Repetitive Experiments Research Articles

A recyclable T-ZIF-8/SnO2@PAN photocatalytic porous fibrous membrane used for CO2 reduction

The challenge of separating and recycling high-performance photocatalysts often results in resource wastage and secondary environmental contamination. Therefore, the advancement of recyclable photocatalysts has emerged as a frontier area of interest in the domain of photocatalytic CO2 reduction. In this work, T-ZIF-8/SnO2@PAN-PVP fibrous membranes were constructed with T-ZIF-8/SnO2 as a photocatalyst, polyacrylonitrile (PAN) as a matrix, polyvinylpyrrolidone (PVP) as a porogen utilizing electrospinning technique. By regulating the variable parameters, the effects of spinning solution concentration, conductive salt dosage, porogenic agent dosage and their effects on fiber microscopic morphology were investigated, and the process parameters were optimized to obtain the best microscopic morphology, and then the photocatalytic porous fibers were obtained through the water impregnation treatment. A comparative analysis of the CO2 reduction capability of photocatalytic powders, fibers, and porous fibers was performed, alongside an exploration of the potential reaction mechanism. The results indicate that the T-ZIF-8/SnO2@PAN-PVP porous fiber membrane irradiated by visible light for 4 h demonstrates excellent CO2 reduction efficiency, achieving CO, CH4, and H2 production rates of 14.3, 7.09, and 8.57 μmol·g−1·h−1, respectively. Durability tests through repetitive experiments have validated that the T-ZIF-8/SnO2@PAN/ZnCl2 porous fiber membrane retains over 90 % of its initial photocatalytic activity after four cycles, thereby facilitating the sustainable recycling and reuse of photocatalysts.

Developing Multiple Media Approach to Investigate Reproducible Characteristic VOCs of Lung Cancer Cells.

Cellular volatile organic compound (VOC) detection is crucial for studying lung cancer biomarkers. However, the reported VOC biomarkers from the same cell line seem to be inconsistent across different research groups. It is possibly related to the variation in culture media, and the result obtained by a conventional single medium approach (SMA) depends on what medium is used in the cell experiment. This study proposes a multiple media approach (MMA) to investigate reproducible characteristic VOCs of lung cancer cells. Using solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) in combination with untargeted analysis, we conducted two independently repetitive experiments to compare lung cancer cells (A549) and normal lung cells (BEAS-2B) under three culture media conditions (RPMI 1640, DMEM, and Ham's F12). Both experiments indicated that, compared with 62-96 VOCs obtained by the SMA, only two VOCs (3-methyl-1-butanol and 2-methyl-1-butanol) were reproducibly achieved by the MMA. Moreover, their concentrations were significantly lower in lung cancer cells than in normal cells. Further targeted analysis confirmed the downregulation trend of both VOCs in subcutaneous and primary tumor tissues from the lung cancer model mouse. The present work demonstrated that the MMA cell experiment, just like the multicenter trials for cell lines, can facilitate the discovery of reproducible characteristic VOCs. This provides a cellular experimental basis and scientific evidence for lung cancer biomarker investigation and even breath biopsy technique development.

Evaluation of large language models under different training background in Chinese medical examination: a comparative study.

