Practical Applications Research Articles

Exploring Practical Pedagogy in High School Biology Education: A Qualitative Study of Pre-Service Biology Teachers’ Experiences in Vietnam

<p style="text-align: justify;">This qualitative study examines the experiences of pre-service high school biology teachers in the Mekong Delta, Vietnam, focusing on the challenges and transformative learning experiences encountered during their school-based internship. It aims to explore the dichotomy between specialized biology knowledge and practical applicability in high school teaching and uncover the significance of fostering student engagement through innovative strategies and the role of mentor teachers in guiding pre-service teachers. Semi-structured interviews were used to collect data from pre-service biology teachers who completed a school-based internship in the Mekong Delta. Thematic analysis was employed to analyze the interview data. The study highlights the challenges of bridging the gap between theoretical knowledge and classroom practice, emphasizing the importance of innovative teaching strategies for student engagement. It recognizes the role of mentor teachers in guiding and enhancing the skills of pre-service teachers in creating effective learning environments. Findings suggest the need for curriculum reforms that balance specialized biology knowledge and practical pedagogical skills. The study emphasizes the importance of experiential learning through teaching apprenticeships to better prepare pre-service teachers for their professional journey.</p>

Measuring Interest: Development and Application of a Three-Dimensional Situational Interest Short Scale

<p>Situational interest is an important factor that has a great influence on learning success in both in-school and out-of-school learning situations. Although there has been extensive research on interest in its diverse forms for decades, an evaluated measurement instrument for situational interest that covers the three theoretically defined components of interest (emotional, cognitive, value-related) is still missing. Therefore, in this study, based on person-object theory of interest, a short scale was developed that can be used in a variety of learning programs independent of content or methods. In study 1, eight suitable items were selected and their structure was examined using exploratory methods. In study 2, the results of study 1 were verified using confirmatory factor analyses. Study 3 shows an example of a practical application of the newly developed scale in two different learning settings. The findings provide evidence that the scale developed here is a practical instrument to measure situational interest taking into account all its components. On the one hand, the scale can help teachers evaluate their educational programs; on the other hand, it can be used by researchers to empirically investigate the construct of interest. Thus, the scale makes an important contribution to research and practice.</p>

Enhanced Carbon-Carbon Coupling at Interfaces with Abrupt Coordination Number Changes.

Cu-catalyzed electrochemical CO2 reduction reaction (CO2RR) produces multi-carbon (C2+) chemicals with considerable selectivities and activities, yet required high overpotentials impede its practical application. Here, we design interfaces with abrupt coordination number (CN) changes that greatly reduce the applied potential for achieving high C2+ Faradaic efficiency (FE). Encouraged by the mechanistic finding that the coupling between *CO and *CO(H) is the most probable C-C bond formation path, we use Cu2O- and Cu-phthalocyanine-derived Cu (OD-Cu and PD-Cu) to build the interface. Using operando X-ray absorption spectroscopy (XAS), we find that the Cu CN of OD-Cu is ~11, favoring CO* adsorption, while the PD-Cu has a COH*-favorable CN of ~4. Operando Raman spectroscopy revealed that the interfaces with abrupt CN changes promote *OCCOH formation. As a result, the designed catalyst achieves a C2+ FE of 85±2 % at 220 mA cm-2 in a zero-gap CO2 electrolyzer. An improvement of C2+ FE by 3 times is confirmed at the low potential regime where the current density is 60-140 mA cm-2, compared to bare OD-Cu. We report a 45-h stable CO2RR operation at 220 mA cm-2, producing a C2+ product FE of ~80 %.

Principles and Design of Biphasic Self‐Stratifying Batteries Toward Next‐Generation Energy Storage

AbstractLarge‐scale energy storage devices play pivotal roles in effectively harvesting and utilizing green renewable energies (such as solar and wind energy) with capricious nature. Biphasic self‐stratifying batteries (BSBs) have emerged as a promising alternative for grid energy storage owing to their membraneless architecture and innovative battery design philosophy, which holds promise for enhancing the overall performance of the energy storage system and reducing operation and maintenance costs. This minireview aims to provide a timely review of such emerging energy storage technology, including its fundamental design principles, existing categories, and prototype architectures. The challenges and opportunities of this undergoing research topic will also be systematically highlighted and discussed to provide guidance for the subsequent R&D of superior BSBs while conducive to bridging the gap for their future practical application.

