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Functional nanomaterials possess exceptional multi-physical (e.g., mechanical, electrical and optical) properties compared with their bulk counterparts. To facilitate both synthesis and device applications of these nanomaterials, it is highly desired to characterize their multi-physical properties with high accuracy and efficiency. The nanomanipulation techniques under scanning electron microscopy (SEM) has enabled the testing of mechanical and electrical properties of various nanomaterials. However, the seamless integration of mechanical, electrical, and optical testing techniques into an SEM for triple-field-coupled characterization of single nanostructures is still unexplored. In this work, we report the first SEM-based nanomanipulation system for high-resolution mechano-optoelectronic testing of single semiconductor InGaN/GaN nanowires (NWs). A custom-made optical measurement setup was integrated onto a four-probe nanomanipulator inside an SEM, with two optical microfibers actuated by the nanoma-nipulator for NW excitation and emission measurement. A conductive tungsten nanoprobe and a conductive atomic force microscopy (AFM) cantilever probe were integrated onto the nanomanipulator for electrical nanoprobing of single NWs for electroluminescence (EL) measurement. The AFM probe also served as a force sensor for quantifying the contact force applied to the NW during nanoprobing. Using this unique system, we examined, for the first time, the effect of mechanical compression applied to an InGaN/GaN NW on its optoelectronic properties.

Pulmonary fibrosis (PF) is an irreversible lung disease that is characterized by excessive scar tissue with a poor median survival rate of 2–3 years. The inhibition of TGF-β receptor type-I (TGF-β RI) by an appropriate drug may provide a promising strategy for the treatment of this disease. Polygonum cuspidatum (PC) is a well-known traditional Chinese herbal medicine which has an anti-PF effect. Accordingly, a combination of high resolution mass spectrometry and an in silico strategy was developed as a new method to search for potential chemical ingredients of PC that target the TGF-β RI. Based on this strategy, a total of 24 ingredients were identified. Then, absorption, distribution, metabolism, and excretion (ADME)-related properties were subsequently predicted to exclude compounds with potentially undesirable pharmacokinetics behaviour. Molecular docking studies for TGF-β RI were adopted to discover new PF inhibitors. Eventually, a compound that exists in PC known as resveratrol was proven to have excellent biological activity on TGF-β RI, with an IC50 of 2.211 μM in vitro. Furthermore, the complex formed through molecular docking was tested via molecular dynamics simulations, which revealed that resveratrol had strong interactions with residues of TGF-β RI. This study revealed that resveratrol has significant potential as a treatment for PF due to its ability to target TGF-β RI. In addition, this research demonstrated the exploration of natural products with excellent biological activities toward specific targets via high resolution mass spectrometry in combination with in silico technology is a promising strategy for the discovery of novel drugs.

The advanced metering infrastructure (AMI) in modern networked smart homes brings various advantages. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of ML algorithms. In this paper, we present a covert electricity theft strategy through mimicking normal consumption patterns. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we propose a feature extraction method and develop a novel detection model based on deep learning. Extensive experiments show that the presented attack could evade existing mainstream detectors and the proposed countermeasure outperforms existing leading methods.

While existing prediction models built on popular deep architectures have shown promising results in facial depression recognition, they still lack sufficient discriminative power due to the issues of 1) limited amount of labeled depression data for deep representation learning and, 2) large variation in facial expression across different persons of the same depression score and the subtle difference in facial expression across different depression levels. In this paper, we formulate the facial depression recognition as a label distribution learning (LDL) problem, and propose a deep joint label distribution and metric learning (DJ-LDML) method to address these issues. In DJ-LDML, LDL exploits label relevance inherent in depression data to implicitly increase the amount of training data associated with each depression level without actually enlarging the dataset, while deep metric learning (DML) aims at learning a deep ordinal embedding with a specifically designed label-aware histogram loss, allowing semantics similarity between video sequences (described by ordinal labels) to be preserved for discriminative feature learning. The two learning modules in our DJ-LDML work collaboratively to enhance the representation ability and discriminative power of the deeply learned spatiotemporal feature, leading to improved depression prediction. We empirically evaluate our method on two benchmark datasets and the results demonstrate the effectiveness of our formulation.

Inrush of limestone karst water through the mine floor occurs frequently in the Carboniferous–Permian coalfield in northern China. The unascertained measurement evaluation and set pair analysis methods are proposed to evaluate water inflow, by using eight indices. Firstly, ordered binary comparison quantization method and interval number fuzzy decision matrix of EA-TOPSIS ordering were used to obtain weights for these eight factors. Secondly, conflicting evidence theory is applied to couple of these weights calculated by ordered binary comparison quantization and EA-TOPSIS ordering, to ensure the effective evaluation of the relative importance with respect to each indicator for the dynamic model. Lastly, the unapprehend measurement evaluation and set pair analysis methods were utilized to evaluate the risk of limestone karst water influx from coal seam floor. Based on the type of index value, the state classification is carried out through quantitative analysis, and single index measure functions including linear and non-linear patterns, are constructed. The process is described as follows: first, the 8 index values collected from 30 water inrush points in Xinwen coalfield are substituted into the measurement function to construct the multi index comprehensive evaluation matrix. Second, the credibility identification criterion is introduced to determine the risk level of water incurred point X i Finally, the general situation of water inflow risk from coal seam floor during coal mining in Xinwen coalfield is illustrated by using set pair potential analysis method. The production practice in Xinwen coalfield has proved that the evaluation results are consistent with the actual situation.

Solving constrained multiobjective optimization problems brings great challenges to an evolutionary algorithm, since it simultaneously requires the optimization among several conflicting objective functions and the satisfaction of various constraints. Hence, how to adjust the tradeoff between objective functions and constraints is crucial. In this article, we propose a dynamic selection preference-assisted constrained multiobjective differential evolutionary (DE) algorithm. In our approach, the selection preference of each individual is suitably switching from the objective functions to constraints as the evolutionary process. To be specific, the information of objective function, without considering any constraints, is extracted based on Pareto dominance to maintain the convergence and diversity by exploring the feasible and infeasible regions; while the information of constraint is used based on constrained dominance principle to promote the feasibility. Then, the tradeoff in these two kinds of information is adjusted dynamically, by emphasizing the utilization of objective functions at the early stage and focusing on constraints at the latter stage. Furthermore, to generate the promising offspring, two DE operators with distinct characteristics are selected as components of the search algorithm. Experiments on four test suites including 56 benchmark problems indicate that the proposed method exhibits superior or at least competitive performance, in comparison with other well-established methods.

