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A number of studies have focused on the effects of rare earth elements (REEs) on crop plants, while little attention has been paid on how tolerant plant species respond to increasing mixed REE concentrations. In this study, ramie (Boehmeria nivea L.) was exposed to a series of REE concentrations prepared with equimolar mixtures of 16 REEs (i.e. 0, 1.6, 8, 16, 80, 160, 400, 800 μmol/L) in order to explore REE accumulation and fractionation characteristics in ramie and the responses of this plant to mixed REEs. Results show that ramie root and shoot biomasses are unaffected under lower REE concentrations (1.6–80 μmol/L), while the growth of ramie and the uptake of nutrients especially Ca and Mn are largely inhibited under higher REE concentrations (160–800 μmol/L). The P and Mo concentrations in the roots increase with the increasing REE concentrations in the solution, suggestive of an involvement of P and Mo in dealing with the high concentrations of REEs in this plant. The preferential uptake of Ce and heavy REEs (HREEs) and the preferential transport of HREEs within the plant lead to a positive Ce anomaly and a HREE enrichment in ramie leaves. Our study suggests that ramie could be a good candidate for the phytoremediation of heavily REE-contaminated soils (e.g., REE mine tailings in southern China). Our results also shed light on points of taking into account phytoremediation management strategies of REE-contaminated soils (e.g., P and Mo fertilization).

The greatest problem in conventional Fenton reaction is the slow production of ROS (reactive oxygen species) because of the inefficient Fe3+/Fe2+ conversion. Based on the extraordinary photo-response property of two-dimensional molybdenite (2DM), photogenerated electrons can be easy separated to accelerate the regeneration of Fe2+. In this work, Fe2+-anchored 2DM (2DM-Fe) was prepared and used as a heterogeneous Fenton catalyst to investigate the degradation efficiency to Methylene Blue (MB) in the presence of light. According to experimental results, 2DM-Fe exhibited extraordinary catalytic activity in MB elimination, which ascribed to the synergetic effect of photogenerated carriers and anchored Fe2+ to H2O2 activation. In addition, 2DM-Fe showed nearly 100% degradation efficiency to MB within 5 cycles with slight leaching amount of Mo and Fe ions, implying the strong stability and reusability in H2O2 system. Furthermore, the influences of H2O2 and 2DM-Fe dosages, pH values as well as the degradation efficiency to different dyes were also investigated. According to quenching experiments and EPR (electron paramagnetic resonance) test, the degradation mechanism of MB mainly ascribed to the oxidation of HO• and •O2−. This finding provides a novel strategy to design rational Fenton catalyst and has great significance to water remediation in the future.

We have developed an efficient strategy to synthesize an active and durable electrocatalyst of Pd hydride nanocubes (NCs). Instead of the traditional chemical method, the PdH0.43 NCs are firstly prepared via a hydrogen diffusion procedure, followed by hydrothermal synthesis of an amorphous CuO layer encapsulating the PdH0.43 NCs to prevent the hydrogen atoms from escaping. Obvious lattice expansion is demonstrated playing a pivotal role in the enhancement of their oxygen reduction reaction (ORR) activity and durability. The obtained PdH0.43@CuO NCs catalysts exhibit an ORR mass activity of 0.18 A mg−1 at 0.90 V versus reversible hydrogen electrode in an alkaline medium, which is about five times higher than that of commercial Pt/C. Accelerated durability tests show that there is only a 32 mV decay in half-wave potential even after 10,000 potential cycles, indicating the excellent stability of PdH0.43@CuO NCs. Density functional theory (DFT) calculations also indicate that, compared to Pd, PdH0.43 has a lower limiting barrier to form OH− during the ORR process. The present study illustrates the importance of lattice expansion caused by hydrogen and offers an available strategy to design highly efficient and durable Pd-based electrocatalysts for alkaline fuel cells.

Potassium-ion batteries (KIBs) have become the most promising alternative to lithium-ion batteries for large-scale energy storage system due to their abundance and low cost. However, previous reports focused on the intercalation-type cathode materials usually showed an inferior capacity, together with a poor cyclic life caused by the repetitive intercalation of large-size K-ions, which hinders their practical application. Here, we combine the strategies of carbon coating, template etching and hydrothermal selenization to prepare yolk-shelled FeSe2@N-doped carbon nanoboxes (FeSe2@C NBs), where the inner highly-crystalline FeSe2 clusters are completely surrounded by the self-supported carbon shell. The integrated and highly conductive carbon shell not only provides a fast electron/ion diffusion channel, but also prevents the agglomeration of FeSe2 clusters. When evaluated as a conversion-type cathode material for KIBs, the FeSe2@C NBs electrode delivers a relatively high specific capacity of 257 mAh/g at 100 mA/g and potential platform of about 1.6 V, which endow a high energy density of about 411 Wh/kg. Most importantly, by designing a robust host with large internal void space to accommodate the volumetric variation of the inner FeSe2 clusters, the battery based on FeSe2@C NBs exhibits ultra-long cycle stability. Specifically, even after 700 cycles at 100 mA/g, a capacity of 221 mAh/g along with an average fading rate of only 0.02% can be retained, which achieves the optimal balance of high specific capacity and long-cycle stability.

Heterostructures show great potential in energy storage due to their multipurpose structures and function. Recently, two-dimensional (2D) graphene has been widely regarded as an excellent substrate for active materials due to its large specific surface area and superior electrical conductivity. However, it is prone to self-aggregation during charging and discharging, which limits its electrochemical performance. To address the graphene agglomeration problem, we interspersed polypyrrole carbon nanotubes between the graphene cavities and designed three-dimensional (3D)-heterostructures of ZnMn2O4@rGO-polypyrrole carbon nanotubes (ZMO@G-PNTs), which demonstrated a high rate and cyclic stability in lithium-ion capacitors (LICs). Furthermore, the 3D porous structure provided more surface capacity contribution than 2D graphene, ultimately resulting in a better stability (333 mAh g-1 after 1000 cycles at 1 A g-1) and high rate capacity (208 mAh g-1 at 5 A g-1). Also, the mechanism of performance difference between ZMO@G-PNTs and ZMO@G was investigated in detail. Moreover, LICs built from ZMO@G-PNTs as an anode and activated carbon as a cathode showed an energy density of 149.3 Wh kg-1 and a power density of 15 kW kg-1 and cycling stability with a capacity retention of 61.5% after 9000 cycles.