Lowering Entropic Barriers in Triplex DNA Switches Facilitating Biomedical Applications at Physiological pH

AbstractTriplex DNA switches are attractive allosteric tools for engineering smart nanodevices, but their poor triplex‐forming capacity at physiological conditions limited the practical applications. To address this challenge, we proposed a low‐entropy barrier design to facilitate triplex formation by introducing a hairpin duplex linker into the triplex motif, and the resulting triplex switch was termed as CTNSds. Compared to the conventional clamp‐like triplex switch, CTNSds increased the triplex‐forming ratio from 30 % to 91 % at pH 7.4 and stabilized the triple‐helix structure in FBS and cell lysate. CTNSds was also less sensitive to free‐energy disturbances, such as lengthening linkers or mismatches in the triple‐helix stem. The CTNSds design was utilized to reversibly isolate CTCs from whole blood, achieving high capture efficiencies (>86 %) at pH 7.4 and release efficiencies (>80 %) at pH 8.0. Our approach broadens the potential applications of DNA switches‐based switchable nanodevices, showing great promise in biosensing and biomedicine.

Principles and Design of Biphasic Self-Stratifying Batteries Toward Next-Generation Energy Storage.

Large-scale energy storage devices play pivotal roles in effectively harvesting and utilizing green renewable energies (such as solar and wind energy) with capricious nature. Biphasic self-stratifying batteries (BSBs) have emerged as a promising alternative for grid energy storage owing to their membraneless architecture and innovative battery design philosophy, which holds promise for enhancing the overall performance of the energy storage system and reducing operation and maintenance costs. This minireview aims to provide a timely review of such emerging energy storage technology, including its fundamental design principles, existing categories, and prototype architectures. The challenges and opportunities of this undergoing research topic will also be systematically highlighted and discussed to provide guidance for the subsequent R&D of superior BSBs while conducive to bridging the gap for their future practical application.

Lowering Entropic Barriers in Triplex DNA Switches Facilitating Biomedical Applications at Physiological pH.

Triplex DNA switches are attractive allosteric tools for engineering smart nanodevices, but their poor triplex-forming capacity at physiological conditions limited the practical applications. To address this challenge, we proposed a low-entropy barrier design to facilitate triplex formation by introducing a hairpin duplex linker into the triplex motif, and the resulting triplex switch was termed as CTNSds. Compared to the conventional clamp-like triplex switch, CTNSds increased the triplex-forming ratio from 30 % to 91 % at pH 7.4 and stabilized the triple-helix structure in FBS and cell lysate. CTNSds was also less sensitive to free-energy disturbances, such as lengthening linkers or mismatches in the triple-helix stem. The CTNSds design was utilized to reversibly isolate CTCs from whole blood, achieving high capture efficiencies (>86 %) at pH 7.4 and release efficiencies (>80 %) at pH 8.0. Our approach broadens the potential applications of DNA switches-based switchable nanodevices, showing great promise in biosensing and biomedicine.

A Self-Adaptive Inertia Hybrid Control of a Fully Constrained Cable-Driven Parallel Robot