BackgroundRecently, Large Language Models have shown impressive potential in medical services. However, the aforementioned research primarily discusses the performance of LLMs developed in English within English-speaking medical contexts, ignoring the LLMs developed under different linguistic environments with respect to their performance in the Chinese clinical medicine field.ObjectiveThrough a comparative analysis of three LLMs developed under different training background, we firstly evaluate their potential as medical service tools in a Chinese language environment. Furthermore, we also point out the limitations in the application of Chinese medical practice.MethodUtilizing the APIs provided by three LLMs, we conducted an automated assessment of their performance in the 2023 CMLE. We not only examined the accuracy of three LLMs across various question, but also categorized the reasons for their errors. Furthermore, we performed repetitive experiments on selected questions to evaluate the stability of the outputs generated by the LLMs.ResultThe accuracy of GPT-4, ERNIE Bot, and DISC-MedLLM in CMLE are 65.2, 61.7, and 25.3%. In error types, the knowledge errors of GPT-4 and ERNIE Bot account for 52.2 and 51.7%, while hallucinatory errors account for 36.4 and 52.6%. In the Chinese text generation experiment, the general LLMs demonstrated high natural language understanding ability and was able to generate clear and standardized Chinese texts. In repetitive experiments, the LLMs showed a certain output stability of 70%, but there were still cases of inconsistent output results.ConclusionGeneral LLMs, represented by GPT-4 and ERNIE Bot, demonstrate the capability to meet the standards of the CMLE. Despite being developed and trained in different linguistic contexts, they exhibit excellence in understanding Chinese natural language and Chinese clinical knowledge, highlighting their broad potential application in Chinese medical practice. However, these models still show deficiencies in mastering specialized knowledge, addressing ethical issues, and maintaining the outputs stability. Additionally, there is a tendency to avoid risk when providing medical advice.

Machine Learning-Enabled Nanoscale Phase Prediction in Engineered Poly(Vinylidene Fluoride).

Engineered poly(vinylidene fluoride) (PVDF) with its diverse crystalline phases plays a crucial role in determining the performance of devices in piezo-, pyro-, ferro- and tribo-electric applications, indicating the importance of distinct phase-detection in defining the structure-property relation. However, traditional characterization techniques struggle to effectively distinguish these phases, thereby failing to offer complete information. In this study, multimodal data-driven techniques have been employed for distinguishing different phases with a machine learning (ML) approach. This developed multimode model has been trained from empirical to theoretical data and demonstrates a classification accuracy of >94%, 15% more noise resilience, and 11% more accuracy from unimodality. Thus, from conception to validation, an alternative approach is provided to autonomously distinguish the different PVDF phases and eschew repetitive experiments that saved resources, thus accelerating the process of materials selection in various applications.

Composite Improved Algorithm Based on Jellyfish, Particle Swarm and Genetics for UAV Path Planning in Complex Urban Terrain.

Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima. Additionally, it enhances the path smoothness, which improves the path optimisation. This enhances the capacity of UAVs to optimise their paths in environments characterised by multiple obstacles. To evaluate the practical effectiveness of the algorithm, a three-dimensional complex city model was constructed for the purposes of the study, and an adaptation function was designed for the purpose of evaluation. The experimental evaluation of 23 benchmark functions, the simulation test of the 3D city model, and 100 repetitive experiments demonstrate that the composite improved algorithm has a considerable advantage over the other comparative algorithms regarding performance. It exhibits fast convergence, high accuracy, and both global and local search capabilities, which enable the effective planning of a UAV flight path and the maintenance of good stability. In comparison to traditional algorithms, the composite improved algorithm demonstrably reduces the flight time and the number of obstacle avoidance manoeuvres required by the UAV. It provides robust technical support for the path planning of the UAV in complex urban environments and facilitates the advancement and implementation of related technologies.

Rapid Experimental Optimization of Liquid Electrolytes Compositions for Safe Li-Ion Battery with Outstanding Long-Term Performance: Multi-Objective Constrained Bayesian Optimization Approach

Li-ion batteries have received significant attention from academia and industry to fulfill the steadily growing demand for electric vehicles. The urgency to develop safe and efficient energy storage solutions has prompted extensive research efforts in optimizing battery materials and technologies. However, optimizing the compositions of battery materials via repetitive experiments to achieve high performance can be exceedingly time-consuming and resource-intensive. This challenge is made worse by the lengthy battery cycle life tests, which often span several weeks to months. To address this critical issue, we have employed multi-objective constrained batch Bayesian optimization (BO) for the highly efficient design of experiments (DoE) of a nonflammable liquid electrolyte for a Li-ion battery. As an objective function, we propose a combination of multiple performance indicators that effectively lead to an excellent long-term performance of the battery despite relatively short cycle life tests (100 cycles at 1C). This method allows for planning the most efficient experiments (i.e., requiring only a few battery cycle tests) with a probabilistic approach to achieve high battery performance while ensuring safe operation. Using our in-house BO-DoE toolkit, we demonstrated that less than 20 experimental datasets were required to discover the optimal compositions of the liquid electrolyte with outstanding long-term performance while meeting the safety constraint. Figure 1