Cold energy storage technologies with phase change materials (PCMs) have received increasing attention, but there is still a great vacancy for PCMs that can be applied below 0°C due to high supercooling degree of aqueous solution. In this work, the nanosheet dispersion phase change materials (NDPCMs) containing the ethylene glycol (EG) aqueous solution and the modified montmorillonite (MTT) at different mass fractions were prepared, in which the modified MTT prepared by surface modification of zwitterionic surfactant and polymer quaternary ammonium salt under high speed shearing could be steadily dispersed due to the combination of charge and steric effects. And the modified MTT could be used as the nanosheet nucleating agent to effectively suppress the supercooling as well as improve the thermal properties of the PCM. The NDPCM with 0.4 wt% modified MTT had an appropriate phase transition temperature of -5.0°C, higher latent heat of fusion of 189.96 kJ•kg−1, and there was no supercooling in the test of step cooling curves. Moreover, the NDPCM still showed excellent dispersion stability after 50 thermal cycles. These results indicate that the NDPCM has practical application value in cold energy storage.

Wear-related energy and material loss cost over 2500 Billion Euro per year. Traditional wisdom suggests that high-strength materials reveal low wear rates, yet, their plastic deformation mechanisms also influence their wear performance. High strength and homogeneous deformation behavior, which allow accommodating plastic strain without cracking or localized brittle fracture, are crucial for developing wear-resistant metals. Here, we present an approach to achieve superior wear resistance via in-situ formation of a strong and deformable oxide nanocomposite surface during wear, by reaction of the metal surface with its oxidative environment, a principle that we refer to as ‘reactive wear protection’. We design a TiNbZr-Ag alloy that forms an amorphous-crystalline oxidic nanocomposite surface layer upon dry sliding. The strong (2.4 GPa yield strength) and deformable (homogeneous deformation to 20% strain) nanocomposite surface reduces the wear rate of the TiNbZr-Ag alloy by an order of magnitude. The reactive wear protection strategy offers a pathway for designing ultra-wear resistant alloys, where otherwise brittle oxides are turned to be strong and deformable for improving wear resistance., Wear-resistant metals have long been a pursuit of reducing wear-related energy and material loss. Here the authors present the ‘reactive wear protection’ strategy via friction-induced in situ formation of strong and deformable oxide nanocomposites on a surface.

Dual-phase metallic glasses (DP-MGs), a special member of the MGs family, often reveal unusual strength and ductility, yet, their corrosion behaviors are not understood. Here, we developed a nanostructured Mg57Zn36Ca7 (at.%) DP-MG and uncovered its corrosion mechanism in simulated body fluid (SBF) at the near-atomic scale utilizing transmission electron microscope (TEM) and atom probe tomography (APT). The 10-nm-wide Ca-rich amorphous phases allow oxygen propagation into the DP-MG, resulting in a micrometer thick hydroxides/oxides layer. This dense corrosion layer protects the DP-MG from further corrosion, enabling a corrosion rate that is 77% lower than that of Mg (99.99% purity).

Anchor mechanism is widely applied in scene text detection methods and demonstrates promising performance. However, existing anchor mechanisms have two major limitations, namely handcrafted anchor design and hard-wired anchor assignment. We propose a novel Grouped Channels Composition(GCC) block to achieve the data-driven anchor design and adaptive anchor assignment. To be more specific, our GCC block uses optimizable anchor functions rather than handcrafted ones to achieve data-drive anchor design. In our GCC block, an adaptive anchor assignment is achieved with the attention mechanism instead of empirically assigning anchor according to the Intersection Over Union (IoU) between ground truth and targets. We then build a corresponding network named GCCNet with our GCC blocks. We also propose a Unified Loss Weighting module to alleviate the inconsistency between classification score and localization accuracy. Experiments conducted on publicly available datasets demonstrate the state-of-the-art performance of our methods.

Oxygen evolution and oxygen reduction are considered as essential processes in the energy conversion devices. The progress of cost-effective bifunctional catalysts has become a critical issue to be solved. Here, we report that Co(OH)2 @N-doped carbon (NC) was facilely synthesized through the impregnation strategy of metal-organic frameworks derived carbon and cobalt ions. N-doped carbon with porous structure and cobalt hydroxide nanosheets play a synergistic effect role, representing excellent catalytic performance toward oxygen evolution and reduction reactions. The obtained Co(OH)2@NC exhibits remarkable activity in terms of a lower overpotential of 330 mV@10 mA cm−2 for OER and a more positive half-wave potential (E1/2 = 0.84 V) for ORR in alkaline medium, outperforming IrO2 and Pt/C. Due to its superior bifunctional catalytic performance, Co(OH)2@NC catalyst is applied into a promising air electrode of Zn-air battery. This presented strategy of impregnation synthesis in this work provides a new design direction for practical electrochemical energy devices.

Low cycle fatigue behaviors of SA508-3 low-alloy steel were investigated in room-temperature air, high-temperature air and in light water reactor (LWR) water environments. The fatigue mean curve and design curve for the low-alloy steel are developed based on the fatigue data in room-temperature and high-temperature air. The environmental fatigue model for low-alloy steel is developed by the environmental fatigue correction factor (Fen) methodology based on the fatigue data in LWR water environments with the consideration of effects of strain rate, temperature, and dissolved oxygen concentration on the fatigue life.