To alleviate the secondary contamination of our environment when using quantum dots (QDs) to detect the organophosphorothioate pesticides (OPPs), we herein report a strategy to assemble magnetic Fe3O4 nanoparticles and luminescent CdTe quantum dots (QDs) into a composite nanosystem, which possesses both the magnetic property of Fe3O4 nanoparticles and the luminescent character of CdTe QDs, for the detection of chlorpyrifos, one of the typical OPPs. This strategy involves the isolated synthesis of magnetic Fe3O4 nanoparticles with positive charges and luminescent CdTe QDs with negative charges, and their subsequent assembly by electrostatic interaction. The as-prepared Fe3O4-CdTe nanocomposites have a detection limit as low as 10 ppb for chlorpyrifos, and are also selective for the OPPs with a phosphorothioate moiety (P=S bond). In specific, the Fe3O4-CdTe nanocomposites can be conveniently harvested by a normal magnet, and the recycling rate for both Cd and Fe determined by inductively coupled plasma atomic emission spectroscopy (ICP-AES) is higher than 96%, showing great potential in alleviating the Cd pollution on the environment.

The magnetic gear integrated permanent magnet brushless machine (MG-IPMBM) is a kind of compact structure with high torque density and complex electromagnetic energy transfer. In order to analyze the transient characteristics of the machine, an analysis method of field circuit coupling is proposed. Both magnetic gear and permanent magnet motor are magnetized by Halbach array. Combining the analytical model of the magnetic gear magnetic field with MATLAB motor simulation module, a joint simulation model of field circuit coupling double closed-loop control system based on the Simulink-analytical method is established. The electromechanical transient response characteristics of machine under load starting, load mutation and overload self-protection are analyzed. The simulation results show that the output speed and torque of machine can accurately follow the given value, and its unique overload self-protection ability greatly improves the safety of machine operation. In addition, a prototype is made and an experimental platform is built. The experimental results show that the output torque follows quickly, the torque ripple is small, and transient performance of the machine is good.

Block permutation and Co-XOR (BPCX) image encryption is a commonly used encryption method for reversible data hiding in the encryption domain, which can effectively improve the embedded capacity and the ability resisting the existing attacks including ciphertext-only attack and known plaintext attack (KPA). This paper proposes a KPA based on bit-block inversion and mean equivalent division (MED) to estimate the block permutation key of BPCX image encryption. Firstly, we divide an image block into the bit-block and point out that the maximum of the numbers of 0 bit and 1 bit of a bit-block before and after the Co-XOR encryption remains unchanged. And then two inversion rules of bit-block are defined to construct pseudo plain-ciphertext images to eliminate the influence of pixel value changes caused by Co-XOR encryption. Finally, the MED based KPA is designed to estimate the block permutation key sequence according to the pseudo plain-ciphertext images. The relationship between the key estimation accuracy and the number of known plain-ciphertext pairs, block size, and pseudo ciphertext are discussed. Experimental results show that even in the minimum block size (2×2), the average estimated correct rate of the block permutation sequence exceeds 40%. The block permutation key estimation accuracy is more than 50% when the block size is greater than 3×3. Some improved encryption methods against the proposed KPA are also given.

To improve the tracking performance of the speed controllers of permanent-magnet synchronous motor (PMSM) drive systems with different disturbances, such as internal parameter variations and external load changes, a novel extended-state-observer-based sliding-mode speed control (ESO-SMSC) scheme for PMSM drives is proposed in this article. First, a fast-response SMSC is designed based on the upper bound of the total disturbance. Then, an ESO is designed to estimate the total disturbance in real time. The parameters of the ESO can be easily designed based on the desired bandwidth of the ESO. The estimated total disturbance is then used to update the control law of the SMSC in real time. The resulting ESO-SMSC has improved speed tracking performance and strong robustness to disturbances while maintaining the fast dynamic response. The stability of the closed-loop PMSM drive system with the proposed ESO-SMSC is proven through the Lyapunov theory. The proposed ESO-SMSC is validated by experimental results for a 200-W salient-pole PMSM drive system.

Gossypol (Gos) is a natural polyphenolic compound that has shown a number of valuable biological properties such as antifertility, antioxidation, and antitumor activities. However, the clinical application of Gos has been hindered by its notable adverse effects such as hypokalemia, hemolytic anemia, and so on. Using sustained-release dosage form provides a hopeful solution to this problem. In this study, a gastric floating tablet for sustained-release of Gos (Gos-GFT) was developed using polyvinylpyrrolidone, hydroxypropyl methyl cellulose, ethyl cellulose, lactose, sodium bicarbonate, and magnesium stearate. Gos-GFT had an average weight of around 200 mg with a drug content percentage of around 13.66%. The physicochemical properties of Gos-GFT satisfied the pharmacopoeial requirements for tablets. Gos-GFT was able to float in an acidic medium and had a sustained drug release for over 12 h. In vivo studies showed that the relative bioavailability of Gos-GFT, as compared with Gos powders, was larger than that of a non-gastric floating tablet which was a dosage form used for comparison with Gos-GFT. Furthermore, compared with the Gos powders and the non-gastric floating Gos tablets, Gos-GFT could prolong the in vivo action time of Gos, and significantly relieve hypokalemia which is a major adverse effect of Gos. These properties made Gos-GFT a promising Gos preparation that warrants further investigation for more extensive clinical applications of this natural compound.

A high-performance capacitive power transfer (CPT) system is expected to achieve the load-independent constant output, near-zero reactive power, and soft switching of power switches simultaneously, resulting in a reduced power stage, simple control circuitry, and minimum component ratings. However, a well-compensated CPT system still suffers very high voltage stresses among not only the main coupled plates but also the leakage coupled plates due to the small coupling and edge emission, which increase the risk of air breakdown and deteriorate the electromagnetic interference (EMI) issue. To solve this problem, the voltage stresses among such coupler plates should be predesigned at an acceptable level. This paper systematically analyzes characteristics of a double-sided LCLC compensated CPT converter, which is proved to have enough design freedom providing predesigned voltage stresses for two kinds of coupled plates. Also, three operating frequencies with load-independent constant current (CC) output and input zerophase angle (ZPA) are found. Without reactive power in the circuit, a parameter design method is proposed for the doublesided LCLC compensated CPT converter at each frequency to satisfy the desired CC output and the predesigned voltage limitations. In this way, the breakdown and EMI issues can be well mitigated by the intended design and this method can also be extended to other CPT circuits. Finally, a CPT prototype is built to verify the theoretical analysis with the predesigned voltage stresses among the coupler plates.

