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Zn vacancy ZnIn2S4 tuned Gibbs free energy of *COOH which is the rate determining step of phototcatalytic CO2 reduction from endothermic to exothermic process.

The crude oil spill accidents cause numerous crude oil contaminations and oily wastewater. Underwater superoleophobic coating has excellent ability to resist crude oil contamination and separate oily wastewater. But it's hard to keep stable performance against the physical or chemical attack. Herein, a robust underwater superoleophobic coating was fabricated by spraying the mixture of polyethyleneimine (PEI) and TiO

This study exhibits potential prospects in thermal management fields and promising alternatives for the thermal harvesting of renewable energy.

Due to their environmental protection and high power generation efficiency, the control technology of hydrogen fuel cells (HFCs) connected to the microgrid has become a research hotspot. However, when they encounter peak demand or transient events, the lack of power cannot be compensated immediately by HFCs which results in sudden changes of the voltage and frequency. The improved virtual synchronous generator (VSG) control strategy based on HFCs and supercapacitors (SCs) combined power generation system is proposed to overcome this shortcoming in this paper. The small-signal model for designing the combined system parameters is provided which are in accordance with the system loop gain, phase angle margin, and adjustment time requirements. Besides, the voltage and current double closed-loop based on sequence control is introduced in the VSG controller. The second-order generalized integrator (SOGI) is utilized to separate the positive and negative sequence components of the output voltage. At the same time, a positive and negative sequence voltage outer loop is designed to suppress the negative sequence voltage under unbalanced conditions, thereby reducing the unbalance of the output voltage. Finally, simulation results in MATLAB/Simulink environment verify that the proposed method has better dynamic characteristics and higher steady-state accuracy compared with the traditional VSG control.

Highly thermally conductive polymer-based composites are becoming increasingly important for effectively removing the accumulated heat of thermal management devices. However, interfacial thermal resistance (ITR) seriously affects the heat transfer performance of composites. Herein, after mildly modifying the commercial graphite, copper nanoparticles (CuNPs) are deposited on the surface of modified graphite (MGr) by chemically reducing. And the CuNPs-deposited MGr (MGr/Cu) hybrids are mixed with epoxy resin to prepare composites. Through the improved interfacial contact and interconnection between CuNPs and MGr, the heat conduction pathways will be easy to form in epoxy matrix. And the MGr/Cu hybrids exhibit well compatibility with epoxy matrix. Consequently, the resultant composite exhibits a high thermal conductivity of 4.57 W m−1 K−1 at 50 wt% MGr/Cu loading. Fitting the experimental value with Foygel nonlinear model further reveals that the ITR is 3.92 × 10−6 m2 K W−1 for MGr/Cu/epoxy composites, which decreases by 27.94% than that of Gr/epoxy composites. Moreover, our composite displays well heat dissipation potential in thermal management application. This strategy provides an effective guidance for reducing the ITR inside polymer composites to prepare thermal management materials. The thermal conductivity is improved via thermally conductive Cu bridges between modified graphite and the improved compatibility between graphite and epoxy.

Silicon is a promising photocathode material in photoelectrochemical water splitting for hydrogen production, but it is primarily limited by photocorrosion in aqueous electrolytes. As an extensively used protective material, crystalline TiO2 could protect Si photoelectrode against corrosion. However, a large number of grain boundaries (GBs) in polycrystalline TiO2 would induce excessive recombination centers, impeding the carrier transport. This paper describes the introduction of oxygen vacancies (Ovac ) with controllable spatial distribution for GBs to promote carrier transport. Two kinds of Ovac distribution, Ovac along GBs and Ovac inside grains, are compared, where the latter one is demonstrated to facilitate carrier transport owing to the formation of tunneling paths across GBs. Consequently, a simple p-Si/TiO2 /Pt heterojunction photocathode with controllable Ovac distribution in TiO2 shows a +400 mV onset potential shift and yields an applied bias photon-to-current efficiency of 5.9 %, which is the best efficiency reported among silicon photocathodes except for silicon homojunction.