To enhance drug utilization and reduce their side effects, the strategy of “tumor-triggered targeting” was introduced to fabricate dual-pH-sensitive chitosan (CHI)/mesoporous silica nanoparticle (MSN)-based anticancer drug delivery system (DDS) in this work. Model drug doxorubicin hydrochloride (DOX) was loaded in MSN, which was modified with benzimidazole (Bz) group. Then chitosan-graft-β-cyclodextrin (CHI-g-CD) was applied as the “gatekeeper” to cover MSN through host-guest interaction between β-CD and Bz. After being coated with targeting peptide adamantane-glycine-arginine-glycine-aspartic acid-serine (Ad-GRGDS), methoxy poly(ethylene glycol) benzaldehyde (mPEG-CHO) was finally grafted on CHI through the pH-sensitive benzoic imine bond. Due to the dynamic protection of PEG, the obtained carriers were “stealthy” at pH 7.4, but could reveal the shielded targeting peptide and the positive charge of CHI in the weakly acidic environment achieved a “tumor-triggered targeting”. Inside cancer cells, the interaction between β-CD and Bz group could be destroyed due to the lower pH, resulted in DOX release. Both in vitro and in vivo studies proved the DDS could targeting induce cancer cell apoptosis, inhibit tumor growth, and reduce the cytotoxicity of DOX against normal cells. It is expected that the system named DOX@MSN-CHI-RGD-PEG could be a potential choice for cancer therapy.

The damage evolution process in 316 austenitic stainless steel at the plastic stage were investigated in this paper by using the in-situ EBSD technique and MD simulation in combination. The damage mechanisms of 316 steel are typified by an intergranular and intragranular mixed mode. The in-situ EBSD results showed that the intergranular damage was the defects formation at the GBs, and the twin-GB intersections and the triple GB are the preferred damage sites. While the intragranular damage was manifested by the mixing of lattice orientation change, intragranular flaws appearance, and twin degradation; The MD simulation results manifested that the initial lattice structure changed after the plastic strain, and the continues increase of the atom displacement resulted in the defect formation. The simulation results also verified that the twin-GB intersection site could be a fragile location, the void preferentially appeared here, and the disorder degree of atoms over here were great. In addition, the defects also generated at the triple GB, which indicated that the triple GB could also be a weak location.

Existing software intelligent defect classification approaches don’t consider radar characters and prior statistics information. Thus when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15%~20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defecs effectively to improve the identifying adequacy of the defects in radar software.

Detecting incidentally captured text in the wild remains an open problem due to challenging factors including unconstrained scenarios and large scale variation. In this paper, we establish a large-scale scene text detection dataset (LS-Text), containing 36, 000 images and 270, 783 text instances with various scales and complex scenarios, to promote the research of text detection. We propose a Scale-residual Learning Network (SLN) to deal with the scale variation problem in a progressive optimization manner. Specifically, we integrate both learnable feature concatenation and feature up-sampling operator. It can effectively eliminate the residuals between the outputs of SLN and ground-truth text instances by processing both the Feature Fusion Residuals (FFR) and the Scale Transformation Residuals (STR), simultaneously. By stacking multi-scale feature maps in a deep-to-shallow manner, SLN continuously optimizes feature representation by accumulating strong semantic information and rich texture details in a scale-residual learning way. Extensive experimental results on five challenging datasets demonstrate the state-of-the-art performance of the proposed SLN model, and the challenging aspects related to real-world scenarios of the proposed LS-Text dataset. Both the source code of SLN and the LS-Text dataset are available at https://github.com/SLN-Text-Detection .

Hydrolytic dehydrogenation of Ammonia-borane (NH3BH3, abbreviation AB) has attracted much attention due to its high hydrogen content and stability. Therefore, the design and construction of low cost and high performance catalysts for AB hydrolysis are of great significance. In this work, a series of RhCu/CoOx alloy catalysts were prepared by a in-situ reduction method. The catalytic activity of hydrolytic dehydrogenation of AB was optimized by regulating the molar ratio of Rh to Cu in the RhCu/CoOx catalysts. The results verify that Rh1Cu2/CoOx has the best catalytic activity for hydrolytic dehydrogenation of AB, and the corresponding hydrolysis reaction type was first order. The turnover frequency (TOF) reached 670.95 mol H 2 mol cat − 1 min−1, and the apparent activation energy (Ea) was 28.86 kJ mol−1, showing excellent catalytic performance for hydrolytic dehydrogenation of AB.

Quantum dots (QDs)/gel composites have sparked tremendous interests due to their great potentials in sensors. However, in most cases, these sensors are focused on single signal detection, suffers from a key hurdle of low signal density. Thus, it is highly desirable to develop multi-signal QDs/gel-based sensors. In this work, we constructed dual-signal QDs/gel assemblies by a microfluidic-assisted method, which shows promising application in dual-signal sensors. Typically, self-healing QDs/gel beads with different fluorescent signals were fabricated via microfluidic techniques. In virtue of the microfluidic-assisted self-assembly, various dual-signal assemblies were constructed controllably in the confined channels, where self-healing QDs/gel beads serve as building blocks. As expected, the assemblies exhibit rapid dual-signal detection capability toward organic amines and pH values. In addition, by using self-healing gels as printable materials, the combination of the self-assembly strategy with 3D microfluidic printing allows the generation of diverse responsive dual-signal patterns and structures. The self-assembly of self-healing building blocks not only offers a promising route toward fluorescent dual-signal architectures for advanced sensors, but will also guide the development of 3D printing for controllable fabrication of functional assemblies.