A reliable, practical and cost-effective portable wireless potentiostat for mobile chemical sensing and biosensing

Abstract Traditional electrochemical workstations are costly, complex, bulky, and primarily used in laboratories. This study develops a reliable, practical, and cost-effective portable wireless potentiostat to achieve real-time detection on-site and overcome the limitations of traditional electrochemical workstations. The system employs a general-purpose microcontroller unit (MCU), a dual-mode Bluetooth module and cost-effective multi-analog-to-digital converter (multi-ADC) to achieve differential sampling of the LMP91000. The system is equipped with buttons and OLEDs, enabling connection to mobile phones and computers for in-depth data analysis or independent operation. The system was successfully tested with [Fe(CN)6]3-, C6H12O6, and C3H6O3 solutions at concentrations ranging from 0 to 20 mM. The goodness-of-fit (R²) values are 0.984, 0.996, and 0.998, respectively. The average relative standard deviation of the three blank solutions is approximately 3.22%. The detection limits measured (0.003, 0.009, and 0.005 mM) are all lower than the minimum detection concentration (0.2, 0.1, and 0.1 mM). The coefficient of variation for repetitive experiments is less than 5.53%. The device accurately executed chronoamperometry (applied voltage range is ± 1.2 V, current range is ± 882 μA, accuracy is ± 1 %) with high sensitivity and good repeatability. Based on this circuit, a lactic acid detector and a urine glucose detector were developed, which work stably and support long-term operation, proving the stability and reliability of the circuit. Compared to commercial electrochemical workstations, PWES offers remarkable advantages in cost (< $6.4), size (41.5 mm × 76.5 mm), and practicality, making it suitable for a range of applications, including biomedical analysis, food safety, environmental monitoring, and smart wearables.

Active learning strategies for the design of sustainable alloys.

Active learning comprises machine learning-based approaches that integrate surrogate model inference, exploitation and exploration strategies with active experimental feedback into a closed-loop framework. This approach aims at describing and predicting specific material properties, without requiring lengthy, expensive or repetitive experiments. Recently, active learning has shown potential as an approach for the design of sustainable materials, such as scrap-compatible alloys, and for enhancing the longevity of metallic materials. However, in-depth investigations into suited best-practice strategies of active learning for sustainable materials science are still scarce. This study aims to present and discuss active learning strategies for developing and improving sustainable alloys, addressing single-objective and multi-objective learning and modelling scenarios. As model cases, we discuss active learning strategies for optimizing Invar and magnetic alloys, representing single-objective scenarios, and more general steel design approaches, exemplifying multi-objective optimization. We discuss the significance of finding the right balance between exploitation and exploration strategies in active learning and suggest strategies to reduce the number of iterations across diverse scenarios. This kind of research aims to find metrics for a more effective application of active learning and is used here to advance the field of sustainable alloy design.This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

A Modified Reentrant Metamaterial with Equal and Enhanced Orthogonal Elastic Properties

Herein, an improved mechanical metamaterial is proposed, which is derived from the decomposition, rotation, and reassembly of reentrant structures. The structure is parametric, allowing for rapid redesign. The study focuses on the variations in equivalent stiffness and Poisson's ratio of the structure within the linear elastic range when internal rib angle θ changes. First, based on homogenization simulation analyses, it has been verified that this design achieves the tailorable equivalent elastic modulus and Poisson's ratio over a wide range, meanwhile, maintaining equal elastic properties in the orthogonal direction at all times. Second, to reduce future repetitive experiments, an interpolation model is established to expand the design domain. The fitting formula indicates that by modifying θ and relative wall thickness t/L0, the elastic performance of the structures can be tailored. Additionally, the impact of boundary conditions is discussed to minimize experimental costs. Finally, the accuracy of the simulations is validated through uniaxial compression tests.

