Traditional Mechanisms Research Articles

Benchmark Ab Initio Mapping of the F- + CH2ClI SN2 and Proton-Abstraction Reactions.

The experimental and theoretical studies of gas-phase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems. Nevertheless, in the case of the prototypical X- + CH3Y → Y- + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored. Thus, here, we perform the first high-level ab initio characterization of the F- + CH2ClI SN2 and proton-abstraction reactions utilizing the explicitly-correlated CCSD(T)-F12b method. Two possible SN2 channels leading to the Cl- + CH2FI and I- + CH2FCl products are distinguished, in which we investigate four different pathways of back-side attack Walden inversion, front-side attack, double inversion, and halogen-bonded complex formation. In order to obtain the benchmark energies of the geometries of the stationary points, determined at the CCSD(T)-F12b/aug-cc-pVTZ level of theory, additional computations are carried out considering the basis set effects, post-CCSD(T) correlations, and core corrections. Using the benchmark data, we assess the accuracy of the MP2, DF-MP2, MP2-F12, and DF-MP2-F12 methods as well. By comparing the present F- + CH2ClI system with the corresponding F- + CH3Y [Y = Cl and I] reactions, this study demonstrates that further halogenation of CH3Y significantly promotes the corresponding proton-abstraction and SN2 retention channels as well as the halogen-bonded complex formation, and as a consequence, the traditional back-side attack Walden-inversion mechanism becomes less pronounced.

Optimal Dispatch of Coal-fired Power Units with Carbon Capture Considering Peak Shaving and Ladder-type Carbon Trading

In the context of the low-carbon transformation of the power system, carbon markets, and carbon capture technologies have become important means to promote energy conservation and emission reduction. The rapid development of renewable energy poses a challenge to the regulation capability of the power system, and energy storage technology is currently an important means to enhance the regulation capability of the power grid. To reduce the carbon emission level of the power system, improve the system economy and the ability to consume renewable energy, this paper proposes a multi-energy complementary system (MECS) with carbon capture power plant (IFCCPP) considering ladder-type carbon trading (LCT) and energy storage. The optimal dispatch model was developed with the objective of minimizing the overall operating cost. The results indicated that the integration of IFCCPP and LCT has better economic and environmental benefits than separate applications. The integration of the two modes can reduce the total cost by 504.33 k$ and carbon emission by 39232.63 t, which verifies the rationality of integrating the two modes. The LCT produces more significant emission reduction effects due to its higher benefits when carbon emissions are lower. The traditional carbon trading mechanism reduces 4779.88 t of carbon emissions, and LCT reduces 38574.45 t.

Study on the protective mechanism of Xuemaitong Capsule against acute myocardial ischemia rat based on network pharmacology and metabolomics

BackgroundXuemaitong Capsule (XMT) is a widely recognized traditional Miao medicine extensively utilized in Chinese clinical settings. Previous studies have demonstrated XMT protective effects against acute myocardial ischemia (AMI). However, the mechanism by which XMT provides protection to AMI rats is yet to be fully understood. Aim of the studyThe purpose of this study was to investigate the protective mechanism of XMT on AMI rats through network pharmacology, traditional pharmacodynamics and metabolomics. Material and methodsThe components and potential targets of XMT were identified through the application of traditional Chinese medicine system pharmacology and traditional Chinese medicine molecular mechanism bioinformatics analysis tools. We constructed herb-composition-target networks and analyzed protein–protein interaction (PPI) networks. The potential mechanism was explored by pathway enrichment analysis. Subsequently, the AMI model was constructed by ligation of the anterior descending branch of the left coronary artery, and XMT protective effects on AMI rats were evaluated by analyzing the myocardial enzyme profiles, electrocardiograms(ECG), Triphenyltetrazolium chloride(TTC) staining, and Hematoxylin-Eosin (HE) staining in AMI rats. Metabolomics based on UHPLC-Q-Exactive Orbitrap MS was used to observe the protective effect of XMT on the serum metabolic profile of AMI, and multivariate statistical analysis further revealed the differential patterns of metabolites after XMT treatment. Finally, integrated pathway analysis was carried out to reveal the biological metabolic mechanism. ResultsA total of 392 active components of XMT acted with 624 targets for treating AMI. Pathway enrichment analysis revealed that XMT could treat AMI through TNF, MAPK and PI3K-Akt signaling pathways. Further, XMT could effectively prevent ST-segment elevation in the ECG, reduce the size of myocardial infarction, decrease cardiac weight index and cardiac enzyme levels, and mitigate histological damage in the hearts of AMI rats. In addition, XMT callback 117 metabolites and four metabolic pathways, including taurine and hypotaurine metabolism, phenylalanine metabolism, pyrimidine metabolism and retinol metabolism. Through integrating network pharmacology and metabolomics, we explored the biological mechanism by which XMT treats AMI. It was speculated that the mechanism of XMT is to regulate TNF signaling, PI3K-Akt pathway and MAPK signaling pathway, and participate in cell apoptosis, oxidative stress, immune and inflammatory reaction and other biological processes. ConclusionXMT plays a protective role in AMI rats by regulating multiple metabolic biomarkers, multiple targets and pathways. Therefore, XMT may provide a potential strategy for the treatment of AMI.