Perovskite LaCoO3 is being increasingly explored as an effective low-cost electrocatalyst for the oxygen evolution reaction (OER). Sr doping in LaCoO3 (La1−xSrxCoO3) has been found to substantially increase its catalytic activity. In this work, we report a detailed study on the evolution of the electronic structure of La1−xSrxCoO3 with 0 ≤ x ≤ 1 and its correlation with electrocatalytic activity for the OER. A combination of X-ray photoemission spectroscopy (XPS) and X-ray absorption spectroscopy (XAS) was used to unravel the electronic density of states (DOS) near the Fermi level (EF), which provide insights into the key electronic structure features for the enhanced OER catalytic activity. Detailed analysis on the Co L-edge XAS suggest that LaCoO3 has a low spin state with t2g6eg0 configuration at room temperature. This implies that the high OER catalytic activity of LaCoO3 should not be rationalized by the occupancy of eg = 1 descriptor. Substituting Sr2+ for La3+ in LaCoO3 induces Co4+ oxidation states and effectively dopes hole states into the top of valence band. A semiconductor-to-metal transition is observed for × ≥ 0.2, due to the hole-induced electronic DOS at the EF and increased hybridization between Co 3d and O 2p. Such an electronic modulation enhances the surface adsorption of the *OH intermediate and reduces the energy barrier for interfacial charge transfer, thus improving the OER catalytic activity in La1−xSrxCoO3. In addition, we found that the La1−xSrxCoO3 surface undergoes amorphization after certain period of OER measurement, leading to a partial deactivation of the electrocatalyst. High Sr doping levels accelerated the amorphization process.

In recent years, tensor computation has become a promising tool for solving big data analysis, machine learning, medical image and EDA problems. To ease the memory and computation intensity of tensor processing, decomposition techniques, especially Tensor-train Decomposition(TTD), are widely adopted to compress the extremely high-dimensional tensor data. Despite TTD’s potential to break the curse of dimensionality, researchers have not yet leveraged its full computational potential, mainly because of two reasons:(1) Executing TTD itself is time-and energy-consuming due to the singular value decomposition(SVD) operation inside each of TTD’s iteration; (2) Additional software/hardware optimizations are often required to process the obtained TT-format data in certain applications such as deep learning inference. In this paper, we address these challenges with two approaches. Firstly, we propose an algorithm-hardware co-design with customized architecture namely TTD Engine to accelerate TTD. We use MRI image compression as a demo application to illustrate the efficacy of the proposed accelerator. Secondly, we present a case study demonstrating the benefit of TT-format data processing and the efficacy of using TTD Engine. In the case study, we use TT approach to realize convolution operation, which is difficult and nontrivial for TT-format data. Experimental results show that, TTD Engine achieves, on average, 14.9×∼36.9× speedup over CPU implementations and 4.1×∼9.9× speedup compared to the GPU baseline. The energy efficiency is also improved by at least 14.4× and 5.4× over CPU and GPU, respectively. Moreover, our hardware-enabled TT-format data processing further leads to more efficient implementations of complicated operations and applications.

It is a challenging task to deploy lightweight security protocols in resource-constrained IoT applications. A hardware-oriented lightweight authentication protocol based on device signature generated during voltage over-scaling (VOS) was recently proposed to address this issue. VOS-based authentication employs the computation unit such as adders to generate the process variation dependent error which is combined with secret keys to create a two-factor authentication protocol. In this paper, machine learning (ML)-based modeling attacks to break such authentication is presented. We also propose a \underline{c}hallenge \underline{s}elf-\underline{o}bfuscation \underline{s}tructure (CSoS) which employs previous challenges combined with keys or random numbers to obfuscate the current challenge for the VOS-based authentication to resist ML attacks. Experimental results show that ANN, RNN and CMA-ES can clone the challenge-response behavior of VOS-based authentication with up to 99.65% prediction accuracy, while the prediction accuracy is less than 51.2% after deploying our proposed ML resilient technique. In addition, our proposed CSoS also shows good obfuscation ability for strong PUFs. Experimental results show that the modeling accuracies are below 54% when $10^6$ challenge-response pairs (CRPs) are collected to model the CSoS-based Arbiter PUF with ML attacks such as LR, SVM, ANN, RNN and CMA-ES.

"Learning from nature" provides endless inspiration for scientists to invent new materials and devices. Here, we review state-of-the-art technologies in flexible electronics, with a focus on bio-inspired smart skins. This review focuses on the development of E-skin for sensing a variety of parameters such as mechanical loads, temperature, light, and biochemical cues, with a trend of increased integration of multiple functions. It highlights the most recent advances in flexible electronics inspired by animals such as chameleons, squids, and octopi whose bodies have remarkable camouflage, mimicry, or self-healing attributes. Implantable devices, being overlapped with smart E-skin in a broad sense, are included in this review. This review outlines the remaining challenges in flexible electronics and the prospects for future development for biomedical applications. STATEMENT OF SIGNIFICANCE: This article reviews the state-of-the-art technologies of bio-inspired smart electronic skin (E-skin) developed in a "learning-mimicking-creating" (LMC) cycle. We emphasize the most recent innovations in the development of E-skin for sensing physical changes and biochemical cues, and for integrating multiple sensing modalities. We discuss the achievements in implantable materials, wireless communication, and device design pertaining to implantable flexible electronics. This review will provide prospective insights integrating material, electronics, and mechanical engineering viewpoints to foster new ideas for next-generation smart E-skin.