In this work, a highly stretchable and sensitive strain sensor using silver nanowires (Ag NWs)/multi-walled carbon nanotubes (MWCNTs) on hair band is proposed. The fabrication process of such sensors enabled scalable and facile production with low cost by dipping hair band into Ag NWs/MWCNTs composites suspension. The sensing performance of the sensors was optimized by tuning the Ag NWs/MWCNTs mass ratio. The hair band based strain sensor with Ag NWs/MWCNTs mass ratio of 30 exhibited the best performance with high sensitivity of 416.0 and good stretchability up to 70%. Moreover, great repeatability and long-term stability could be achieved over 7500 cycles of stretch-release. This Ag NWs/MWCNTs-hair band sensor can be used for monitoring human motions and swallowing action.

We report the design and simple spray coating fabrication process of a perfect absorber based on carbon nanotubes (CNTs) and carbon black particles with omnidirectional high absorption efficiency beyond 99.9% over the broad wavelength range from 400 nm to 20 μm. The carbon black particles are introduced into the CNT solution to form multi-scale all-carbon-based nanomaterials, effectively modifying the morphology of the sprayed layer to improve the light absorption. The promising absorption characteristics of the CNT–carbon black layer are attributed to not only the intrinsic optical properties of the carbon nanomaterials but also the unique surface nanostructures that efficiently trap light. In addition to serving as absorbers in the coating layer, the CNTs, more importantly, act as connectors between carbon black particles for absorber preparation at a large scale, which is the key to modifying the surface morphology. The all-carbon-based coating layer is also shown to be effective for solar thermal harvesting and self-cleaning function. The developed mixing method for CNTs and carbon black particles in solutions opens a new path for the realization of multi-functional sprayed layers based on CNTs and advances the technology of CNT coatings for scale-up to industry applications.

The development of efficient strategies to recycle lithium-ion battery (LIB) electrode materials is an important yet challenging goal for the sustainable management of battery waste. This work reports a facile and economically efficient method to convert spent cathode material, LiFePO4, into a high-performance NiFe oxy/hydroxide catalyst for the oxygen evolution reaction (OER). Herein, Ni-LiFePO4 is synthesized via the wetness impregnation method and further evolves into defect-rich NiFe oxy/hydroxide nanosheets during the OER. The introduction of the Ni promoter together with in situ evolution strengthens the electronic interactions among the metal sites and creates an abundance of defects. Experimentally, the evolved Ni-LiFePO4 delivers a low overpotential of 285 mV at 10 mA cm−2 and a small Tafel slope of 45 mV dec−1, outperforming pristine LiFePO4 and is even superior to the benchmark catalyst RuO2. Density functional theory (DFT) calculations reveal that the introduction of Ni effectively activates Fe sites by optimizing the free energy of the *OOH intermediate and that the abundance of oxygen defects facilitates the oxygen desorption step, synergistically enhancing the OER performance of LiFePO4. As a green and versatile method, this is a new opportunity for the scalable fabrication of excellent electrocatalysts based on spent cathode materials.

The topological surface states of magnetic topological systems, such as Weyl semimetals and axion insulators, are associated with unconventional transport properties such as nonzero or half-quantized surface anomalous Hall effect. Here we study the surface anomalous Hall effect and its spectral signatures in different magnetic topological phases using both model Hamiltonian and first-principles calculations. We demonstrate that by tailoring the magnetization and interlayer electron hopping, a rich three-dimensional topological phase diagram can be established, including three types of topologically distinct insulating phases bridged by Weyl semimetals, and can be directly mapped to realistic materials such as MnBi2Te4/(Bi2Te3)n systems. Among them, we find that the surface anomalous Hall conductivity in the axion-insulator phase is a well-localized quantity either saturated at or oscillating around e2/2h, depending on the magnetic homogeneity. We also discuss the resultant chiral hinge modes embedded inside the side surface bands as the potential experimental signatures for transport measurements. Our study is a significant step forward towards the direct realization of the long-sought axion insulators in realistic material systems., Experimentally detectable signature of an axion insulator remains elusive. Here, the authors predict a topological phase diagram of MnBi2Te4/(Bi2Te3)n heterostructure, where the chiral hinge mode induced by the surface anomalous Hall conductivity is identified as a signature of an axion insulator state.

This article presents an innovative framework of real-time vision-based pose tracking for asteroid using the contour information of the asteroid image. At the first-time instant, the tracking is initialized by the distance-based template matching and contour-based pose optimization. Subsequently, at each time instant, with the prediction of the extended Kalman filter (EKF) as initial guess, the pose of the asteroid is obtained in real time by geometrically fitting the contour of the projected asteroid CAD model over the contour of the asteroid image with M-estimation. The variance of the pose is calculated based on 1-order approximation inference, which enables EKF to generate the final pose estimation and predict the pose at the next time instant. Sufficient experiments validate the accuracy and the efficiency of the proposed method.

Objective: Transcranial focused ultrasound (tFUS) is an emerging non-invasive brain stimulation tool for safely and reversibly modulating brain circuits. The effectiveness of tFUS on human brain has been demonstrated, but how tFUS influences the human voluntary motor processing in the brain remains unclear. Methods: We apply low-intensity tFUS to modulate the movement-related cortical potential (MRCP) originating from human subjects practicing a voluntary foot tapping task. 64-channel electroencephalograph (EEG) is recorded concurrently and further used to reconstruct the brain source activity specifically at the primary leg motor cortical area using the electrophysiological source imaging (ESI). Results: The ESI illustrates the ultrasound modulated MRCP source dynamics with high spatiotemporal resolutions. The MRCP source is imaged and its source profile is further evaluated for assessing the tFUS neuromodulatory effects on the voluntary MRCP. Moreover, the effect of ultrasound pulse repetition frequency (UPRF) is further assessed in modulating the MRCP. The ESI results show that tFUS significantly increases the MRCP source profile amplitude (MSPA) comparing to a sham ultrasound condition, and further, a high UPRF enhances the MSPA more than a low UPRF does. Conclusion: The present results demonstrate the neuromodulatory effects of the low-intensity tFUS on enhancing the human voluntary movement-related cortical activities evidenced through the ESI. Significance: This work provides the first evidence of tFUS enhancing the human endogenous motor cortical activities through excitatory modulation.