Optimization of Decoder Priors for Accurate Quantum Error Correction.

Accurate decoding of quantum error-correcting codes is a crucial ingredient in protecting quantum information from decoherence. It requires characterizing the error channels corrupting the logical quantum state and providing this information as a prior to the decoder. We introduce a reinforcement learning inspired method for calibrating these priors that aims to minimize the logical error rate. Our method significantly improves the decoding accuracy in repetition and surface code memory experiments executed on Google's Sycamore processor, outperforming the leading decoder-agnostic method by 16% and 3.3%, respectively. This calibration approach will serve as an important tool for maximizing the performance of both near-term and future error-corrected quantum devices.

Toward widespread use of virtual trials in medical imaging innovation and regulatory science.

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user-friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data-sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science.

A compact solid-state high repetition rate trigger based on a spiral generator.

The trigger generator made with a spiral generator (SG) has the advantages of light weight, compact structure, and low cost and has promising applications in the pulsed power field. This paper introduces a compact solid-state high-voltage pulse trigger system based on an improved SG, which has improved repetition rate and lowered the demands for semiconductor switches' maximum current and current rise rate when compared with previous studies. The improvement is achieved by winding outward an additional layer of the passive layer and low-voltage metal strip, which realizes a significant reduction of the peak current and current rise rate of the discharge switch. The final dimension of the trigger is 25 × 10 × 10cm3, excluding the power supply. An experiment carried out in single shot mode shows that the peak value of the output pulse can reach 50kV with a leading edge of 57ns. Repetitive experiments were carried out up to 1kHz, with the peak voltage of the output pulse being 30.5kV, the leading edge being 48ns, and the jitter being 0.84ns. Finally, the generator is used to trigger a gas switch, and it works stably and reliably.

Development of an Automatic Liquid Dosing System in Microliter Scale

The goal of this study is to design a novel automatic liquid dosing system for liquid sampling at the microliter level. For this purpose, a mechatronics system is designed to position a syringe at the desired position in the workspace and then drive its piston to inject the liquid to be sampled. Then, an application-specific algorithm is developed to be able to prepare samples in 3x3, 4x4, and 5x5 sample container arrays with a desired volume. The performance tests are conducted for preparing samples with up to three different liquids. The repetitive experiments are performed for 50 and 100 µL sampling volumes. The results indicated that it is possible to dose a single liquid with the highest average deviation of 3.9%. Moreover, it is found that it is possible to prepare a sample with a mixture of three liquids by the highest average deviation from the reference value of around 3.4% when the targeting sampling volume is 250 µL for each liquid.

A highly efficient Co-based catalyst for peroxymonosulfate activation for rapid degradation of RhB over a wide pH range

Cobalt-based materials have been demonstrated to be effective heterogeneous catalysts for peroxymonosulfate activation in degrading many refractory organic pollutants. In this study, a new cobalt-based catalyst was prepared using cobalt chloride hexahydrate (CoCl2·6H2O) as the Co2+ source, 2,3-pyrazine dicarboxylic acid and 3,6-DI-4-pyridyl-1,2,4,5-tetrazine (4-PYTZ) as ligands. This catalyst was employed as a peroxymonosulfate activator for the degradation of Rhodamine B(RhB). The results indicated that the prepared Co-based catalyst can effectively degrade RhB dye in aqueous solution by activating peroxymonosulfate (PMS). Specifically, when the initial concentration of RhB was 20 mg/L, its degradation rate exceeded 98 % within merely 9 min. The factors influencing the activation of PMS by the Co-based catalyst were examined, including PMS concentration, reaction temperature, solution pH, and the concentration of organic pollutants. Stability and reusability tests demonstrated that the prepared catalyst exhibited good stability, with the degradation rate of RhB remaining above 97 % after four repetitive experiments. Free radical quenching experiments and electron paramagnetic resonance (EPR) analyses revealed that sulfate radicals (SO4−) and hydroxyl radicals (OH) are the primary reactive radicals accountable for the degradation process. Additionally, it displayed excellent adaptability over a wide pH range, particularly within the pH levels ranging from 5 to 9. This study demonstrates that the prepared Co-based catalyst can serve as a stable and effective option for degrading organic dyes in wastewater treatment applications.