A major cause of security incidents such as cyber attacks is rooted in software vulnerabilities. These vulnerabilities should ideally be found and fixed before the code gets deployed. Machine learning-based approaches achieve state-of-the-art performance in capturing vulnerabilities. These methods are predominantly supervised. Their prediction models are trained on a set of ground truth data where the training data and test data are assumed to be drawn from the same probability distribution. However, in practice, the test data often differs from the training data in terms of distribution because they are from different projects or they differ in the types of vulnerability. In this paper, we present a new system for Cross Domain Software Vulnerability Discovery (CD-VulD) using deep learning (DL) and domain adaptation (DA). We employ DL because it has the capacity of automatically constructing high-level abstract feature representations of programs, which are likely of more cross-domain useful than the handcrafted features driven by domain knowledge. The divergence between distributions is reduced by learning cross-domain representations. Firstly, given software program representations, CD-VulD converts them into token sequences and learns the token embeddings for generalization across tokens. Next, CD-VulD employs a deep feature model to build abstract high-level presentations based on those sequences. Then, the metric transfer learning framework (MTLF) technique is employed to learn cross-domain representations by minimizing the distribution divergence between the source domain and the target domain. Finally, the cross-domain representations are used to build a classifier for vulnerability detection. Experimental results show that CD-VulD outperforms the state-of-the-art vulnerability detection approaches by a wide margin. We make the new datasets publicly available so that our work is replicable and can be further improved.

A prevalent family of fully convolutional networks are capable of learning discriminative representations and producing structural prediction in semantic segmentation tasks. However, such supervised learning methods require a large amount of labeled data and show inability of learning cross-domain invariant representations, giving rise to overfitting performance on the source dataset. Domain adaptation, a transfer learning technique that demonstrates strength on aligning feature distributions, can improve the performance of learning methods by providing inter-domain discrepancy alleviation. Recently introduced output-space based adaptation methods provide significant advances on cross-domain semantic segmentation tasks, however, a lack of consideration for intra-domain divergence of domain discrepancy remains prone to over-adaptation results on the target domain. To address the problem, we first leverage prototypical knowledge on the target domain to relax its hard domain label to a continuous domain space, where pixel-wise domain adaptation is developed upon a soft adversarial loss. The development of prototypical knowledge allows to elaborate specific adaptation strategies on under-aligned regions and well-aligned regions of the target domain. Furthermore, aiming to achieve better adaptation performance, we employ a unilateral discriminator to alleviate implicit uncertainty on prototypical knowledge. At last, we theoretically and experimentally demonstrate that the proposed prototypical knowledge oriented adaptation approach provides effective guidance on distribution alignment and alleviation on over-adaptation. The proposed approach shows competitive performance with state-of-the-art methods on two cross-domain segmentation tasks.

Oxidation of sulfite and competitive absorption existed in Na2SO3 solution for simultaneous removal of NOx and SO2, inhibited the long-term high-efficiency when used for practical applications. A matching strategy was developed to solve these problems. Antioxidants combination was used to retard the oxidation of antioxidant and enhance inhibition of S(IV) (tetravalent sulfur) oxidation. Hydroquinone (HQ) and sodium thiosulfate (ST) showed a positive synergistic effect on inhibition of S(IV) oxidation. When SO2 concentration was 500 and 2000 ppmV, the addition of 0.1 wt.% HQ and 1 wt.% ST decreased the percentage of S(IV) oxidized by oxygen by over 30% and 40%, respectively. Alkali (Na2CO3) alleviated the competitive absorption between NOx and SO2. Moreover, Na2CO3 exhibited an enhancement effect on the absorption of NOx and SO2 when coupled with anti-oxidants. While the increase of oxygen pressure accelerated the oxidation of S(IV), the anti-oxidants can retard the oxidation. The measurement of pH suggested the removal efficiency of NOx highly depended on SO32⁻ concentration rather than pH. The further investigation of the mechanism suggested the match effect was related to the interaction between ST and the intermediate product of HQ. The match strategy holds a potential for application of SO32⁻ to denitration.

The rational design of nanozymes with superior activities is essential for improving bioassay performances. Herein, nitrogen and boron co-doped graphene nanoribbons (NB-GNRs) are prepared by a hydrothermal method using urea as the nitrogen source and boric acid as the boron source, respectively. The introduction of co-doped and edge structures provides high defects and active sites. The resultant NB-GNRs nanozymes show superior peroxidase-like activities to nitrogen-doped and boron-doped counterparts due to the synergistic effects. By taking advantage of their peroxidase-like activities, NB-GNRs are used for the first time to develop enzyme-linked immunosorbent assay for the detection of interleukin-6. The biosensors exhibit a high performance with a linear range from 0.001 ng/mL to 1000 ng/mL and a detection limit of 0.3 pg/mL. Due to their low cost and high stability, the proposed nanomaterials show great promise in biocatalysis, immunoassay development and environmental monitoring.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a tissue perfusion imaging technique. Some versatile free-breathing DCE-MRI techniques combining compressed sensing (CS) and parallel imaging with golden-angle radial sampling have been developed to improve motion robustness with high spatial and temporal resolution. These methods have demonstrated good diagnostic performance in clinical setting, but the reconstruction quality will degrade at high acceleration rates and overall reconstruction time remains long. In this paper, we proposed a new parallel CS reconstruction model for DCE-MRI that enforces flexible weighted sparse constraint along both spatial and temporal dimensions. Weights were introduced to flexibly adjust the importance of time and space sparsity, and we derived a fast-thresholding algorithm which was proven to be simple and efficient for solving the proposed reconstruction model. Results on both the brain tumor DCE and liver DCE show that, at relatively high acceleration factor of fast sampling, lowest reconstruction error and highest image structural similarity are obtained by the proposed method. Besides, the proposed method achieves faster reconstruction for liver datasets and better physiological measures are also obtained on tumor images.

Surface oxygen vacancy defects of mesoporous CeO2 nanosheets assembled microspheres (D-CeO2) are engineered by polymer precipitation, hydrothermal and surface hydrogenation strategies. The resultant D-CeO2 with a main pore diameter of 9.3 nm has a large specific surface area (~102.3 m2/g) and high thermal stability. The mesoporous nanosheets assembled microsphere structure prevents the nanosheets from aggregation, which is beneficial to effective mass transfer and shortens the migration distance of charge carriers. After surface hydrogenation, the photoresponse extends to long wavelength region, combing with the band gap from 2.63 eV reduced to 2.39 eV. Under AM 1.5 G radiation, the photocatalytic degradation rate of tetracycline (TC) can be up to 99.99%, which is three times as high as that of pristine CeO2 microspheres. The excellent solar-driven photocatalytic performance can be attributed to the efficient surface oxygen vacancy engineering and the mesoporous nanosheets assembled microsphere structure, which narrows the band gap, shortens the migration distance of carriers, promotes the spatial separation of photogenerated electron-hole pairs and favors mass transfer. The strategy provides new insights for fabricating other high-efficient oxide photocatalysts .