The composites with high thermal conductivity (TC) have attracted extensive attention in thermal management fields. Incorporating a three dimensional (3D) thermal transport framework in polymer is one of the most effective ways to improve the TC of composites. However, most micro-zone of those 3D thermal transport framework is filled with polymers with very low TC, leading to limited TC improvement of polymer composites. Herein we presented a novel 3D boron nitride (BN)/nickel foam (NF)/epoxy (EP) composite with high TC. Results indicated that as a good reinforced filler with high TC, BN was homogeneously dispersed in epoxy and fully filled into 3D NF micro-zone to get obvious TC increase of the BN/NF/EP composites. With the micro-zone enhancement strategy, the prepared BN/NF/EP-50 composite demonstrated excellent thermal conductive property. The obtained TC (5.14 W m−1 K−1) is approximately 24 and 5 times higher than that of epoxy and NF/EP composite, respectively. This research potentially provides a unique way to design and fabricate high TC 3D materials with the micro-zone enhancement strategy.

CeO2 was used as the only oxidant for aniline polymerization, and polyaniline (PANI) was grown on CeO2 surface to form CeO2@PANI nanocomposites. SEM and TEM showed that the CeO2@PANI composites were nanorods in a core-shell structure. XPS indicated that CeO2-core was reduced by aniline into Ce3+, which was then captured by the PANI-shell. Then the passive ability of CeO2@PANI coating on steels was explored. EIS showed that the impedance modulus of the CeO2@PANI coating exceeded 108 Ω·cm2 after accelerated immersion. The SVET and XPS confirmed that PANI polarized the anodic reaction, Ce3+ suppressed the cathodic reaction, and PO43- formed complex precipitation. Some high-valent metal oxides can directly oxidize the aniline polymerization to form MO@PANI hybrid particles. The cations obtained by the metal oxide reduction can be captured by PANI and participate in passivation together with dopants. This study provides a new kind of nanocomposite fillers that can effectively passivate steel substrates within epoxy coatings for long-time protection.

For power system transient stability assessment (TSA), the consequences of misclassifications of unstable samples are more serious than those of stable samples. Therefore, the cost-sensitive method is introduced into TSA. A constant weight is set for all unstable training samples by the conventional cost-sensitive method. However, the fault severities of the training samples are different. Thus, the impacts of the samples with different fault severities on assessment rules are also different. Therefore, an improved cost-sensitive method based on the fault severity is proposed. Firstly, the fault severity of each sample is calculated by its fault clearing time. On this basis, different cost-sensitive coefficients are assigned to the samples with different fault severities. The weights of critical samples are larger than those of non-critical ones. Then, a stacked sparse auto-encoder model based on the improved cost-sensitive method is built through modifying the loss function. Finally, the proposed method is tested in an actual system. Compared with the traditional cost-sensitive method, the results show that the improved cost-sensitive method has better performance. Also, it has the advantages of the low false rate of unstable samples, high-assessment accuracy, and strong anti-noise ability.

The real-time transient stability assessment (TSA) is critical for emergency control of power systems. Accurate and fast TSA can provide an important basis for post-fault control of power systems. At present, the accuracy of the evaluation model based on machine learning is very high, but there are still some misjudgements in the results. In order to build a high-accuracy evaluation model, a novel frame based on the cost-sensitive method is proposed. Firstly, a deep belief network (DBN) is applied to build a TSA frame. The DBN is effectively trained by means of pre-training and fine tuning. Secondly, two models with the opposite preference are constructed based on the cost-sensitive method. Based on the output results of the two models, the deterministic or uncertain evaluation results are obtained. The samples that may be misclassified are divided into uncertain evaluation results. Thus, the accuracy of the deterministic evaluation results can be improved greatly. Through this design, the practicability of the evaluation model based on machine learning is greatly improved. The effectiveness of the proposed scheme is verified by the simulation results in the IEEE-39 bus system and a realistic regional system.