Rolling bearing local fault vibration mechanism research is the theoretical basis of advanced bearing fault diagnosis and size measurement technology. In this paper, the additional displacement and impact force excitation of inner and outer ring local faults under low speed and heavy load condition are analysed, and rolling bearing local fault vibration model is established. The simulated dual impulse characteristics have higher accuracy under low speed and heavy load condition compared with other traditional local fault models. The outer and inner ring fault simulated dual impulse interval errors are 6.7% and 1.1%, respectively. Then the local fault vibration mechanism is analysed, and the influence of heavy load condition on dual impulse characteristic is not negligible. Finally, a size measurement method of bearing local fault based on vibration model is proposed. Two sizes of outer ring fault bearing are tested in four different working conditions, and the average measurement error is 3.78%. The results show that the proposed method can overcome the influence of different working conditions and accurately measure different local fault sizes.

The addition of effective additives can effectively improve the pyrolysis performance of oil sludge (OS) and have a great potential to reduce pyrolysis costs. In the present study, co-pyrolysis performance of OS with different proportions of additives at a heating rate of 10°C/min was conducted in a thermal analyzer. Walnut shell, Fe2O3, K2CO3, PVC and the pyrolysis char produced from OS at the final pyrolysis temperature of 700°C were selected as the additives. TG results showed that the OS pyrolysis with and without additives can be divided into five reaction stages, which include volatilization of free water, the escape of light components, the cleavage of heavy components, carbon decomposition and inorganic minerals decomposition. The addition of additives decreased the maximum weight loss rate when the blending ratio was 5 wt% during OS pyrolysis. Kinetic analysis revealed that the overall activation energy of pyrolysis reaction was lower during pyrolysis of OS with the addition of walnut shells and pyrolysis char. The activation energy of three main reaction stages all decreased during co-pyrolysis of OS with K2CO3 and PVC.

Viscoelasticity, a time-scale mechanical feature of the native extracellular matrix (ECM), is reported to play crucial roles in plentiful cellular behaviors, whereas its effects on neuronal behavior and the underlying molecular mechanism still remain obscure. Challenges are faced in the biocompatible synthesis of neural ECM-mimicked scaffolds solely controlled with viscoelasticity and due to the lack of suitable models for neurons-viscoelastic matrix interaction. Herein, we report difunctional hyaluronan-collagen hydrogels prepared by a static-dynamic strategy. The hydrogels show aldehyde concentration-dependent viscoelasticity and similar initial elastic modulus, fibrillar morphology, swelling as well as degradability. Utilizing the resulting hydrogels, for the first time, we demonstrate matrix viscoelasticity-dependent neuronal responses, including neurite elongation and expression of neurogenic proteins. Then, a motor-clutch model modified with a tension dissipation component is developed to account for the molecular mechanism for viscoelasticity-sensitive neuronal responses. Moreover, we prove enhanced recovery of rat spinal cord injury by implanting cell-free viscoelastic grafts. As a pioneer finding on neurons-viscoelastic matrix interaction both in vitro and in vivo, this work provides intriguing insights not only into nerve repair but also into neuroscience and tissue engineering.

Purpose The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection. Design/methodology/approach This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences. Findings The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects. Research limitations/implications The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner. Originality/value This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.

Highly efficient noble-metal-free electrocatalysts for oxygen reduction reaction (ORR) are essential to reduce the costs of fuel cells and metal-air batteries. Herein, a single-atom Ce-N-C catalyst, constructed of atomically dispersed Ce anchored on N-doped porous carbon nanowires, is proposed to boost the ORR. This catalyst has a high Ce content of 8.55 wt % and a high activity with ORR half-wave potentials of 0.88 V in alkaline media and 0.75 V in acidic electrolytes, which are comparable to widely studied Fe-N-C catalysts. A Zn-air battery based on this material shows excellent performance and durability. Density functional theory calculations reveal that atomically dispersed Ce with adsorbed hydroxyl species (OH) can significantly reduce the energy barrier of the rate-determining step resulting in an improved ORR activity.

Tennis players have more physical training content, and the training items are complex. For athletes, training programs that adapt to their individual characteristics should be formulated according to their physical characteristics. The current development of big data has brought about changes in thinking, management, and business models. The combination of complex systems and big data can also make breakthroughs in the sports field. Based on this, this article proposes a tennis player training schedule intelligent formulation system based on complex system big data. First of all, this article adopts the literature data method, comparative analysis method, experimental analysis method, etc., in-depth study of the concepts of big data, complex system, and the physical structure characteristics of tennis sports. This paper designs an intelligent system for making tennis players’ training schedule, which collects, transforms, and integrates tennis training data through the characteristics of big data. Then, the dynamic time regulation of tennis is performed through a complex system, and finally, the experimental system is analyzed. This article mainly analyzes the comparison of physical indicators between the experimental group and the control group before and after the experiment, the evaluation indicators of sports events, the strength training effects of tennis events, and the analysis of shoulder joint tests. There is no significant difference between the experimental group and the control group in the items before the experiment,P>0.05, which suggests that the physical fitness of the two groups of athletes is similar; in the posttest data, the experimental group and the control group have significant differences,P<0.05, indicating an effect from the experiment. In particular, in fan running, forward and backward strokes, and the serve, the scores of the experimental group were higher than those of the control group, indicating that the use of the formulated training system demonstrated significant results.

