Elaboration and characterization of nanoplate structured alpha-Fe2O3 films by Ag3PO4

A new strategy for surface treatment of hematite nanoplates for efficient photoelectrochemical (PEC) performances is proposed. Silver orthophosphate (Ag₃PO₄) has been adopted to mediate the formation of α-Fe₂O₃ films. Phosphate ions in Ag₃PO₄ is found to cause a significant morphology change during annealing process, from β-FeOOH nanorod arrays to hematite nanoplates. Meanwhile, Ag ions is doped into α-Fe₂O₃ film. The obtained nanoplate structured Fe₂O₃ –Ag–P films demonstrate much higher photoelectrochemical performance as photoanodes than the bare Fe₂O₃ nanorod thin films. The effects of phosphate and silver ions on the morphology, surface characteristics and the PEC properties of the photoanodes are investigated

Self-assembly of 3D fennel-like Co3O4 with thirty-six surfaces for high performance supercapacitor

Three-dimensional (3D) fennel-like cobalt oxide (II,III) (Co3O4) particles with thirty-six surfaces on nickel foams were prepared via a simple hydrothermal synthesis method and its growth process was also researched. The crystalline structure and morphology were investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman spectroscopy. The Brunauer-Emmett Teller (BET) analysis revealed that 3D fennel-like Co3O4 particles have high specific surface area. Therefore, the special structure with thirty-six surfaces indicates the good electrochemical performance of the micron-nanometer material as electrode material for supercapacitors. The cyclic voltammetry (CV), galvanostatic charge-discharge, and electrochemical impedance spectroscopy (EIS) were conducted to evaluate the electrochemical performances. Compared with other morphological materials of the similar sizes, the Co3O4 particles on nickel foam exhibit a high specific capacitance of 384.375 F.g(-1) at the current density of 3A.g(-1) and excellent cycling stability of a capacitance retention of 96.54% after 1500 galvanostatic charge-discharge cycles in 6M potassium hydroxide (KOH) electrolyte

Cross-level Attention with Overlapped Windows for Camouflaged Object Detection

Camouflaged objects adaptively fit their color and texture with the environment, which makes them indistinguishable from the surroundings. Current methods revealed that high-level semantic features can highlight the differences between camouflaged objects and the backgrounds. Consequently, they integrate high-level semantic features with low-level detailed features for accurate camouflaged object detection (COD). Unlike previous designs for multi-level feature fusion, we state that enhancing low-level features is more impending for COD. In this paper, we propose an overlapped window cross-level attention (OWinCA) to achieve the low-level feature enhancement guided by the highest-level features. By sliding an aligned window pair on both the highest- and low-level feature maps, the high-level semantics are explicitly integrated into the low-level details via cross-level attention. Additionally, it employs an overlapped window partition strategy to alleviate the incoherence among windows, which prevents the loss of global information. These adoptions enable the proposed OWinCA to enhance low-level features by promoting the separability of camouflaged objects. The associated proposed OWinCANet fuses these enhanced multi-level features by simple convolution operation to achieve the final COD. Experiments conducted on three large-scale COD datasets demonstrate that our OWinCANet significantly surpasses the current state-of-the-art COD methods

A Graph Regularized Point Process Model For Event Propagation Sequence

Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. In this paper we aim at modeling latent dynamics of event propagation in graph, where the event sequence propagates in a directed weighted graph whose nodes represent event marks (e.g., event types). Most existing works have only considered encoding sequential event history into event representation and ignored the information from the latent graph structure. Besides they also suffer from poor model explainability, i.e., failing to uncover causal influence across a wide variety of nodes. To address these problems, we propose a Graph Regularized Point Process (GRPP) that can be decomposed into: 1) a graph propagation model that characterizes the event interactions across nodes with neighbors and inductively learns node representations; 2) a temporal attentive intensity model, whose excitation and time decay factors of past events on the current event are constructed via the contextualization of the node embedding. Moreover, by applying a graph regularization method, GRPP provides model interpretability by uncovering influence strengths between nodes. Numerical experiments on various datasets show that GRPP outperforms existing models on both the propagation time and node prediction by notable margins.Comment: IJCNN 202

In situ growth of ultrathin Co-MOF nanosheets on Α-Fe2O3 hematite nanorods for efficient photoelectrochemical water oxidation

Efficient charge transport is an important factor in photoelectrochemical (PEC) water splitting. The charge transfer at the semiconductor/electrolyte interface is of great importance, especially for the complex water oxidation reaction. In this study, we explored the feasibility of improving charge transfer efficiency at the interface of semiconductor/electrolyte by in situ growth of Co based Metal-Organic Frame work (Co-MOF) through a facile ion-exchanging method. Under optimized conditions, the Co-MOF nanosheet-modified hematite gave a photocurrent density of 2.0 mA cm−2 (200% improvement) at 1.23 VRHE with a cathodic shift of 180 mV in the photocurrent onset potential, in comparison to bare α-Fe2O3 (0.71 mA cm−[email protected] VRHE). To elucidate the role of Co-MOF, X-ray photoelectron spectroscopy, electrochemical impedance spectroscopy and Mott-Schottky measurements were carried out. It was found that the atomically distributed Co2+ in Co-MOF possessed excellent hole storage capability and charge transfer efficiency, as evidenced by the high surface capacitance and extremely low surface charge transfer resistance

Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning

In the field of quantitative trading, it is common practice to transform raw historical stock data into indicative signals for the market trend. Such signals are called alpha factors. Alphas in formula forms are more interpretable and thus favored by practitioners concerned with risk. In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together. However, most traditional alpha generators mine alphas one by one separately, overlooking the fact that the alphas would be combined later. In this paper, we propose a new alpha-mining framework that prioritizes mining a synergistic set of alphas, i.e., it directly uses the performance of the downstream combination model to optimize the alpha generator. Our framework also leverages the strong exploratory capabilities of reinforcement learning~(RL) to better explore the vast search space of formulaic alphas. The contribution to the combination models' performance is assigned to be the return used in the RL process, driving the alpha generator to find better alphas that improve upon the current set. Experimental evaluations on real-world stock market data demonstrate both the effectiveness and the efficiency of our framework for stock trend forecasting. The investment simulation results show that our framework is able to achieve higher returns compared to previous approaches.Comment: Accepted by KDD '23, ADS trac

Effect of zinc acetate concentration on optimization of photocatalytic activity of p-Co3O4/n-ZnO heterostructures

In this work, p-Co3O4/n-ZnO heterostructures were fabricated on Ni substrate by hydrothermal-decomposition method using cobaltous nitrate hexahydrate (Co(NO3)(2)center dot 6H(2)O) and zinc acetate dihydrate (Zn(CH3COO)(2)center dot 2H(2)O) as precursors with zinc acetate concentration varying from 5.0 to 55.0 mM. Structure and morphology of the developed samples were characterized by X-ray diffraction (XRD), Raman spectroscopy, and scanning electron microscopy (SEM). Effect of zinc acetate concentration on the photocatalytic activity of p-Co3O4/n-ZnO heterostructures was investigated by degradation of methyl orange (MO) under the UV light irradiation. The fabricated p-Co3O4/n-ZnO heterostructures exhibited higher photocatalytic activity than pure Co3O4 particles. In order to obtain the maximum photocatalytic activity, zinc acetate concentration was optimized. Specifically, at 35 mM of zinc acetate, the p-Co3O4/n-ZnO showed the highest photocatalytic activity with the degradation efficiency of MO reaching 89.38% after 72 h irradiation. The improvement of photocatalytic performance of p-Co3O4/n-ZnO heterostructures is due to the increased concentration of photo-generated holes on Co3O4 surface and the higher surface-to-volume ratio in the hierarchical structure formed by nano-lamellas

Preparation Method of Co 3

Co3O4 nanoparticles were fabricated by a novel, facile, and environment-friendly carbon-assisted method using degreasing cotton. Structural and morphological characterizations were performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The component of the sample obtained at different temperatures was measured by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). Nitrogen adsorption and desorption isotherms were utilized to reveal the specific surface areas. The formation mechanism of Co3O4 nanoparticles was also proposed, demonstrating that the additive degreasing cotton played an indispensable role in the process of synthesizing the sample. The resultant Co3O4 sample calcined at 600°C exhibited superior electrochemical performance with better specific capacitance and long-term cycling life, due to its high specific surface areas and pores structures. Additionally, it has been proved that this facile synthetic strategy can be extended to produce other metal oxide materials (e.g., Fe3O4). As a consequence, the carbon-assisted method using degreasing cotton accompanied a promising prospect for practical application

Progress on Optical Fiber Biochemical Sensors Based on Graphene

Graphene, a novel form of the hexagonal honeycomb two-dimensional carbon-based structural material with a zero-band gap and ultra-high specific surface area, has unique optoelectronic capabilities, promising a suitable basis for its application in the field of optical fiber sensing. Graphene optical fiber sensing has also been a hotspot in cross-research in biology, materials, medicine, and micro-nano devices in recent years, owing to prospective benefits, such as high sensitivity, small size, and strong anti-electromagnetic interference capability and so on. Here, the progress of optical fiber biochemical sensors based on graphene is reviewed. The fabrication of graphene materials and the sensing mechanism of the graphene-based optical fiber sensor are described. The typical research works of graphene-based optical fiber biochemical sensor, such as long-period fiber grating, Bragg fiber grating, no-core fiber and photonic crystal fiber are introduced, respectively. Finally, prospects for graphene-based optical fiber biochemical sensing technology will also be covered, which will provide an important reference for the development of graphene-based optical fiber biochemical sensors

Investigation of methane adsorption mechanism on Longmaxi shale by combining the micropore filling and monolayer coverage theories

Understanding the methane adsorption mechanism is critical for studying shale gas storage and transport in shale nanopores. In this work, we conducted low-pressure nitrogen adsorption (LPNA), scanning electron microscopy (SEM), and high-pressure methane adsorption experiments on seven shale samples from the Longmaxi formation in Sichuan basin. LPNA and SEM results show that pores in the shale samples are mainly nanometer-sized and have a broad size distribution. We have also shown that methane should be not only adsorbed in micropores (< 2 nm) but also in mesopores (2-50 nm) by two hypotheses. Therefore, we established a novel DA-LF model by combining the micropore filling and monolayer coverage theories to describe the methane adsorption process in shale. This new model can fit the high-pressure isotherms quite well, and the fitting error of this new model is slightly smaller than the commonly used D-A and L-F models. The absolute adsorption isotherms and the capacities for micropores and mesopores can be calculated using this new model separately, showing that 77% to 97% of methane molecules are adsorbed in micropores. In addition, we conclude that the methane adsorption mechanism in shale is: the majority of methane molecules are filled in micropores, and the remainder are monolayer-adsorbed in mesopores. It is anticipated that our results provide a more accurate explanation of the shale gas adsorption mechanism in shale formations.Cited as: Zhou, S., Ning, Y., Wang, H., Liu, H., Xue, H. Investigation of methane adsorption mechanism on Longmaxi shale by combining the micropore filling and monolayer coverage theories. Advances in Geo-Energy Research, 2018, 2(3): 269-281, doi: 10.26804/ager.2018.03.0