A Cable-Driven Parallel Robot (CDPR) is a special type of parallel robot actuated by elastic cables instead of rigid links, which brings uncertainties of model nonlinearities and structural friction depending on the payload variation. A precise motion with minimum control effort against an unknown payload has been a challenging task. To conquer it, we propose an adaptive hybrid control approach for the fully-constrained CDPR with an unknown payload in this paper. A Cartesian space position control and a joint space cable force control are designed through inertia estimation to encounter the effect of the uncertain payload. And then, an artificial neural network (ANN) is employed to estimate the end-effector force in Cartesian space for precise force control and consecutive uncertainties compensation. The stability of the proposed controller is proven based on the Lyapunov method and the efficacy is verified by the experiments. As for feasibility results, the proposed adaptive hybrid control-based cable force estimation was evaluated on the Mini CDPR. Compared to the traditional control scheme, the tracking error was decreased approximately 56% and the utilized tension was significantly reduced for the given trajectory tracking test. Furthermore, the investigation of free movements of the helix trajectory and pick-and-place tasks showed that the proposed hybrid adaptive control can control the position and cable force altogether while being able to estimate the inertia of the end-effector. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The study was motivated by the control problem of adjusting cable tensions of a cable-driven parallel robot (CDPR) against varying payloads at the end-effector. Several advanced control approaches were proposed for but limited to CDPR model parameter estimation and did not consider varying payload. This paper suggests a practical methodology capable of both self-adaptation to unknown payload inertia and consequently improving tracking control performance for a CDPR. In this paper, we derived an adaptive control model and proved stability to ensure safety. In order to encounter unmodeled uncertainties such as pulley friction and cable characteristics, we utilized an artificial neural network (ANN) trained by experiments. The experimental results could confirm that the proposed scheme can estimate the payload inertia and generate minimized cable tension while maintaining good trajectory tracking performance. In future research, we will show more practical validation for a scale-up CDPR with heavy objects toward logistics or pick-and-place application in practice.

Diversity Management: Practical Application in a Healthcare Organization.

If you are not confused, you don't know what's going on. -Jack Welch In commenting on the business environment today (1994), Jack Welch could have been talking about healthcare organizations experiencing the double-barreled assault on two sweeping changes simultaneously. Not only are hospitals preparing for yet-to-be-determined changes in national healthcare delivery systems but they are doing so in the midst of shifting demographics in both the population and employee base. While many make the case for managing diversity, we would like to go one step further in offering a framework for developing an organization's ability to build an inclusive environment that gets the best from its staff and provides the best service to its customers.

Method Toward Network Embedding Within Homogeneous Attributed Network Using Influential Node Diffusion-Aware

Network embedding (NE) focuses on discovering low-dimensional embeddings of nodes while retaining their intrinsic features and structure of nodes. It is essential for many practical applications, containing text mining, community detection, and node classification. However, the great majority of existing systems are incapable of combining structural and attribute information. To tackle the above-mentioned problem, considering the information diffusion process, we present a novel model for attribute NE (ANE), namely influential node diffusion-based matrix factorization (INDMF), which contains topology level and attribute level. In detail, we first propose a novel method to extract high-order information via influential node diffusion sequences. Then, we regard the optimization of our proposed structure-based and attribute-based loss functions as a matrix factorization problem. Furthermore, this model can be used to generate final node embedding by aggregating the topology level and attribute level hierarchically. Experiments are conducted on four real-world datasets, which indicates that INDMF beats all competing algorithms in node categorization, community detection, and graph visualization.

Factors propelling mathematics learning: insights from a quantitative empirical study

Mathematics learning (ML) is a fundamental aspect of education that lays the groundwork for various academic disciplines and practical applications. Understanding the factors that propel ML is crucial for optimizing educational outcomes. This quantitative empirical study investigates the impact of logical reasoning (LR), critical thinking (CT), information technology (IT), and distance learning (DL) on ML. The study employs structural equation modeling (SEM) using SmartPLS 4 for data analysis and hypothesis testing. The findings reveal that LR, CT, IT, and DL positively influence ML. The results highlight the importance of fostering LR, CT, and the integration of IT in mathematics education. This study contributes to the existing body of knowledge by providing insights into the factors that promote effective ML. These findings have implications for educators, policymakers, and curriculum developers, aiding in the design of instructional strategies and the integration of technology to enhance ML outcomes.

Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable Data-Driven Models

Efficient energy optimization and scheduling in industrial factories depend on accurate, reasonable, and real-time monitoring of equipment power consumption. However, the power prediction of industrial equipment requires a large number of process data, which could be inevitably contaminated by some imperfect data, due to the harsh environments. Monitoring or predicting equipment power consumption is usually not feasible using data-driven black-box models with imperfect data. This paper proposes a prediction-based approach for power consumption monitoring using an interpretable data-driven model. First, a data preprocessing method is used to remove outliers and fill in missing values. Then, a Volterra polynomial basis function (VPBF) model is built to predict equipment power consumption. This model decomposes power values into a series of basis functions consisting of input parameters. Moreover, to compensate for data dropouts during the power consumption monitoring process, a networked predictive monitoring system is also proposed. Finally, this paper presents two case studies based on actual production equipment in an industrial manufacturing factory. The results demonstrate that the proposed approach can achieve satisfactory monitoring accuracy and adaptation ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problem of power consumption monitoring of industrial equipment in harsh production environments. A conventional solution is to predict the power consumption using data-driven black-box models, with limited feasibility and interpretability. This paper proposes a new power consumption monitoring approach, utilizing an interpretable data-driven model and a networked predictive method. This approach accurately reveals the transparent relationships between the output and input parameters. The power prediction result is consequently interpretable and more reasonable. Meanwhile, this approach actively compensates for data dropouts in the network, which can help operators real-time grasp the power consumption of equipment. Furthermore, this approach can be integrated into energy optimization systems as a basis of optimization and decision-making. Practical applications in an aluminum manufacturing company located in Guangdong Province demonstrate that this approach is feasible and applicable. Future research is to expand this approach further for energy optimization and scheduling.

Finite-Time Adaptive Fuzzy Backstepping Control for Quadrotor UAV With Stochastic Disturbance

This paper proposes an adaptive fuzzy output feedback control approach for quadrotor unmanned aerial vehicles (QUAV) with stochastic disturbances, besides which we consider the unmeasurable states and unknown nonlinear functions. The QUAV system contains stochastic terms which are not bounded and not differentiable. By combining the It <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\hat{\rm \textbf{o}}$</tex-math> </inline-formula> differential equation with finite-time theory, a novel stochastic finite-time stability controller for QUAV is raised for the first time. The fuzzy logic system (FLS) is utilized to approximate the unknown nonlinear functions in the model. Dynamic surface control technology is introduced to reduce the complexity of differential. Furthermore, an observer with FLS is designed through the adaptive backstepping technique to estimate the immeasurable states. It is proved that this control approach can ensure that all states of the closed-loop QUAV system are semi-global and finite-time stable in probability. Meanwhile, the errors of system outputs and observer converge to a small neighborhood of origin. The simulation results show the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical applications, many systems are often affected by internal and external disturbances, and not all of them can be described by mathematical models, so they are called stochastic disturbances. Taking the QUAV as an example, when the system is disturbed by internal parameters, external environment, system input, sensor error, and other stochastic factors, the deterministic system can no longer accurately describe the controlled object. In this paper, the QUAV is regarded as a stochastic system for the first time, and a novel controller is designed to solve the above problems. In addition, the QUAV needs fast action in some applications, such as emergency collision avoidance, which has high requirements for the convergence performance of the controller. Therefore, studying the finite-time control for QUAV systems with stochastic disturbances is necessary. At the same time, this paper also considers the problem that the state is not measurable and the system contains unknown nonlinear equations.

Event-Triggered-Based Adaptive NN Cooperative Control of Six-Rotor UAVs With Finite-Time Prescribed Performance