An OpenCV and Single-Camera Facial Recognition Real-Time Attendance System

Abstract--Traditional attendance mechanisms have fallen short of providing an actual guarantee in attendance, especially in advanced commercial and academic milieus, based on the general practice of proxy attendance. This paper details the implementation of an attendance system based on technologies like cameras and facial recognition capabilities. By prohibiting unlawful attendance using proxy mechanisms, this system seeks to improve the accuracy of attendance tracking. We present a strong framework that takes, processes, and validates facial photos in real- time by utilising developments in computer vision and machine learning. This provides a creative way to get around the drawbacks of traditional attendance systems. Keywords—Facial recognition, Accuracy of attendance tracking, Computer vision, Machine learning, Real-time processing, Attendance validation, Advanced commercial and academic milieus.

Energy Aware Optimal Virtual Machine Scheduling in Cloud Environment Using Hybridized Egret Swarm with Sea Lion Optimization Algorithm

With emergence of advanced technologies, the internet plays as the essential role to share the information across the world. With emergence of large number of users, the data quality gets affected thus; it leads to cause burden in scheduling where it provides fewer services to the users. Due to this existence, the cloud computing technology emerges to offer better services. Certainly, the optimal scheduling process of a Virtual Machine (VM) becomes challenging in the larger size of data in cloud environments. Modern systems and data centers place a high priority on energy consumption. Thus, it provides poor performance regarding energy usage and throughput analysis marked in the traditional techniques. In order to consider aforementioned issues, a novel VM scheduling mechanism is introduced for allocating and managing resources in cloud. The major scope of this developed VM scheduling process tends to minimize energy consumption. The developed Hybrid Egret Swarm-based Sea Lion Optimization (HES-SLO) algorithm is implemented to allocate resources based on several objective functions like execution time, energy or power consumption, cost, and resource utilization. Thus, the overall performance analysis takes place, where the developed VM scheduling model is compared with the traditional VM allocation mechanisms to verify the efficacy. In this validation, the HES-SLO attains 21.16%, 4.903%, 0.17%, and 7.86% for energy consumption, resource utilization, execution time, and throughput analysis. Based on the entire validation, it shows greater performance by compared with existing approaches.

Analytical Modeling of Bistable Beam With Double-Ring-Shaped Deformation Element

Abstract The field of negative stiffness structures has recently seen an increase in attention as a result of advancements in additive manufacturing (AM) technology. These advancements have made it possible to produce materials in a more expedient manner. The purpose of this research is to demonstrate a highly tunable and reusable bistable beam design that is based on a double-ring-shaped deformation element that undergoes pure bending, unlike traditional bistable mechanisms with buckling-based deformation elements. Further, an analytical model is developed and then validated by means of experimental and numerical results to precisely predict the snap-through behavior of a beam. It has been shown that the analytical model that was proposed has accuracy in terms of foreseeing the snap-through transition. A detailed study is conducted to explore the influence of geometric parameters on the snap-through characteristics. By carefully modifying these geometric properties, the required mechanical behavior of negative stiffness metamaterial (NSM) may be achieved for specific applications.