Transitional metal selenides have high conductivity, even metal quality, which makes them great for using as electrode materials for fabricating supercapacitors. Here, hierarchical Ni3Se2 nanosheet-on-nanorods on Ni foam (NSR-Ni3Se2/Ni) was fabricated by a facile three-dimensional (3D) substrate-assisted confinement assembly method, and used as a freestanding electrode material for hybrid supercapacitors (HSCs). In this design, metallic Ni3Se2 with hybrid 1D/2D architecture could effectively enhance the active specific surface area of electrode and improve space utilization, as well as significantly facilitate electrons transport, while Ni foam served as the Ni source of Ni3Se2 and provided 3D multi-electron transport channels, thus boosting the specific capacity. The constructed hierarchical NSR-Ni3Se2 electrode delivered a superior areal specific capacity of 1.068 mAh/cm2 (7.69 F/cm2) at 2 mA/cm2 and retained 68.2% of the initial capacity when the current density increases by 15 times. Furthermore, the as-assembled NSR-Ni3Se2 device exhibited an ultrahigh energy density of 56.4 Wh/kg and high power density of 4640.3 W/kg, and a capacity retention of 92.6% even after 6000 cycles.

International audience; Visible nanocrystal-based light-emitting diodes (LEDs) are about to become commercially available. However, their infrared counterparts suffer from two key limitations. First, III–V semiconductor technologies are strong competitors. Second, their potential for operation beyond 1.7 µm remains unexplored. The range from 1.5 to 4 µm corresponds to a technological gap in which the efficiency of interband quantum-well-based devices vanishes and quantum cascade lasers are not efficient enough. Powerful infrared LEDs in this range are needed for applications such as active imaging, organic molecule sensing and airfield lighting. Here we report the design of a HgTe nanocrystal-based LED with luminescence between 2 and 2.3 µm. With an external quantum efficiency of 0.3% and radiance up to 3 W Sr−1 m−2, these HgTe LEDs already present a competitive performance for emission above 2 µm.

To prevent postoperative skin tumor recurrence and repair skin wound, a glucose oxidase (GOx)-loaded manganese silicate hollow nanospheres (MS HNSs)-incorporated alginate hydrogel (G/MS-SA) was constructed for starvation-photothermal therapy and skin tissue regeneration. The MS HNSs showed a photothermal conversion efficiency of 38.5%, and endowed composite hydrogels with satisfactory photothermal effect. Taking advantage of the catalytic activity of Mn ions, the composite hydrogels could decompose hydrogen peroxide (H2O2) into oxygen (O2), which can alleviate the problem of tumor hypoxia microenvironment and endow GOx with an ability to consume glucose in the presence of O2 for tumor starvation. Meanwhile, hyperthermia triggered by near infrared (NIR) irradiation could not only accelerate the reaction rate of H2O2 decomposition by MS HNSs and glucose consumption by GOx, but also ablate tumor cells. The anti-tumor results showed that synergistic effect of starvation-photothermal therapy led to the highest death rate of tumor cells among all groups, and its anti-tumor effect was obviously improved as compared with that of single photothermal treatment or starvation treatment. Interestingly, the introduction of MS HNSs into hydrogels could distinctly promote the epithelialization of the wound beds by releasing Mn ions as compared with the hydrogels without MS HNSs. It is expected that such a multifunctional platform with starvation-photothermal therapy will be promising for treating tumor-caused skin defects in combination of its regeneration bioactivity in the future., Graphical abstract Schematic diagram. Schematic illustrations of (a) the fabrication of GOx-loaded MS HNSs-incorporated hydrogels (G/MS-5SA) and (b) the application of G/MS-5SA hydrogels in starvation-photothermal therapy and skin tissue regeneration.Image 1, Highlights • Glucose oxidase-loaded manganese silicate hollow nanospheres-incorporated alginate hydrogels consumed glucose in the presence of O2 for tumor starvation. • Hyperthermia could accelerate the reaction rate of H2O2 decomposition. And starvation-photothermal synergetic anti-tumor therapy effect was better than that of single treatment. • The composite hydrogels promote the epithelialization of the wound beds by releasing Mn ions.

In this work, we used the selective laser melting (SLM) fabricated Co-Cr alloy with prominent residual strain, extremely non-equilibrium microstructures, and low stacking fault energy as a precursor to fabricate materials with the optimal grain boundary character distribution. The grain boundary engineering (GBE) of the Co-Cr alloy was achieved by a simple heat treatment of the SLM-fabricated Co-Cr alloy. The obtained GBE Co-Cr alloy exhibited 81.47% of special grain boundaries (Σ3n n = 1, 2, 3)), while it substantially disrupted the connectivity of the random high-angle boundaries, successfully reducing the propensity of intergranular degradation. Slow strain rate tests (SSRTs) showed that the GBE Co-Cr alloy possessed lower stress corrosion cracking (SCC) susceptibility and higher ductility in the corrosive environment (0.9% NaCl solution) than in the air. The high fraction of special boundaries, coupled with the stress-induced martensitic transformation (SIMT) in the GBE Co-Cr alloy yielded these results, which unique and rarely simultaneously satisfied for common structural materials. The current “SLM induced GBE strategy” offers a novel approach towards customized GBE materials with high SCC resistance and ductility in the corrosive environment, shedding new light on developing high-performance structural materials.

A HAT based large PAH discotic molecule PN8 is developed. The enlarged chromophoric core and doping heteroatoms enable colorimetric and fluorometric sensing of Cu2+ and Zn2+ with highly appreciable optical changes, good selectivity and low detection limit. Moreover, PN8 was demonstrated as an excellent adsorbent to remove Cu2+ and Zn2+ from wastewater.