This paper studies the disturbance-observer-based adaptive neural network cooperative event-triggered control problem for six-rotor unmanned aerial vehicles with finite-time prescribed performance. Six-rotor unmanned aerial vehicle systems are divided into the position subsystem and the attitude subsystem. The switching threshold event-triggered mechanism that considers the influence of event triggering on the consensus control performance is designed of each six-rotor unmanned aerial vehicle to save the transmission resources, which excludes the Zeno behavior. Neural networks are introduced for estimating the uncertainties and solving the algebraic loop problem. In addition, for the position subsystem, by combining the prescribed performance with the velocity function, a finite-time control scheme is proposed, which can guarantee that the consensus errors converge to a prespecified neighborhood of the origin, and all closed-loop signals are bounded. Based on the designed control input signal of position subsystem, an adaptive neural network event-triggered control mechanism is designed to stabilize the attitude subsystem. Finally, some verification results are given to test the rationality of the proposed control strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The purpose of this paper is to design an event-triggered cooperative control scheme to save the transmission resources for six-rotor unmanned aerial vehicle systems with performance limitations and lumped disturbances. In practical applications, if the impact of the event-triggered mechanism on the system performance is not taken into account, the system performance may be significantly degraded while the information transmission resource is saved. Thus, this paper designs an event-triggered mechanism that considers the system performance, which can effectively weigh the relationship between system performance and transmission resource consumption and better meet the practical requirements. Moreover, a finite-time specified performance control strategy is introduced to avoid the problem of difficult determination of residual set and improve the transient-state and steady-state performances of unmanned aerial vehicle systems, which is more in line with the actual demand.

Messages are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-Series Forecasting.

This paper delves into the problem of correlated time-series forecasting in practical applications, an area of growing interest in a multitude of fields such as stock price prediction and traffic demand analysis. Current methodologies primarily represent data using conventional graph structures, yet these fail to capture intricate structures with non-pairwise relationships. To address this challenge, we adopt dynamic hypergraphs in this study to better illustrate complex interactions, and introduce a novel hypergraph neural network model named CHNN for correlated time series forecasting. In more detail, CHNN leverages both semantic and topological similarities via an interaction model and hypergraph diffusion process, thereby constructing comprehensive collaborative correlation scores that effectively guide spatial message propagation. In addition, it incorporates short-term temporal information to generate efficient spatio-temporal feature maps. Lastly, a long-term temporal module is proposed to generate future predictions utilizing both temporal attention and a gated recurrent network. Comprehensive experiments conducted on four real-world datasets, i.e., Tiingo, Stocktwits, NYC-Taxi, and Social Network demonstrate that the proposed CHNN markedly outperforms a range of benchmark methods.

MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong capabilities of deep learning, we propose a convolutional generative adversarial network-based (Conv-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box malware detectors. Motivated by the rival game between malware authors and malware detectors, MalFox adopts a confrontational approach to produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate adversarial malware examples. To demonstrate the effectiveness of MalFox, we collect a large dataset consisting of both malware and benignware programs, and investigate the performance of MalFox in terms of accuracy, detection rate, and evasive rate of the generated adversarial malware examples. Our evaluation indicates that the accuracy can be as high as 99.01% which significantly outperforms the other 12 well-known learning models. Furthermore, the detection rate is dramatically decreased by 45.1% on average, and the average evasive rate is noticeably improved by up to 56.0%.

Limiting Temperature Rise in Cochlear Implantation Bone Drilling: A Novel Approach.

In the process of cochlear implantation surgery, it is crucial to develop a method to control the temperature during the drilling of the implant channel since high temperatures can result in damage to bone and nerve tissue. This paper simplified the traditional point heat source temperature rise model and proposed a novel extreme peck drilling model to quantitatively calculate the maximum temperature rise value. It is also innovatively introduced a new method for calculating the best peck drilling duty cycle to strictly control the maximum temperature rise value. Besides, the neural network is trained with virtual data to identify two important thermal parameters in the temperature rise model. In the experiment of epoxy resin and temporal bone, the difference between predicted maximum temperature and actual maximum temperature was less than 1.5 °C, and the error rate was less than 10%. And the error source was analyzed by variational mode decomposition, along with discussion of potential solutions. In the temperature control experiment, the model successfully controlled the maximum temperature rise within 10 °C.For cochlear implantation surgery, we also divide the implantation channel into different stages based on the bone density in CT images to identify thermal parameters and calculate drilling strategies. This method provides a new strategy for accurate and effective control of borehole heat generation. These achievements provide new ideas and directions for research in cochlear implantation surgery and related fields, and are expected to have extensive application in medical practice.