Pd hydride metallene aerogels with lattice hydrogen participation for efficient hydrogen evolution reaction

Hydrogen adsorption and desorption in single-phase catalysts often occur at a single catalytic site based on the traditional hydrogen evolution reaction (HER) pathway, which makes it difficult to break the limitation entailed by the Sabatier principle. Herein, β-Pd hydride metallene (β-PdHene) aerogels are synthesized as advanced HER catalysts. A lattice hydrogen-involved mechanism is reported to separate adsorption and desorption sites, which is thermodynamically favorable compared to the traditional reaction pathway. In situ differential electrochemical mass spectrometry and theoretical calculations reveal that lattice hydrogen as additional active sites directly participate in the HER process. Consequently, β-PdHene aerogels exhibit a low overpotential of only 20 mV at 10 mA cm−2 and remarkable HER stability, which are even comparable to commercial Pt/C. Our work opens an avenue to rationally develop highly active HER catalysts, bypassing the design limitations of catalysts under traditional mechanisms.

Comparative Study on Anomaly based Intrusion Detection using Deep Learning Techniques

With an array of applications, Wireless Sensor Networks (WSNs) have the potential to transform the world into a smart planet. WSNs consist of a collection of resource-constrained sensors that gather data, which is then utilized for decision-making and analysis, leading to improvements in quality of service, management, and efficiency. However, the open nature of WSNs exposes them to numerous vulnerabilities and threats. Operating in potentially hostile and unattended environments makes these networks attractive targets for adversaries. Therefore, it is essential to detect the presence of malicious attacks within the networks and implement robust security systems to address these challenges. While traditional security mechanisms such as authentication and cryptographic methods are commonly employed, they often fall short in effectively countering the dynamic nature of modern attacks. Hence, IDS (Intrusion Detection System) tends to continuously monitor the network and detect potential threats in real-time scenarios. This method possess the ability of identifying, responding promptly, preventing and thus ensures resilience of the network. Therefore, the present study reviews the various intrusion detection techniques and data collection methods. The main aim of the study is to investigate the design challenges of deploying IDS in a WSN environment. So, the study analysed the AI (Artificial Intelligence) based techniques involved in intrusion detection and how these techniques could be adopted in WSN. In addition, the comparative analysis of several ML (Machine Learning) and DL (Deep Learning) algorithms are also deliberated to portray the different deployment technique with corresponding outcomes. Further, the main challenges faced by each studies with their limitations are specified for supporting future researchers in developing new trends in intrusion detection for WSN.

Order-of-magnitude increased range of constant force adjustment via section optimization

An adjustable constant force environment is critical in engineering applications, including precision manipulation, surgical robots, and advanced manufacturing, all requiring a wider range of force regulation and adjustment. Traditional adjustable constant force mechanisms (ACFMs) have significant limitations in achieving a wide range of constant force (CF) adjustments due to factors like stress and interference. Existing ACFMs typically offer only a 2–4 times change in CF, which is insufficient. This research aims to provide an order-of-magnitude increase in CF adjustment range while remaining compact and preserving CF quality through section optimization. An analytical model demonstrates the efficacy of adjusting CF by an order-of-magnitude in prismatic and non-prismatic beams for CF adjustment method selection. Additionally, finite element analysis and design optimization of the non-prismatic serpentine beam with an out-of-plumbness imperfection angle and polynomial section description were conducted to maximize CF adjustability and quality. Experimental validation showed a 38 times change in CF adjustability for 5 percent variation in the CF, 18 times improvement in compactness, and high Energy Similarity Index SCF compared to the prismatic benchmark mechanism. This proposed system may be implemented in several applications, including load control, impact and vibration mitigation, space exercise, wearables, etc.

Polyelectrolyte complex coatings based on PSS/CTAB for enhanced antifogging and antibacterial performances

Antifogging and antibacterial properties are both essential in biomedical examination and diagnostics to ensure clear visibility and prevent potential bacterial infections. However, it is extremely challenging to integrate both functions on one surface. Herein, this study presents a rapidly assembled polyelectrolyte complex (PEC) coating, which can be applied to different transparent substrates through a facile combination of anionic polystyrene sulfonic acid (PSS) and cationic surfactant cetyltrimethylammonium bromide (CTAB). Unlike traditional superhydrophilic anti-fogging mechanisms, this coating leverages the hygroscopic properties of polyelectrolyte complex groups, ensuring effective anti-fogging performances across a wide temperature range. Additionally, the incorporation of quaternary ammonium compounds (QACs) within the PEC complex confers strong bactericidal properties to the coating. The resultant coating exhibits excellent surface adhesion and robust resistance to water immersion, and low cytotoxicity. With its dual antifogging and antibacterial properties, this coating shows significant promise for diverse applications in medical diagnostic and detection lens equipment.