Multi-Unmanned Aerial Vehicle (UAV) systems with high-resolution cameras have been found useful for operations such as smart city and disaster management. These systems feature Flying Ad-Hoc Networks (FANETs) that connect the computation edge with UAVs and a Ground Control Station (GCS) through air-to-ground wireless network links. Leveraging the edge/fog computation resources effectively with energy-latency-awareness, and handling intermittent failures of FANETs are the major challenges in supporting video processing applications. In this paper, we propose a novel “DroneCOCoNet” framework for drone video analytics that coordinates intelligent processing of large video datasets using edge computation offloading and performs network protocol selection based on resource-awareness. We present two edge computation offloading approaches, i.e., heuristic-based and reinforcement learning-based approaches. These approaches provide intelligent task sharing and co-ordination for dynamic offloading decision-making among UAVs. Our scheme handles the problem of computation offloading tasks in two separate ways: (i) heuristic decision-making process, and (ii) Markov decision process; wherein we aim to minimize the total computation costs as well as latency in the edge/fog resources while minimizing video processing times to meet application requirements. Our experimental results show that our heuristic-based offloading decision-making scheme enables lower scheduling time and energy consumption for low drone-to-ground server ratios. In comparison, our dynamic reinforcement learning-based decision-making approach increases the accuracy and saves overall time periodically. Notably, these results also hold in various other multi-UAV scenarios involving largely different numbers of detected objects in e.g., smart farming, transportation traffic flow monitoring and disaster response.

This study aims to investigate the role of nano TiO2 in modifying the characteristics of Shanghai clayey silt – alluvial soil in Yangtze river estuary. The Shanghai clayey silt is first mixed with nano TiO2 with different size group and content before it is made to undergo liquid and plastic limit tests, standard Proctor compaction tests, and acid-resistant tests. The results show that nano TiO2 can substantially increase the liquid limit and plastic limit of TiO2-treated clayey silt, but can decrease the plasticity index of it to some degree. The result from standard Proctor compaction tests shows that the maximum dry bulk density (MBD) decreases and the optimum water content (OWC) increases compared with untreated samples. Acid-resistance of sample is significantly increased after being treated by nano titanium dioxide. The data provided by this study can be used for not only the soil and water conservation, but also for soil improvement, diversity of vegetables and animals, amelioration of crop land, as well as sustainable development.

To improve the tracking performance of the current controllers of permanent-magnet synchronous motor (PMSM) drive systems that are subject to internal disturbances, such as parameter variations, a novel active-disturbance-rejection-based sliding-mode current control (ADR-SMCC) scheme for PMSM drives is proposed in this article. First, a fast-response SMCC is designed based on the upper bound of the internal disturbance. Then, an extended state observer (ESO) is designed to estimate the internal disturbance in real time without the need for an accurate mathematical model of the PMSM. The parameters of the ESO can be easily designed based on the desired bandwidth of the ESO. The estimated internal disturbance is then used to update the control law of the SMCC in real time. The resulting ADR-SMCC has improved steady-state and transient current tracking performance and enhanced robustness to internal disturbances. The stability of the closed-loop PMSM drive system with the ADR-SMCC is proven by the Lyapunov theory. The ADR-SMCC is validated by experimental results for a 200-W salient-pole PMSM drive system.

Gravel and cement can effectively improve the low strength of traditional Earth materials. There have been few studies on test methods for raw soil-based composite admixtures. By introducing the theory of single lattice formula testing, the compressive strength of 10 formulas and 60 modified raw soil cubic specimens were tested. The failure process, failure mode, and compressive strength of specimens were studied. The effects of different formulations of raw soil on strength, peak displacement, and dispersion were analyzed. The results showed that cement content had a significant effect on the compressive strength and dispersion of earth material specimens. The optimal modification formula of cement, gravel, and raw soil was determined to be 0.1/0.08/0.82 (mass ratio). At the same time, the applicability of the test method was verified, which can be used as a reference for the experimental study on modification formulations of earth materials.

In the current report, characteristics of the propagated waves in a multi-hybrid nanocomposite (MHC) reinforced doubly curved panel is investigated. First-order shear deformable theory (FSDT) is ap...

The porous g-C3N4 (PCN) nanosheets are successfully synthesized and further modified with nano-sized Ag by a simple wet-chemical process. Interestingly, the Ag-modified porous g-C3N4 (Ag-PCN) nanosheets exhibit competitive fluorescence detection performance of chloride ion (Cl−) in aqueous solution. Under the optimized conditions, the concentration of Cl− could be quantitative analyzed with the Ag-PCN in a wide detection range from 0.5 mmol/L to 0.1 mol/L, with a low detection limitation of 0.06 mmol/L. It is confirmed that the fluorescence of PCN could be effectively decayed by the photoinduced charge transfer via the adsorbed Cl− for trapping holes, mainly by means of the time-resolved fluorescence and surface photovoltage spectra. The porous structure and modified Ag promote the adsorption of Cl− on resulting Ag-PCN, leading to excellent fluorescence detection for Cl−. This work provides a feasible route to develop a fluorescence detection of Cl− with g-C3N4 nanosheets in environment water.

Metasurfaces have been extensively investigated to achieve radar cross section (RCS) reduction over a wide frequency band. In this article, we propose the design of a novel metasurface with a transmission window within the wide RCS reduction band. The function of this metasurface is similar to rasorbers, whereas the mechanism behind the wideband RCS reduction is scattering cancellation rather than absorption. To this end, the ideal reflection and transmission properties of the metasurface unit cell are firstly revealed. And then an anisotropic unit cell composed of multilayered metallic patterns is designed to obtain the desired reflection and transmission properties. The bottom layer is composed of the typical patch-grid-patch bandpass frequency selective surface, which allows for high transmission in the transparent window and also behaves as a metallic-like reflection sheet in the RCS reduction band. Two metasurfaces, with checkerboard and aperiodic configurations, respectively, were designed, fabricated, and measured, as a proof-of-concept demonstration. A good agreement between the simulations and measured results provides a solid verification for our design. The results here provide an alternative method to open a transparent window within the RCS reduction band and may find potential applications in stealth radomes.

Lithium ion batteries have been widely used in the power-driven system and energy storage system. While thermal safety for lithium ion battery has been constantly concerned all over the world due to the thermal runaway problems occurred in recent years. Lithium ion battery has high temperature sensitivity and the relatively narrow operating temperature range because of the complex electrochemical reactions at different temperatures. And the temperature change, including the global temperature change in different seasons and the local temperature rise that is induced by its self-heating etc., can trigger side reactions and then lead to thermal runaway, which should be further considered to ensure thermal safety of lithium ion battery. This review summarizes the inducements of thermal runaway and relevant solutions, spanning a wide temperature range. The low temperature induced issues, such as capacity fade and lithium plating and dendrite, can cause internal short circuit (ISC), while as the temperature is above the critical temperature, the speeding of side reactions and reduction of lifespan (T ​> ​40 ​°C) and thermal runaway (T ​> ​90 ​°C) will be triggered. In order to solve the thermal issues in batteries, extensive approaches have been investigated to prevent the occurrence, propagation and deterioration of thermal runaway, from the perspective of material to the battery system. The triggered mechanism at a wide temperature range, key factors for thermal safety and the effective heat dissipation strategies are concluded in this review. This review is expected to offer effective thermal safety strategies and promote the development of lithium ion battery with high-energy density.

