Highlights
Comprehensive summary of crystalline structures and morphologies of carbon nitride-based materials (CNBMs).
Density functional theory computation for the design of functional CNBMs for ...rechargeable battery applications.
The experimental synthesis strategies of CNBMs for rechargeable battery application.
Carbon nitrides (including CN, C
2
N, C
3
N, C
3
N
4
, C
4
N, and C
5
N) are a unique family of nitrogen-rich carbon materials with multiple beneficial properties in crystalline structures, morphologies, and electronic configurations. In this review, we provide a comprehensive review on these materials properties, theoretical advantages, the synthesis and modification strategies of different carbon nitride-based materials (CNBMs) and their application in existing and emerging rechargeable battery systems, such as lithium-ion batteries, sodium and potassium-ion batteries, lithium sulfur batteries, lithium oxygen batteries, lithium metal batteries, zinc-ion batteries, and solid-state batteries. The central theme of this review is to apply the theoretical and computational design to guide the experimental synthesis of CNBMs for energy storage, i.e., facilitate the application of first-principle studies and density functional theory for electrode material design, synthesis, and characterization of different CNBMs for the aforementioned rechargeable batteries. At last, we conclude with the challenges, and prospects of CNBMs, and propose future perspectives and strategies for further advancement of CNBMs for rechargeable batteries.
The incorporation of augmentative functionalities into a single synaptic device is greatly desired to enhance the performance of neuromorphic computing, which has brain-like high intelligence and low ...energy consumption. This encourages the development of multi-functional synaptic devices with architectures that are capable of achieving demanded synaptic plasticity. Here we take advantage of the remarkable optical absorption of boron (B)-doped silicon nanocrystals (Si NCs) to make synaptic phototransistors, which can be stimulated by both optical and electrical spikes. The optical and electrical stimulations enable a series of important synaptic functionalities for the synaptic Si-NC phototransistors, well mimicking biological synapses. It is interesting that the synergy of the photogating and electrical gating of the synaptic Si-NC phototransistors leads to the implementation of aversion learning and logic functions. We show that a spiking neural network based on the synaptic Si-NC phototransistors may be trained for the recognition of handwritten digits in the modified national institute of standards and technology (MNIST) database with a recognition accuracy around 94%. The energy consumption of the synaptic Si-NC phototransistors may be rather low, which should help advance energy-efficient neuromorphic computing.
Display omitted
•Synaptic phototransistors with optical and electrical stimulations are fabricated by using silicon nanocrystals (Si NCs).•The synergy of the photogating and electrical gating enables the emulation of aversion learning and logic functions.•A spiking neural network based on Si-NC phototransistors is proposed for pattern recognition with accuracy up to 94%.
Threaded connections are the most common pipe fittings used in oil and gas transportation systems. Due to external vibrations, cyclic loads, and pollution, the fitting parts may start getting loose, ...which could result in pipeline leaks and other environmental disasters. It is of great significance to develop a reliable technique that could provide real-time monitoring of the looseness of pipeline fittings. In this paper, a piezoceramic-based active sensing method combined with the electro-mechanical impedance (EMI) technique was developed to monitor the health condition of threaded pipe connections in real time. Two pipe segments coupled with a threaded coupling fitting were assembled in the laboratory, and a lead zirconate titanate (PZT) patch was surface bonded onto the coupling part. In the experiment, the PZT impedance signatures were measured at each simulated loosening condition. A root-mean-square deviation (RMSD) method was employed to build a looseness index from the measured impedance signatures. To verify the effectiveness of the developed EMI technique, the experimental results were compared with those computed from a numerical simulation. The good agreement from experimental and numerical results highlights that the developed piezoceramic-based EMI technique has great potential for determining early looseness, as well as for monitoring the health status of the pipeline fitting during its service life.
The ground-airborne time-domain electromagnetic (GATEM) method, as a new type of electromagnetic method, offers detection efficiency and large investigation depth. It is an efficient electromagnetic ...detection technique for geological exploration and groundwater exploration. The field data are disturbed by motion noise and mixed noises, such as white noise and electromagnetic noises, which will reduce the data quality and accuracy of data interpretation. The characteristics of GATEM responses are analysed, and a denoising algorithm based on variational Bayesian-based adaptive Kalman filter (VBAKF) is proposed. Considering the attenuation characteristics of the GATEM response, the exponentially fitted data input into the VBAKF. Then the VBAKF with inverse Wishart priors is used to denoise GATEM field data, and the unknown process noise covariance matrix (PNCM) and measurement noise covariance matrix (MNCM) are estimated by variational Bayesian. The VBAKF algorithm is tested on the responses with different noises. It is verified on a layered geological model and compared with the adaptive Kalman filter (AKF) algorithm and the combined wavelet filter (CWF) algorithm. It is also tested on synthetic data of an anomalous model. Through synthetic data, the VBAKF algorithm can effectively suppress noise of GATEM data. Finally, the algorithm is applied to field data in Leshan, Jilin province, China. It effectively improves the quality of field data.