In this paper, we propose an improved Harris hawk optimizer (IHHO) to overcome the shortcomings of Harris hawk optimizer (HHO) that there is aimless search in the global search process. IHHO is different from HHO in that during the global search process, the position of the Harris hawks is not random but moves according to the position of the best several Harris hawks in the population. The advantage of the new global search process in the IHHO is that it effectively improves the quality of candidate solutions. We use the proposed algorithm to solve job-shop scheduling problem, which is an NP-hard problem and also a very challenging optimization problem, because it is difficult to find a reasonable candidate solution in polynomial time. Many researchers have tried various methods to solve it, but failed to get an effective result. Therefore, we use it to verify the performance of the proposed algorithm. The experimental results of IHHO are compared with those of improved genetic algorithm, differential evolution and HHO. It can be seen from the experimental results that IHHO has obvious advantages compared with the results of other comparison algorithms in terms of convergence accuracy, convergence speed, stability and running time. The experimental results of Wilcoxon's rank sum test also show that the IHHO is essentially different from other comparison algorithms, which shows that the optimization performance of the IHHO is meaningful.

Automatic Speech Recognition (ASR) is playing a vital role in a wide range of real-world applications. However, Commercial ASR solutions are typically “one-size-fits-all” products and clients are inevitably faced with the risk of severe performance degradation in field test. Meanwhile, with new data regulations such as the European Union’s General Data Protection Regulation (GDPR) coming into force, ASR vendors, which traditionally utilize the speech training data in a centralized approach, are becoming increasingly helpless to solve this problem, since accessing clients’ speech data is prohibited. Here, we show that by seamlessly integrating three machine learning paradigms (i.e., T ransfer learning, F ederated learning, and E volutionary learning (TFE)), we can successfully build a win-win ecosystem for ASR clients and vendors and solve all the aforementioned problems plaguing them. Through large-scale quantitative experiments, we show that with TFE, the clients can enjoy far better ASR solutions than the “one-size-fits-all” counterpart, and the vendors can exploit the abundance of clients’ data to effectively refine their own ASR products.

The low photocatalytic activity of red phosphorus (RP) for H2 production seriously restricts its wide application. In this work, we reported a facile synthesis and remarkable activity improvement of NiO/RP composite for water splitting. As a result, the 3% NiO/RP composite exhibited the highest photocatalytic activity for H2 production (57.27 μmol g−1 h−1), which is 68.56 times higher than that of pure RP (0.82 μmol g−1 h−1) under visible light (λ ≥ 420 nm) irradiation. The investigation of photocarriers separation mechanism indicated NiO/RP composite applied a Z-scheme mechanism to promote the photocarriers separation. This is a potential strategy to dramatically enhance the photocatalytic activity of RP for H2 evolution using transition metal oxide to efficiently separate the photocarriers under light irradiation.

Free-standing nanocrystalline graphite (NCG) membranes directly grew on SiNx substrates with the aid of the catalyst-free chemical vapor deposition method. A new type of NCG nanopore was formed in the NCG membrane via controlled dielectric breakdown in KCl electrolyte. The electrical properties, especially the ionic conductance of the NCG nanoporewere examined under different physical parameters (pore size, ion strength, and pH condition). Due to their impressive transport properties, we first realized DNA sensing using the NCG nanopores.

International audience; Ramie plants (Boehmeria nivea) have been cultivated on ion-adsorption rare earth elements (REEs) mine tailings and showed a low ability to accumulate REEs, especially in the stems (REE= 24±17 mg kg-1) with higher concentration in leaves and roots (REE= 57 to 66 mg kg-1). Ramie individual fibers were isolated using three degumming processes: a traditional alkali treatment (AT), a steam explosion (SE) treatment after a neutral (SEN) or after a basic (SEB) impregnation. Cellulose-rich (> 80%) fine fibers with very low amounts of non-cellulosic sugars (

Aiming at industrial parks and business parks equipped with photovoltaic (PV) power plants and vanadium redox battery energy storage devices, this work studies the collaborative scheduling optimisation problem of real-time response of two-area active distribution system to the random peak shaving demand of large power grids. Firstly, considering the randomness of source and load, the stochastic dynamic changes of PV output, various load demands and the grid peak shaving demand are described as Gauss-Markov processes. Secondly, the hierarchical dynamic control mode is used to transform the collaborative dynamic scheduling problem of two-area active distribution system into a two-layer scheduling optimisation model. The upper layer considers the total cost of operation as the optimal goal and resolves problems related to the task assignment of peak shaving demand for active distribution systems in each area. Meanwhile, the lower-layer areas are optimised to complete the peak shaving task assigned by the upper layer and realise the economic operation of the active distribution system in each area. This study proposes a corresponding model-independent double-layer Q learning algorithm to optimise the hierarchical scheduling strategy. A simulation is conducted to verify the effectiveness of this algorithm. These results indicate that the hierarchical scheduling optimisation mechanism and double-layer Q learning algorithm can effectively solve the collaborative scheduling problem of two-area active distribution systems considering the peak shaving demand of the power grid.

The contact state and stiffness of linear guide are affected by the complex external loads, and the vibrations in different directions are coupled with each other. However, existing dynamic models are inaccurate because they ignore displacement coupling and do not consider the position deviation of guide rail. This paper presents a five degree-of-freedom dynamic model for table system that incorporates the effects of the displacement coupling and position deviation of guide rail. The ball-to-groove contact model and position deviation model are established by using the Hertz contact theory and homogeneous coordinate transformation, respectively. Dynamic equations are solved by combining the Monte Carlo and Newmark methods to determine the displacements of table system, and the accuracy of proposed dynamic model is verified through experimental method. The results show that the vibrations of table system are coupled with each other, and the position deviation of guide rail only affects the vibration amplitudes in the region other than the resonance regions. Proposed model is thus suitable for improving the machining accuracy and stability of machine tools.

