames in a time series, thus realizing fast and accurate recognition of dairy cow’s motion behaviours. 1009 videos containing 2,270,250 frames of dairy cows in different scenes and postures were collected and tested to compare the performance of the proposed algorithm, four state-of-the-art behaviour recognition algorithms: C3D, VGG16-LSTM, ResNet50-LSTM, and DensNet169-LSTM were carried out. Meanwhile, the precision, recall, accuracy, recall and the average number of frames recognized per second (ANFR) were used to evaluate the performance of the algorithm. Experimental results showed that the behaviour recognition accuracy of the algorithm was 97.87%, which was 4.25% higher than that of the classical ResNet50-LSTM, and the ANFR was 134f/s. In addition, the study was combined with a sliding window to realize the behaviour recognition of undivided single-target dairy cow videos, and the final recognition accuracy could reach 95.20%, showing that the proposed algorithm was effective and could be used for the health status perception and disease prevention of dairy cows.

Rapid detection of vanillin in food samples is important in food safety. In this work, lamellar C3N4 sheets were prepared by simple pyrolysis of melamine. The as-prepared C3N4 sheets were used to fabricate a vanillin electrochemical sensor. C3N4 was also successfully used to modify glassy carbon electrode and exhibited excellent sensing performance. Under the optimized condition, the prepared electrochemical sensor could linearly detect vanillin between 20 nM to 10 μM and 15 μM–200 μM with a low detection limit of 4 nM. In addition, the vanillin sensor was applied to detect vanillin in milk tea and biscuits.

The era of big data has brought shocks to education, and it has also put forward new requirements and expectations for education innovation. Based on the development requirements of comprehensive education reform, innovative talent training, and optimization of education and teaching methods in the era of big data, it is extremely necessary to carry out educational innovation. In the era of big data, education innovation has also shown a certain development trend. We should fully understand the necessity of education innovation in the era of big data, grasp the development trend of education innovation in the era of big data, make full use of big data thinking, methods and techniques, and start from the aspects of education decision-making, education and teaching reform, and the construction of big data education service platform to find a new path for educational innovation in the new era.