Cross-layer Authentication Mechanism Model Combined with 5G Converged Channel Fingerprint

In the actual communication environment, once the attacker successfully steals the legitimate channel information, the key information can be cracked according to the authentication response, so there is a security risk of key leakage. This paper combines the physical layer channel characteristics with the shared key, and combines it into a joint key to design a challenge-response physical layer authentication mechanism based on interpolation polynomial. Then, this method is applied to the EAP-AKA’ authentication protocol of 5G network, and a cross-layer authentication mechanism for 5G converged channel fingerprint is proposed. Finally, using the captured high-level authentication challenge response data, a simulation environment is built in MATLAB and the feasibility and security of the scheme are verified. The experimental results show that the authentication mechanism has better authentication performance and security performance. Compared with the traditional high-level authentication mechanism, it has certain advantages in computing overhead and can meet the 5G application scenarios of large-scale IoT terminals. Moreover, it combines physical layer authentication with high-level authentication, realizes mutual complementarity and mutual enhancement, and enhances the security performance of authentication.

Triggering lattice oxygen in in-situ evolved CoOOH for industrial-scale water oxidation

Oxygen evolution reaction (OER) is a kinetic bottleneck in water electrolysis. Activating lattice oxygen oxidation mechanism (LOM) can break the limitation of the theoretical overpotential of the traditional adsorbate evolution mechanism (AEM) and improve the intrinsic activity. Here, we introduced Ag single atoms and Se vacancies in CoSe2 (Ag-CoSe2), and found that Ag single atoms and Se vacancies could activate lattice oxygen in CoOOH electrochemically in-situ derived from CoSe2, triggering the LOM. The catalyst exhibits a low overpotential of 167 mV@10 mA cm−2 and a high mass activity of 732.925 A g−1@250 mV. More importantly, the Ag-CoSe2||Pt/C pair requires only 1.68 V to deliver 500 mA cm−2 in an anion-exchange membrane electrolyzer (30 wt% KOH, 60℃) and maintains virtually no degradation for up to 1500 h. Advanced spectroscopic analyses and density-functional theory calculations demonstrate that the synergy between Ag single atoms and Se vacancies results in an upward shift in the O 2p band and strengthens the covalency of metal-oxygen bonds. This effect activates the lattice oxygen oxidation mechanism and reduces the adsorption energy barrier. Additionally, the shortened Co-Se bond and the decreased formation energy of Se vacancies contribute to enhanced structural and catalytic stability in CoSe₂.

Collaborative Optimization Scheduling of Source-Network-Load-Storage System Based on Ladder-Type Green Certificate–Carbon Joint Trading Mechanism and Integrated Demand Response

To fully leverage the potential flexibility resources of a source-network-load-storage (SNLS) system and achieve the green transformation of multi-source systems, this paper proposes an economic and low-carbon operation strategy for an SNLS system, considering the joint operation of ladder-type green certificate trading (GCT)–carbon emission trading (CET), and integrated demand response (IDR). Firstly, focusing on the load side of electricity–heat–cooling–gas multi-source coupling, this paper comprehensively considers three types of flexible loads: transferable, replaceable, and reducible. An IDR model is established to tap into the load-side scheduling potential. Secondly, improvements are made to the market mechanisms: as a result of the division into tiered intervals and introduction of reward–penalty coefficients, the traditional GCT mechanism was improved to a more constraining and flexible ladder-type GCT mechanism. Moreover, the carbon offset mechanism behind green certificates serves as a bridge, leading to a GCT-CET joint operation mechanism. Finally, an economic low-carbon operation model is formulated with the objective of minimizing the comprehensive cost consisting of GCT cost, CET cost, energy procurement cost, IDR cost, and system operation cost. Simulation results indicate that by effectively integrating market mechanisms and IDR, the system can enhance its capacity for renewable energy penetration, reduce carbon emissions, and achieve green and sustainable development.