Preparation of mixed matrix membranes with PVP-induced fluorinated Zr-MOF for high-efficiency hydrogen purification under high humidity conditions

Given that hydrogen production often involves impurities such as CO2, CH4, H2O, and alkanes, hydrogen purification is crucial for global green energy applications. Mixed matrix membranes (MMMs) using metal-organic framework (MOF) materials offer a non-thermal, energy-efficient method for hydrogen purification. These membranes have controllable structures and promote environmental sustainability. However, challenges such as filler aggregation and matrix-filler incompatibility during the preparation of MOF-MMMs are difficult to avoid and can adversely affect separation performance. In this study, 4-(trifluoromethyl) benzoic acid (4-TFMBA) was introduced in situ to grow synchronously with polyvinyl pyrrolidone (PVP) on UiO-66-NH2, forming a hydrophobic bifunctional nanoparticle composite, PVP-Zr-MOF-F. Furthermore, in situ loading of PVP-Zr-MOF-F onto a PVDF polymer matrix was achieved using a wet method and the NIPS technique to prepare PVP-Zr-MOF-F@PVDF. The introduction of 4-TFMBA, with highly electronegative F atoms, enhances hydrophobicity and increases H2 affinity through polarization and hydrogen bonding induced by C–F bonds. PVP promotes the formation of smaller Zr-MOF particles, increases the proportion of small pores, prevents aggregation, and acts as a pore-forming agent, facilitating the in situ loading and dispersion of PVP-Zr-MOF-F within PVDF chains. This bifunctionalization not only improves the compatibility and dispersion of the nanofillers but also increases the number of small pores, enhancing van der Waals forces and confinement effects on H2 molecules, making it suitable for gas separation under humid conditions. The results indicate that the H2 permeance of PVP2–Zr-MOF-F@PVDF can reach 114,735 GPU, with selectivities of 19, 21, 24, and 120 for H2/CO2, H2/CH4, H2/C2H4, and H2/n-C6H14, respectively, surpassing the 2008 Robeson's upper bound. Notably, under high humidity conditions, the selectivities for H2/CO2, H2/CH4, and H2/n-C6H14 are 1566 %, 1891 %, and 3264 % higher than those of the original PVDF membrane, achieving high selectivity separation. Additionally, after repetitive experiments with varying humidity, the membrane's selectivity remained almost unchanged, demonstrating the high hydrothermal stability of PVP2–Zr-MOF-F@PVDF. This research provides a new reference for hydrogen purification and holds significant implications for the sustainable development of green energy.

Robustness of hypergraph under attack with limited information based on percolation theory

Hypergraphs serve as a representative form to display higher-order interactions that persist in the community structure of ecological and social systems. A significant characteristic of these entities is their critical behaviours and robustness in response to aggressive assaults. However, in reality, the network structure may be partially observable, meaning that it is distinct from the assumption of global accessibility under random and deliberate assaults. In this work, we provide a theoretical framework to analyse the changes in the giant component in a hypergraph when subjected to limited information disintegration. Percolation theory is employed to derive the size of the giant component and the critical point. Specifically, we apply the message-passing method based on factor graphs to establish a framework for percolation analysis under limited information attacks. On this basis, the hypergraph structure is accurately described. The advantages lie in avoiding the redundant demands of multiple repetitive experiments. Our study demonstrates the relationship between the diversity of the attacked network and the critical condition. By conducting numerical simulations and theoretical research, we also conclude that controlling the observed size of the network and moderately increasing the average hyperdegree can enhance the robustness of the hypergraph. These findings further indicate the impact of hypergraph topology on the resilience of systems and provide analytical insights for optimizing systems with cost-effectiveness and robustness.