Today’s era of big data is witnessing a gradual increase in the amount of data, more correlations between data, as well as growth in their spatial dimension. Conventional linear statistical models applied to mineral prospectivity mapping (MPM) perform poorly because of the random and nonlinear nature of metallogenic processes. To overcome this performance degradation, deep learning models have been introduced in 3D MPM. In this study, taking the Huayuan sedimentary Mn deposit in Hunan Province as an example, we construct a 3D digital model of this deposit based on the prospectivity model of the study area. In this approach, 3D predictor layers are converted from the conceptual model and employed in a 3D convolutional neural network (3D CNN). The characteristics of the spatial distribution are extracted by the 3D CNN. Subsequently, we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3D CNN model and weight of evidence (WofE) method on each group. The predictive model is trained on the basis of the coupling correlation between the spatial distributions of the variables and the underground occurrence space of the Mn orebodies, and the correlation between different ore-controlling factors. The analysis of 12 factors indicates that the 3D CNN model performs well in the 3D MPM, achieving a promising accuracy of up to 100% and a loss value below 0.001. A comparison shows that the 3D CNN model outperforms the WofE model in terms of predictive evaluation indexes, namely the success rate and ore-controlling rate. In particular, the 1–12 ore-controlling factors selected in experiment 5 provide a significantly better prediction effect than the other factors. Consequently, we conclude that the Mn deposit in the study area is not only related to the stratum and interlaminar anomalous bodies but also to the spatial distribution of the faults. The experimental results confirm that the proposed 3D CNN is promising for 3D MPM as it eliminates the interference factors.

Germanium monoselenide (GeSe) is an emerging promising photovoltaic absorber material due to its attractive optoelectronic properties as well as non-toxic and earth-abundant constitutes. However, all previously reported GeSe solar cells rely on a superstrate configuration coupled with a CdS buffer layer, and suffer from unsatisfactory performance. Here we demonstrate that this low efficiency arises from the inevitable high-temperature treatment of p-n junction in superstrate configuration. This results in the diffusion of Cd atoms from CdS layer into GeSe film that introduces detrimental deep trap states inside the bandgap of GeSe (∼0.34 eV below conduction band minimum). We adopt therefore a substrate configuration that enables the deposition of CdS atop pre-deposited polycrystalline GeSe film at room temperature, avoiding the Cd diffusion. By optimizing the annealing temperature of complete devices via a high-throughput screening method, the resulting substrate solar cells annealed at 150°C achieve an efficiency of 3.1%, two times that of the best previously reported superstrate GeSe results.

Carbon dots (CDs) with tunable size were synthesized form coal tar pitch through a simple hydrothermal method using NaOH as additive. The chemical composition and morphological feature of the obtained CDs were characterized. The prepared CDs were high crystallinity in nature. The size of CDs could be regulated by varying the addition amount of NaOH. By increasing the CDs’ size from 1.9 to 5.8 nm, the CDs supported Pt show a 70% improvement in the solar-to-hydrogen efficiency.

Crosslinker is a vital factor for the preparation of polymeric membranes, which has a substantial effect not only on the membrane-forming process, but also on the membrane structure as well as separation performance. However, little attention has been paid on UV-crosslinked membrane for organics pervaporation. In this work, the effect of crosslinker 3-methacryloxypropylmethyldimethoxysilane (KH571) on (1) the UV-crosslinking kinetics was characterized by real-time infrared spectroscopy and photo-rheology, (2) the membrane structure was characterized by crosslinking density, and (3) separation performance was evaluated by ethanol pervaporation. Moreover, the conventional polydimethylsiloxane (PDMS) was replaced by polydimethylsiloxane-poly[(3,3,3-trifluoropropyl)methylsiloxane] (PDMS-PTFPMS) block copolymer, where the strong C–F bond contributes to enhance membrane hydrophobicity. Results show an ultrafast membrane-forming process (30−50 s) for all coating solution systems and an outstanding separation performance, including 11.3 of ethanol separation factor and 1149 g m−2 h-1 of total flux for pervaporation recovery of 5 wt% ethanol/water solution at 60 °C.

Deep neural networks (DNNs) have been extensively studied in medical image segmentation. However, existing DNNs often need to train shape models for each object to be segmented, which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging (MRI). In this paper, we propose a capsule-based neural network, named Seg-CapNet, to model multiple regions simultaneously within a single training process. The Seg-CapNet model consists of the encoder and the decoder. The encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers, capsule layers, and fully-connected layers. And the decoder transforms the feature vectors into segmentation masks by up-sampling. Feature maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers, which are conducive to the backpropagation of the model. The output vectors of Seg-CapNet contain low-level image features such as grayscale and texture, as well as semantic features including the position and size of the objects, which is beneficial for improving the segmentation accuracy. The proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017 (ACDC 2017) and the Sunnybrook Cardiac Magnetic Resonance Imaging (MRI) segmentation challenge. Experimental results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7% and the average Hausdorff distance is reduced by 22%. The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions. Supplementary Information The online version contains supplementary material available at 10.1007/s11390-021-0782-5.

Potassium batteries are an emerging energy storage technology due to the large abundance of potassium, low cost, and potentially high energy density. However, it remains challenging to find suitabl...

The application of the carbon fiber–reinforced polymer (CFRP) becomes increasing in aerospace. In this paper, based on the two-parameter elastic foundation model, a representative volume element (RVE) method is used to establish the deflection curve equation of the cutting fiber as the thickness of the uncut material changes. The cutting force prediction model was proposed from the three areas of the chipping area of the rake face, the bounding area of the flank face, and the shearing area of the tool nose. The deformation process of the cutting fiber under different cutting depths was analyzed. The relationship between the uncut material thickness and the fiber deformation length was discussed. The orthogonal cutting experiment of unidirectional carbon fiber–reinforced polymer (UD-CFRP) in the thickness direction was conducted to verify the model. The experimental results show that the relative errors between theoretical and experimental values of the cutting force Fy and Fx are 8.2 and 1%, respectively, when the cutting depth is 0.025 mm. The theoretical cutting force is in good agreement with the experimental cutting force, which proved the accuracy of the fiber deflection curve model. The deflection curve and the debonding length of the cutting fiber changes with the uncut material thickness under different cutting depths of 0.025, 0.05, and 0.075 mm. In addition, it explains the formation of burrs when the thickness of the uncut material is 0.1 mm.