Ultimately bounded control for nonlinear systems under denial-of-service attacks: an accumulation-based event-triggered mechanism

This paper investigates the control problem for nonlinear systems under denial-of-service (DoS) attacks. In contrast to traditional event-triggered mechanisms (ETMs), an accumulation-based ETM, which is robust to abrupt signals, is employed to schedule the signal transmission between sensors and controllers, thereby saving more communication resources. Due to the openness of network-based transmission, DoS attacks are encountered during data transmissions, where the DoS attacks considered in this paper follow the Bernoulli distribution. This paper aims to design a controller for nonlinear systems considering the impact of DoS attacks and accumulation-based ETMs, ensuring that the controlled system state is ultimately bounded in the mean square. Firstly, sufficient conditions are provided to guarantee that the system state can achieve the given control performance, along with an explicit expression for the ultimate bound. Then, the controller gains are obtained by solving certain linear matrix inequalities. Finally, two simulation examples are conducted to validate the effectiveness of the designed controller.

Modelling, Simulation and Testing of Steer-by-Wire System with Variable Steering Ratio Control Strategy in 14-DOF Full Vehicle Model

This research explores the application of Proportional-Integral-Derivative (PID) controllers and Variable Steering Ratio (VSR) control strategies within a Steer-by-Wire (SbW) system, utilizing a comprehensive 14 Degrees of Freedom (DOF) full vehicle dynamic model. The study aims to advance vehicle dynamics and control through SbW technology. This research addresses the challenge of optimizing steering control by integrating an SbW system with PID and VSR strategies to overcome the limitations of traditional steering mechanisms and enhance vehicle responsiveness and stability. The SbW system, using a PID-based controller, was rigorously tested and validated using MATLAB Simulink and Hardware-in-the-Loop System (HiLS) method. The performance of the system was assessed through four distinct signal tests: step, sine, square, and sawtooth inputs. These tests confirmed the precision of the PID controller in position tracking. To improve the practical applicability of the SbW system, a 14-DOF vehicle dynamic model was developed and validated before integration with the SbW system. The integrated system accurately replicated conventional steering behavior, as validated by Step Steer Input (SSI) and Double Lane Change (DLC) tests, demonstrating less than 11% RMS percentage error - well within the acceptable threshold of less than 15%. Additionally, the study proposed and validated VSR control strategies through the DLC test. Results indicate that the PID controller effectively tracked desired signals, while the VSR strategy reduced driver workload and improved steering stability. This research offers valuable insights into enhancing SbW system performance and lays a foundation for future advancements in vehicle control systems.

Innovation and Expansion of Neural System-Based Teacher Evaluation Management Mechanism in Academy

The traditional evaluation mechanism of ideological education in universities faces many challenges. How to improve the quality of knowledge service work in the university and enhance instructors' skill level and effectiveness is the main problem facing the sustainable development of higher education in the new century. This article studies the innovation and expansion of the evaluation and management mechanism for university teachers based on the neural system in order to achieve effective scientific and systematic teaching methods. The experimental results show that the improvement rate of higher education academic performance under neural system learning is 75.12%, which is 17.34% higher than traditional Blended Open Learning(BOP) learning methods. Therefore, the improvement rate using the neural system learning is higher. This article targets different groups responsible for university-level teaching, thereby greatly improving the learning of college students.

Effect of inclusion on contact damage evolution of wind turbine gears based on configurational force theory

The gear box of wind turbine is subjected to the complex environment such as random wind load for a long time, and the gear contact fatigue becomes a key factor limiting the stability and reliability of wind turbine equipment. Therefore, the research on the gear contact damage evolution mechanism is faced with difficulties such as complex stress states, damage anisotropy and failure modeling. However, traditional fracture mechanics and damage mechanics are limited in predicting the failure load of complex defects and evaluating the structural integrity. Material configurational force theory can describe the effect of defect configurational change on the free energy of materials and be used to predict the damage and failure behavior of materials. In this paper, a wind turbine gear contact damage model is constructed based on this theory. The gear contact interface stress field simulation analysis is carried out for the key bearing area of gear contact, and the gear contact damage evolution process under contact load is simulated. For the problem of gear failure caused by metal oxide inclusion, the damage evolution model based on Jk parameters is established. The damage evolution process of gear containing inclusions under contact loading is simulated numerically, and the influence of depth, shape and location of inclusions on the damage evolution behavior is analyzed. The research results have shown that the configurational force theory damage model can effectively simulate the contact damage phenomenon of gear and explain the pitting and spalling of gear surfaces. It has theoretical significance to predict contact fatigue life of gear accurately.