With the rapid development of machine learning technology, a new tool is provided for real-time stability evaluation in power systems. The training of a machine learning-based model is inseparable from a large number of training samples. However, compared with stable samples, unstable samples in power systems are infrequent. The results of the model evaluation are biased due to the imbalance of training samples. Faced with such a problem, a framework based on deep imbalanced learning is proposed. Firstly, for each sample, the samples nearby in the opposite class are applied to calculate its space information. Based on the space information of all samples, the spatial distribution characteristics of the training samples are obtained. And then, in order to obtain balanced training samples, unstable samples are generated according to their spatial distribution characteristics. Finally, stacked sparse denoising auto-encoder (SSDAE) based model, which has the ability of anti-noise, is established as the classifier. Simulation results in IEEE 39-bus system show the high performance of the proposed imbalanced correction scheme and evaluation scheme.

The grain boundary in copper-based electrocatalysts has been demonstrated to improve the selectivity of solar-driven electrochemical CO2 reduction toward multicarbon products. However, the approach to form grain boundaries in copper is still limited. This paper describes a controllable grain growth of copper electrodeposition via poly(vinylpyrrolidone) used as an additive. A grain-boundary-rich metallic copper could be obtained to convert CO2 into ethylene and ethanol with a high selectivity of 70% over a wide potential range. In situ attenuated total reflection surface-enhanced infrared absorption spectroscopy unveils that the existence of grain boundaries enhances the adsorption of the key intermediate (*CO) on the copper surface to boost the further CO2 reduction. When coupling with a commercially available Si solar cell, the device achieves a remarkable solar-to-C2-products conversion efficiency of 3.88% at a large current density of 52 mA·cm-2. This low-cost and efficient device is promising for large-scale application of solar-driven CO2 reduction.

Denial of Service (DoS) attack is a serious threat to Software Defined Network (SDN). Although many research efforts have been devoted to identify new features for DoS attack detection, the existing approaches are not able to detect various types of DoS attacks. In SDN, DoS attacks against data plane are mainly organized in two ways: 1) DoS attack with multiple flow entries (M-DoS) to exhaust the Ternary Content-Addressable Memory (TCAM) resource of the switch. 2) DoS attack with a single well-designed entry (S-DoS) to overwhelm the target link and further impact the controller. To detect these two attacks, we propose a new approach by extracting six features of flow table, and using the back propagation (BP) neural network to construct the classifier. Test results of test-bed experiments indicate that the accurate detection probability of proposed approach is 98.9%, which can effectively distinguish M-DoS flows and S-DoS flows without being affected by Flash crowd scene.

Due to the requirement of safety and reliability in power systems, unstable samples in the real system are rarely appeared. The evaluation results of the model trained by these imbalance samples have a certain preference. Generally, the imbalance in quantity is taken into account, while the imbalance in quality is ignored. Faced with such a problem, an imbalanced correction method based on support vector machine (SVM) is proposed. Firstly, the classification hyperplane trained by SVM and the normalized Euclidean distance between each sample and the classification hyperplane are calculated so as to obtain their fault severity. Based on this, training samples can be grouped to multilevel sets. Then, the original stacked sparse auto-encoder (SSAE) are pretrained to quantify the imbalance between two classes of samples in multilevel sets. Subsequently, in order to improve the imbalance of training samples, a cost-sensitive correction matrix is generated according to the imbalanced information of multilevel sets. Finally, the loss function of SSAE is modified by cost-sensitive correction matrix to establish the final classifier. Simulation results in IEEE 39-bus system and the realistic regional power system of Eastern China show the high performance of the proposed imbalanced correction method.

Hepatocellular carcinoma is one of the deadliest neoplasms around the world, and a major proportion of patients are diagnosed in an advanced state not amenable to curative treatment. Lenvatinib, a promising first-line targeted therapy, has shown antitumour activity in both preclinical studies and clinical trials. Emerging evidence indicates that a combination of lenvatinib plus anti-PD-1 inhibitors or locoregional therapies exerts a stronger antitumour effect than monotherapy and even offers the possibility of long-term survival while maintaining acceptable tolerability. Several studies have also shown the superiority of lenvatinib over sorafenib in combination strategies. This review addresses the rationale behind lenvatinib-based combination therapies and comprehensively summarizes various clinical studies investigating lenvatinib in combination with immune checkpoint inhibitors (ICIs), locoregional therapies, and other systemic treatments. We discuss the unsatisfactory search for suitable biomarkers and key ongoing trials in this field.