New materials and related new equipment are increasingly important to maintain the safety and stability of the asynchronous interconnection systems. DC lines equipped with Frequency Limit Controller (FLC) are able to quickly balance power fluctuation and limit frequency deviation. However, the frequency stability problem, especially in the sending end system characteristics of “large generation and small network”, still draws our attention for its significance to the gird. Based on the analysis of the primary frequency regulation principle of the power system and the impact of reserve configuration on frequency deviation in asynchronous interconnection, an optimization approach for primary frequency regulation reserve capacity, featured by the sequence quadratic programming, with the minimum quasi-steady-state frequency deviation, was proposed in this paper. The optimization idea of this approach is to arrange the reserve configuration in proportion to the unit’s adjustment coefficient and the dead zone, in order to prevent non-performance of some units while other units are sufficient. A numerical simulation indicated that, compared with the original scheme, the system frequency deviation was effectively reduced.

Recently, the two-dimensional heterostructure is a research hot spot of semiconductor materials, and it has wide application prospects in electronic devices and photocatalysis. In this work, we constructed novel graphene-GaN-graphene (G-GaN-G) sandwich heterostructure, and the electronic and optical properties have been theoretically investigated based on the first principles calculations. The computation results indicate that the heterostructure is most stable when interlayer distance d0 is 3.189 A, and electronic and optical properties of intrinsic GaN and graphene are well preserved. In addition, the effect of vertical and biaxial strain on the G-GaN-G heterostructure is also investigated, we find that strain can effectively regulate the electronic and optical properties. Therefore, the electronic and optical properties of G-GaN-G can be tuned by applying different levels of strain to meet the needs of devices. This study results reveal that the G-GaN-G sandwich heterostructure can be hopefully applied to ultraviolet photodetectors and optoelectronic devices.

It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes (SWCNTs). Here, a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs. Patterned cobalt (Co) nanoparticles were deposited on a numerically marked silicon wafer as catalysts, and parameters of temperature, reduction time and carbon precursor were optimized. The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity (IG/ID) was extracted automatically and mapped to the growth parameters to build a database. 1,280 data were collected to train machine learning models. Random forest regression (RFR) showed high precision in predicting the growth conditions for high-quality SWCNTs, as validated by further chemical vapor deposition (CVD) growth. This method shows great potential in structure-controlled growth of SWCNTs.

The influence of the weaving method on the flame retardancy of the fabrics prepared cannot be ignored. In this work, the thermal stability, flame retardancy, flame-retardant mechanism, mechanical properties and water absorption of cotton/alginate blended knitted fabrics were investigated. Thermogravimetric analysis results showed that the char residues at 700 °C of cotton/alginate blended knitted fabrics increased both in N2 and air. In the cone calorimetry test, compared with cotton knitted fabrics, the fire growth rate index of cotton5/alginate5 decreased from 2.63 to 1.40 kW m−2 s. Thermogravimetric analysis coupled with Fourier transform infrared analysis indicated that the cotton/alginate blended knitted fabrics released more non-flammable gases and fewer inflammable products containing carbonyl and ether groups. Analysis of Raman spectra showed that the char residues of cotton5/alginate5 had a higher degree of graphitization. The analysis of X-ray photoelectron spectroscopy spectra proved the existence of CaCO3 and CaO in the char residues. Moreover, the introduction of alginate fibers improved the water absorption and water retention properties of cotton knitted fabrics, but reduced its mechanical properties.

Textured transparent conductive electrodes for thin-film solar cells have been considered as an effective route for enhancing sunlight harvest due to light trapping. Here, we report a self-assembling electrochemical approach for preparing Al-doped ZnO nanorod arrays (NRAs) as light-trapping electrodes from a mixed aqueous solution of zinc nitrate and aluminium nitrate. The mechanism and optimized process conditions of forming one-dimensional ZnO nanorods with Al-doping were systematically investigated. The results showed that Al atoms were successfully doped into ZnO crystal lattice, and the morphologies could be controlled by adjusting the Al3+/Zn2+ ratio in the precursors and deposition time. The Al-doped ZnO films grew into well-aligned hexagonal NRAs with c-axis perpendicular to the substrates and then transited into a mixture of nanosheets and nanorods with Al3+/Zn2+ ratio increasing. They exhibited good electrical conductivity with a sheet resistance of 68-167Ω/square and appropriate visible light transmittance of 61-82%. Taking into account of desired morphology and phase purity, as well as good electrical conductivity and optical transmittance, the optimal window of Al3+/Zn2+ ratio in the precursors was determined between 1 at% and 2 at% with applied potential of -1.5V, bath temperature of 80°C, and deposition time of about 30min. The electrodeposition method provides a facile and efficient route for obtaining large-area textured transparent electrodes at a low cost.

With the rapid development of urban underground space, the depth of working shaft has been gradually becoming deeper to accommodate the underground construction needs. The existing studies for earth pressure distribution of shafts are mainly focused on sandy soils within 30 m. There is a strong need to investigate the earth pressure distribution on deep shafts (more than 30 m) in complicated soil conditions using centrifuge modelling to fill the knowledge gap and provide experimental basis for design. In this paper, geotechnical centrifuge modelling was used to investigate the earth pressure distribution along deep circular shaft structures (with a depth of 100 m) in composite geological strata. Three centrifuge tests on model shafts in layered ground with different thickness of clay and sand were carried out, respectively. Drainage method was used in the experiment to simulate the unloading effect caused by excavation of soils inside the shaft model. The earth pressure distributions along the shaft lining before and after the excavation were investigated. The results were analysed and compared with other existing earth pressure theories. This research provides valuable insights for earth pressure distribution for design of circular deep shafts (e.g. 100 m) in composite geological strata.

The realization of high-efficiency desalination technologies is the key to the reuse of circulating cooling sewage in power plants. Desalination experiments were carried out on an electro-sorption ...