Pattern‐moving‐based dynamic description and optimal control for non‐Newtonian mechanical systems with generalized cell mapping

AbstractThis article focuses on the problem of modelling and control for a non‐Newtonian mechanical system that may be subject to the statistical law instead of the traditional Newtonian principles mechanics. A discrete method, synthesizing the generalized cell mapping (GCM) together with dynamic programming (DP), with inverse search strategy, using pattern moving theory (PMT) framework, was proposed. The basic idea is to describe and control the system's dynamic peculiarity utilizing pattern class variable in ‘pattern moving space’. First, a few prior concepts were reviewed, including PMT, cell mapping, and optimal control for this kind system. Then, we articulated a cross‐mapping method to analyze the system dynamic behaviour, which takes into account the computational and statistical properties of pattern class variable simultaneously. For system optimal control, the improved GCM and cost function were performed to determine the discrete optimal control table (DOCT) in accordance with dynamic programming and inverse search. Finally, simulation results of two cases demonstrate the efficiency and practicality of the proposed approach. The study has resulted in a solution of describing and controlling based on pattern class variable for non‐Newtonian mechanical systems, and its main objective was to give a different perspective in term of research into non mechanistic principles modelling and application of nonlinear systems.

Oxygen Plasma Triggered Co-O-Fe Motif in Prussian Blue Analogue for Efficient and Robust Alkaline Water Oxidation.

In the context of oxygen evolution reaction (OER), the construction of high-valence transition metal sites to trigger the lattice oxygen oxidation mechanism is considered crucial for overcoming the performance limitations of traditional adsorbate evolution mechanism. However, the dynamic evolution of lattice oxygen during the reaction poses significant challenges for the stability of high-valence metal sites, particularly in high-current-density water-splitting systems. Here, we have successfully constructed Co-O-Fe catalytic active motifs in cobalt-iron Prussian blue analogs (CoFe-PBA) through oxygen plasma bombardment, effectively activating lattice oxygen reactivity while sustaining robust stability. Our spectroscopic and theoretical studies reveal that the Co-O-Fe bridged motifs enable a unique double-exchange interaction between Co and Fe atoms, promoting the formation of high-valence Co species as OER active centers while maintaining Fe in a low-valence state, preventing its dissolution. The resultant catalyst (CoFe-PBA-30) requires an overpotential of only 276 mV to achieve 1000 mA cm-2. Furthermore, the assembled alkaline exchange membrane electrolyzer using CoFe-PBA-30 as anode material achieves a high current density of 1 A cm-2 at 1.76 V and continuously operates for 250 hours with negligible degradation. This work provides significant insights for activating lattice oxygen redox without compromising structure stability in practical water electrolyzers.

Oxygen Plasma Triggered Co−O−Fe Motif in Prussian Blue Analogue for Efficient and Robust Alkaline Water Oxidation

AbstractIn the context of oxygen evolution reaction (OER), the construction of high‐valence transition metal sites to trigger the lattice oxygen oxidation mechanism is considered crucial for overcoming the performance limitations of traditional adsorbate evolution mechanism. However, the dynamic evolution of lattice oxygen during the reaction poses significant challenges for the stability of high‐valence metal sites, particularly in high‐current‐density water‐splitting systems. Here, we have successfully constructed Co−O−Fe catalytic active motifs in cobalt‐iron Prussian blue analogs (CoFe‐PBA) through oxygen plasma bombardment, effectively activating lattice oxygen reactivity while sustaining robust stability. Our spectroscopic and theoretical studies reveal that the Co−O−Fe bridged motifs enable a unique double‐exchange interaction between Co and Fe atoms, promoting the formation of high‐valence Co species as OER active centers while maintaining Fe in a low‐valence state, preventing its dissolution. The resultant catalyst (CoFe‐PBA‐30) requires an overpotential of only 276 mV to achieve 1000 mA cm−2. Furthermore, the assembled alkaline exchange membrane electrolyzer using CoFe‐PBA‐30 as anode material achieves a high current density of 1 A cm−2 at 1.76 V and continuously operates for 250 hours with negligible degradation. This work provides significant insights for activating lattice oxygen redox without compromising structure stability in practical water electrolyzers.