Effects of Hexagonal Boron Nitride and Mesoporous Silica Nanoparticles on the Morphology, Mechanical Properties and Antimicrobial Activity of Dental Composites

Hexagonal boron nitride (HBN), an artificial material with unique properties, is used in many industries. This article focuses on the extent to which hexagonal boron nitride and silica nanoparticles (MSN) affect the physicochemical and mechanical properties and antimicrobial activity of prepared dental composites. In this study, HBN, and MSN were used as additives in dental composites. 5% and 10% by weight of HBN are added to the structure of the composite materials. FTIR analysis were performed to determine the components of the produced boron nitride powders, hexagonal boron nitride-containing composites, and filling material applications. The structural and microstructural properties of dental composites have been extensively characterized using X-ray diffractometry (XRD). Surface morphology and distributions of nano boron nitride were determined by scanning electron microscopy (SEM)-EDS. In addition, the solubility of dental composites in water and their stability in water and chemical solution (Fenton) were determined by three repetitive experiments. Finally, the antimicrobial activity of dental composites was detected by using Minimum Inhibitory Concentration (MIC) measurement, as well as Minimum Fungicidal Concentration (MFC) method against yeast strain Saccharomyces cerevisiae, and Minimum Bactericidal Concentration (MBC) method against bacteria strains, Staphylococcus aureus and Escherichia coli. Since the HMP series have better antimicrobial activity than the HP series, they are more suitable for preventing dental caries and for long-term use of dental composites. In addition, when HMP and HP series added to the composite are compared, HMP-containing dental composites have better physicochemical and mechanical properties and therefore have a high potential for commercialization.

Dynamical force measurements for contacting soft surfaces upon steady sliding: Fixed-depth tribology.

The tribology between surfaces can have a profound impact on the response of a mechanical system, such as how granular particles are driven to flow. In this work, we perform experiments that time resolve the tangential and normal components of the force between two semicylindrical polydimethylsiloxane samples immersed in fluid, as they slide against each other in a range of controlled speeds. The time-averaged friction force shows a nonmonotonic dependence on the sliding speed over four decades, which is consistent with the paradigmatic Stribeck diagram and three dynamical regimes associated with it. Our specially designed fixed-depth setup allows us to study the fluctuation of force that exhibits strong stick-slip patterns in one of the regimes. Data from repetitive experiments reveal that both the "onset speed" for the stick-slip patterns and its spatial location along the sample change gradually during the course of our experiments, indicating changes on the sample surfaces. In addition, we conduct counterpart experiments by using spherical samples rubbing against each other, to make a direct connection of the interparticle tribology to the granular flow reported in our previous work [J.-C. Tsai et al., Phys. Rev. Lett. 126, 128001 (2021)10.1103/PhysRevLett.126.128001].

Enhanced photocatalytic activity of g-C3N4/Bi2WO6 heterojunction via Z-scheme charge-transfer mechanism

In this study, g-C3N4/Bi2WO6 (CN/BWO) photocatalytic composite materials were prepared using a combination of calcination and solvothermal methods. The coiled sheet CN and the nanosheet BWO are coupled to form Z-scheme semiconductor junctions. For the close alignment between BWO conduction band (CB) and CN valence band (VB), photogenerated electrons originating from the BWO CB readily recombine with holes from the CN VB, which results in the accumulation of photogenerated holes in the BWO VB, exhibiting remarkable oxidation capability, and photogenerated electrons in the CN CB, displaying strong reducibility. This mechanism plays a pivotal role in the generation of ·O2– and ·OH radicals, optimizing the photocatalytic performance. As the atomic proportion of CN/BWO was 9:20, it exhibits a degradation degree of 90.2 % for rhodamine B (RhB) after 60 min illumination, significantly higher than pure BWO (44.0 %). Repetitive experiment results demonstrated the sample's good stability. Additionally, based on electrochemical tests and active species experiment results, the photocatalytic mechanism of the CN/BWO Z-scheme semiconductor structure involving photogenerated charge separation, migration, and photocatalysis was proposed.

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.

Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.