An enhanced linear active disturbance rejection controller (ELADRC) based rotor position sensorless field-oriented control scheme for the permanent magnet synchronous motor (PMSM) drivers is proposed in this article. The ELADRC consists of two linear extended state observers (LESOs) and a proportional current controller. One LESO is designed to estimate the back electromotive force (EMF), which is treated as the external disturbance. Then, the rotor position and speed are obtained from the estimated back EMF without any phase delay or chattering problem. The other LESO is designed to estimate the internal disturbances, such as the parameter and current regulation quality variations. The estimated total disturbance is used as a feedforward compensation term in the current control loop to improve the current regulation quality of the plant, which further improves the rotor position estimation performance. The plant combined with the two LESOs is equivalent to an integrator with a unity gain, which is controlled by a simple proportional current controller to generate the desired voltage vector for the pulsewidth modulation operation. Finally, the stability of the closed-loop PMSM drive system with the ELADRC-based scheme is analyzed. Based on the analysis, the parameters of the ELADRC are designed. The proposed scheme is validated by the experimental results for a 275-W salient-pole PMSM drive in which the PMSM is similar to the traction motor used in Toyota Prius hybrid electric vehicles at a reduced scale.

This article aims at high-gain and wide-angle scanning lens antenna design in millimeter-wave (MMW) band. The proposed antenna consists of a novel gradient-index (GRIN) lens and a phased array antenna (PAA) feed with aperture-coupled microstrip antenna (ACMA) elements. A new design methodology of the GRIN lens antenna is proposed, which comprises a preliminary design for rough shapes of GRIN lenses and two successive optimization steps. In the first optimization step, parameters of the GRIN lens are determined to achieve high directivity and wide scanning angle based on particle swarm optimization (PSO) algorithm and a quasi-2-D model. In the second step, the excitation coefficients of all elements for beam-steering purpose are obtained by the PSO algorithm with the goal of maximum directivity. To validate the approach, an MMW lens antenna, operating at 28 GHz, is numerically demonstrated with high directivity and a wide scanning range up to ±58°. Finally, the proposed antenna is fabricated by 3-D printing techniques. The measured results are in acceptable agreement with simulations.

In this paper, the solubility of chlorpropamide (CPA) in 14 solvents including 8 alcohols (ethanol, n-propanol, isopropanol, n-butanol, 2-butanol, iso-butanol, n-pentanol, and isopentanol) and 6 ac...

SnO2 is an attractive anodic material for advanced lithium-ion batteries (LIBs). However, its low electronic conductivity and large volume change in lithiation/delithiation lead to a poor rate/cycling performance. Moreover, the initial Coulombic efficiencies (CEs) of SnO2 anodes are usually too low to build practical full LIBs. Herein, a two-step hydrothermal synthesis and pyrolysis method is used to prepare a SnO2/C nanocomposite, in which aggregated SnO2 nanosheets and a carbon network are well-interpenetrated with each other. The SnO2/C nanocomposite exhibits a good rate/cycling performance in half-cell tests but still shows a low initial CE of 45%. To overcome this shortage and realize its application in a full-cell assembly, the SnO2/C anode is controllably prelithiated by the lithium-biphenyl reagent and then coupled with a LiCoO2 cathode. The resulting full LIB displays a high capacity of over 98 mAh g-1LCO in 300 cycles at 1 C rate.

In this work, the solubility of bezafibrate in 16 pure solvents including 9 alcohols (methanol, ethanol, n-propanol, iso-propanol, n-butanol, iso-butanol, sec-butanol, 1-pentanol, and iso-pentanol)...

Distance measures of fuzzy sets have been developed for feature selection and finding redundant features in the fields of decision-making, prediction, and classification problems. Terms commonly used in the definition of fuzzy sets are normal and convex fuzzy sets. This paper extends the general fuzzy set definitions to subnormal and non-convex fuzzy sets that are more precise when implementing uncertain knowledge representations by weighing fuzzy membership functions. A distance measure method for subnormal and non-convex fuzzy sets is proposed for embedded feature selection. Constructing fuzzy membership functions and extracting fuzzy rules play a critical role in fuzzy classification systems. The weighted fuzzy membership functions prevent the combinatorial explosion of fuzzy rules in multiple fuzzy rule-based systems. The proposed method was validated by a comparison with two other methods. Our proposed method demonstrated higher accuracies in training and test, with scores of 97.95% and 93.98%, respectively, compared to the other two methods.

The efficient utilization of solar energy through photocatalysis is ideal for solving environmental issues and the development sustainable future. BiOBr-based semiconductors possess unique narrowed bandgaps and layered structures, thereby widely studied as photocatalysts for environmental remediation. However, a little has been focused on the comprehensive reviewing of BiOBr despite its extensive and promising applications. In this review, the state-of-the-art developments of BiOBr-based photocatalysts for environmental remediation are summarized. Particular focus is paid to the synthetic strategies for the control of the resulting morphologies, as well as efficient modification strategies for improving the photocatalytic activities. These include boosting the bulk phase by charge separation, enhancing the spatial charge separation, and engineering the surface states. The environmental uses of BiOBr-based photocatalysts are also reviewed in terms of purification of pollutants and CO2 reduction. Finally, future challenges and opportunities of BiOBr-based materials in photocatalysis are discussed. Overall, this review provides a good basis for future exploration of high-efficiency solar-driven photocatalysts for environmental sustainability.