A Time Delay Estimation Interpretation of Extended State Observer-Based Controller With Application to Structural Vibration Suppression

This paper aims to investigate the disturbance estimation mechanism of extended state observer (ESO) from a new perspective: a time-delay estimation (TDE) interpretation. By drawing the concept of input-output linearization, ESO is transformed into an equivalent TDE form in frequency domain. The establishment of this relationship can lead to an improved understanding of ESO’s principle. In addition, ESO’s parameters for disturbance estimation are given with explicitly physical meanings. Furthermore, theoretical analysis from this perspective also results in a quantitative relationship between ESO estimation performance and its two tuning parameters including control gain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b_0$</tex-math> </inline-formula> and bandwidth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega _o$</tex-math> </inline-formula> . Theoretical results are evaluated by using an all-clamped plate structure with an inertial actuator, which is considered to be a typical benchmark for active vibration control. Extensive comparative experiments validate algorithm effectiveness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was focused on the ability of extended state observer (ESO) to estimate total disturbances. The controlled system is a typical benchmark for active vibration control consisting of an all-clamped plate structure with an inertial actuator. The model uncertainties and external disturbances that bothers the system is compensated by the real-time estimation of the ESO. In order to facilitate practical engineering applications, the distinct physical meanings of ESO parameters are expounded with the help of the relationship between ESO and time delay estimation (TDE) established in frequency domain. Moreover, the influence of parameters control gain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b_0$</tex-math> </inline-formula> and bandwidth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega _o$</tex-math> </inline-formula> on ESO is qualitatively established. Therefore, the engineer can avoid fall into a trial-and-error strategy that could be laborious and time-consuming. Thus, this paper proposed a convenient way for practitioners interested in developing anti-disturbance control strategies based on ESO. The simplicity and the partial-model-based characteristics are also profitable in practice. Potential applications include robotic systems, smart structures and electrical drives. In the future, the improvements of ESO based on the proposed method to deal with the intractable disturbances should be taken in an effort.

Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype

E-health is being adapted in modern hospitals as a significant addition to the existing healthcare services. To this end, modern hospitals urgently require a mobile, high-capacity, secure, and cost-effective communication infrastructure. In this paper, we explore potential applications of a hybrid broadband power line communication (PLC) and Wi-Fi in an indoor hospital scenario. It utilizes the existing power line cables and Wi-Fi plug-and-play devices for indoor broadband communication. Broadband power line (BPL) adaptors with Wi-Fi outputs are used to build an access network in hospitals, particularly in areas where the wireless router signal is poor. The Tenda PH10 AV1,000 AC Wi-Fi power line adapter is a set of BPL adapters that offer operational bandwidth of up to 1,000 Mbps. These adapters are based on the HomePlug AV2 protocol and can provide a data rate up to 200 Mbps on the physical layer. An experiment using the PLC Wi-Fi kit is carried out to show that a Wi-Fi and PLC hybrid network is the best candidate to provide wide range of practical applications in a hospital including, but not limited to, telemedicine, electronic medical records, early-stage disease diagnosis, health management, real-time monitoring, and remote surgeries.

Fabrication of Two-Dimensional Material Membranes

Membrane separation is an energy-efficient separation process. Two-dimensional (2D) materials have shown potential as a new generation of membrane materials due to their unique structures and physicochemical properties. The separation performance of 2D material membranes crucially depends on how 2D nanosheets are assembled in membranes, such as interlayer spacing between stacked nanosheets, chemical properties of nanosheet surfaces, alignment of nanosheets and thickness of membranes, which are closely related to their fabrication methods. This short review concisely overviews commonly used membrane fabrication methods for different types of 2D materials, including graphene-based materials, 2D covalent organic frameworks, 2D metal-organic frameworks, MXenes and other 2D materials. The representative 2D material membranes resulting from their essential fabrication methods are discussed. The advantages and shortcomings of different fabrication methods are compared. The critical challenges to realising large-scale production of 2D material membranes for practical applications are highlighted.