•Focus on obtain high-precision GATEM data.•Improve robustness of VBAKF with inverse Wishart priors.•Verify the VBAKF method through synthetic and field data.
In this paper, the influence of Nano-silica (NS) and Polyvinyl alcohol (PVA) fibers on the corrosion behavior of steel rebar embedded in high-volume fly ash cement mortars under accelerated chloride ...attack was studied by using an impressed voltage technique. The PVA fibers used were 1.0 vol.%, and two mass fractions of cement (50 and 60 wt.%) were replaced by fly ash. Four NS mass fractions (0, 0.5, 1.0, and 1.5 wt.%) were utilized in this paper. In addition, the mono and hybrid effects of NS and PVA on the mechanical properties and water absorption of mortar were also studied. The results showed that the incorporation of PVA and nano-SiO2 can improve the flexural and compressive strengths of high-volume fly ash mortar. Generally, the flexural and compressive strengths increased with the increase of nano-SiO2 content. Moreover, the incorporation NS can also reduce the capillary water–absorption rate of cement mortar. The impressed voltage corrosion test indicated that the composite incorporation of nano-SiO2 and PVA can significantly delay the deterioration process of steel bars in mortar, effectively reducing the steel rebar’s corrosion level and increasing the exposure time of the surface crack. With hybrid-incorporation 1.0 vol.% PVA and 1.0 wt.% nano-SiO2, the steel rebar had the lowest corrosion degree, which exhibited a mass loss of 49% and increased the broken time by 71% as compared to the control mortar.
We present an analysis on the heavy flavor hadron decay electrons with charm and beauty contributions decomposed via a data driven method in p+p and Pb+Pb collisions at sNN = 5.02 TeV at LHC. The ...transverse momentum pT spectra, nuclear modification factor RAA and azimuthal anisotropic flow v2 distributions of electrons from charm and beauty decays are obtained. We find that the electron RAA from charm (RAAc→e) and beauty (RAAb→e) decays are suppressed at pT>2.0 GeV/c in Pb+Pb collisions, which indicates that charm and beauty interact with and lose their energy in the hot-dense medium. A less suppression of RAAb→e than RAAc→e at 2.0<pT<8.0 GeV/c is observed, which is consistent with the mass-dependent partonic energy loss scenario. A non-zero electron v2 from beauty decays (v2b→e) is observed and in good agreement with ALICE measurement. At low pT from 1.0 to 3.0 GeV/c, a discrepancy between RHIC and LHC results is observed with an 85% confidence level, which might be a possible hint of an energy-dependent behavior of beauty quarks reacting with the medium created in heavy-ion collisions. At 3.0<pT<7.0 GeV/c, v2b→e deviates from a number-of-constituent-quark (NCQ) scaling hypothesis, which favors that beauty quark is unlikely thermalized in heavy-ion collisions at LHC energy.
In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development ...mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments.
Taxi demand prediction is one of the key factors in making online taxi hailing services more successful and more popular. Accurate taxi demand prediction can bring various advantages including, but ...not limited to, enhancing user experience, increasing taxi utilization, and optimizing traffic efficiency. However, the task is challenging because of complex spatial and temporal dependencies of taxi demand. In addition, relationships between non-adjacent regions are also critical for accurate taxi demand prediction, whereas they are largely ignored by existing approaches. To this end, we propose a novel graph and time-series learning model for city-wide taxi demand prediction in this paper. It has two main building blocks, the first one utilize a graph network with attention mechanism to effectively learn spatial dependencies of taxi demand in a broader perspective of the entire city, and the output at each time interval is then transferred to the second block. In the graph network, the edge is defined by an Origin–Destination relation to capture non-adjacent impacts. The second one uses a neural network which is adept with processing sequence data to capture the temporal correlations of city-wide taxi demand. Using a large, real-world dataset and three metrics, we conduct an extensive experimental study and find that our model outperforms state-of-the-art baselines by 9.3% in terms of the root-mean-square error.
In this paper, we use the spin-on-dopant technique for phosphorus doping to improve the photoelectric properties of soft-chemical-prepared silicon nanosheets. It was found that the luminescence ...intensity and luminescence lifetime of the doped samples was approximately 4 fold that of the undoped samples, due to passivation of the surface defects by phosphorus doping. Meanwhile, phosphorus doping combined with high-temperature heat treatment can reduce the resistivity of multilayer silicon nanosheets by 6 fold compared with that of as-prepared samples. In conclusion, our work brings soft-chemical-prepared silicon nanosheets one step closer to practical application in the field of optoelectronics.
PDF