This paper reports a fresh and robust strategy to develop polyurethane/polysiloxane pressure-sensitive adhesives (PSAs) with excellent properties by replacing part of C5 petroleum resin with modified lignin. A unique aspect of this work is the use of renewable lignin to obtain modified monomers. The phenolic hydroxyl group of lignin is increased by 21.4% after demethylation, which will help to introduce 6-bromo-1-hexene into the lignin structure through Williamson method. The L3 lignin and C5 petroleum resin are mixed with polyurethane/polysiloxane prepolymer, and furthermore a series of PSAs are obtained under ultraviolet light. It turns out that L3 lignin can not only replace part of C5 petroleum resin, but also obtain attractive and controllable features. Especially when the mass ratio of C5 petroleum resin to L3 lignin is 6:4, compared with pure C5 petroleum resin, the 180° peel strength and the shear strength of PU46 are increased by 24.1% and 91.5% respectively. Additionally, the shear strength on the wood substrate is increased by 320.6%. This study provides an effective method for the preparation of high value-added lignin PSA, and expands the application fields of PSA.

In this paper, a novel D eep M ulti-view J oint C lustering ( DMJC ) framework is proposed, where multiple deep embedded features, multi-view fusion mechanism, and clustering assignments can be learned simultaneously. Through the joint learning strategy, the clustering-friendly multi-view features and useful multi-view complementary information can be exploited effectively to improve the clustering performance. Under the proposed joint learning framework, we design two ingenious variants of deep multi-view joint clustering models, whose multi-view fusion is implemented by two kinds of simple yet effective schemes. The first model, called DMJC-S, performs multi-view fusion in an implicit way via a novel multi-view soft assignment distribution. The second model, termed DMJC-T, defines a novel multi-view auxiliary target distribution to conduct the multi-view fusion explicitly. Both DMJC-S and DMJC-T are optimized under a KL divergence objective. Experiments on eight challenging image datasets demonstrate the superiority of both DMJC-S and DMJC-T over single/multi-view baselines and the state-of-the-art multi-view clustering methods, which proves the effectiveness of the proposed DMJC framework. To the best of our knowledge, this is the first work to model the multi-view clustering in a deep joint framework, which will provide a meaningful thinking in unsupervised multi-view learning.

The crossflow is the key phenomenon in turbulent flow inside rod bundles. In order to establish confidence on application of computational fluid dynamics (CFD) to simulate the crossflow in rod bundles, three Reynolds-Averaged Navier Stokes (RANS) models i.e. the realizable k-e model, the k-ω SST model and the Reynolds stress model (RSM), and the Large Eddy simulations (LES) with the Wall-Adapting Local Eddy-viscosity (WALE) model are validated based on the Particle Image Velocimetry (PIV) flow measurement experiment in a 5 × 5 rod bundle. In order to investigate effects of periodic boundary condition in the gap, the numerical results obtained with four inner subchannels are compared with that obtained with the whole 5 × 5 rod bundle. The results show that periodic boundaries in the gaps produce strong errors far downstream of the spacer grid, and therefore the full 5 × 5 rod bundle should be simulated. Furthermore, it can be concluded, that the realizable k-e model can only provide reasonable results very close to the spacer grid, while the other investigated models are in good agreement with the experimental data in the whole downstream flow in the rod bundle. The LES approach shows superiority to the RANS models.

In this work, UV light activated multi-cycle photoelectric properties of TiO2 and CdS/TiO2 films in formaldehyde were researched. TiO2 film was prepared by screen printing, CdS/TiO2 compounded film was synthesized by SILAR method. XRD and FE-SEM was used to characterize the TiO2 and CdS/TiO2 samples. Multi-cycle photoelectric properties of TiO2 and CdS/TiO2 with uv light on and off were evaluated by testing the photocurrent. On one hand, under the same bias voltage, CdS/TiO 2showed a higher photocurrent than that by TiO2. The reason for this result should be ascribed to the compounded structure in CdS/TiO2, with which the separation and transfer of photogenerated electron-hole pairs could be improved. On the other hand, with the testing cycle number increased, the photocurrent amplitudes of TiO2 and CdS/TiO2 increased. These results suggested that the time to reach a stable photocurrent value for TiO2 and CdS/TiO2 is much longer than one cycle time (300 S). To illustrate the increased photocurrent amplitude value cycle by cycle, the photocurrent of CdS/TiO2 to a much longer time (more than 4000 seconds) was also tested. To explain these results, corresponding possible illustrations were presented.

Hybrid supercapacitors have the advantages of fast charging and discharging and long service life, which are an efficient and practical energy storage device. Therefore, the design of hybrid supercapacitors is the focus of current research. In this paper, the silver modified spinel NiCo2S4 nanorods (Ag2S-NiCo2S4/CF) are synthesized by an efficient and economical method, which has excellent electrochemical performance. The Ag2S-NiCo2S4/CF shows a high specific capacity of 179.7 mAh g−1 at current density of 1 A g−1, and excellent rate capability (capacitance retention of ~87% at 20 A g−1). The corresponding Ag2S-NiCo2S4/CF//AC/CF hybrid supercapacitor is assembled by Ag2S-NiCo2S4/CF as the positive electrode, which can provide an energy density of 35.978 Wh kg−1 at a high-power density of 800 W kg−1 and has significant cyclic stability (~80% of the initial capacitor after ~9600 cycles). Therefore, Ag2S-NiCo2S4/CF material is a promising electrode material that can be applied to hybrid supercapacitors.

The rapid recombination of photogenerated charges is one of the main restriction for promoting the photocatalytic H2 generation of graphitic carbon nitride (CN) material. Herein, donor-acceptor (D-A) system was introduced into CN nanosheets by oxygen and/or phenyl doping (DA-CN) strategy to facilitate the transport of photoinduced charge carriers and H2 generation. Experimental and theoretical results revealed that the nanosheet structure of DA-CN shortened the photoexcited charges transport length to the surface, and the D-A system embedded in DA-CN provided the dipole-induced internal electric field for charges transport. As a consequence, compared with pristine CN, DA-CN samples performed the improved transport of photogenerated charges and photocatalytic H2 evolution. Notably, DA-CN-OP (oxygen and phenyl co-doping) with the strongest dipole-induced internal electric field originated from D-A system displayed the highest photocatalytic H2 evolution rate at 7.394 mmol g−1h−1, which was 7.67 times as that of pristine CN (0.964 mmol g−1h−1). This work not only provides a simple strategy to construct highly efficient CN nanosheet photocatalyst with D-A system, but also promote the deep insight into the effect of molecular dipole originated from D-A system on the transport of photoinduced charge carriers and photocatalytic activity for CN material.