Engineering Methods Research Articles

DVRT: Design and Evaluation of a Virtual Reality Drone Programming Teaching System

Virtual Reality (VR) is an immersive virtual environment generated through computer technology. VR teaching, by utilizing an immersive learning model, offers innovative learning methods for Science, Technology, Engineering and Mathematics (STEM) education as well as programming education. This study developed a Drone Virtual Reality Teaching (DVRT) system aimed at beginners in drone operation and programming, with the goal of addressing the challenges in traditional drone and programming education, such as difficulty in engaging students and lack of practicality. Through the system's curriculum, students learn basic drone operation skills and advanced programming techniques. We conducted a course experiment primarily targeting undergraduate students who are beginners in drone operation. The test results showed that most students achieved scores above 4 out of 5, indicating that DVRT can effectively promote the development of users' comprehensive STEM literacy and computational thinking, thereby demonstrating the great potential of VR technology in STEM education. Through this innovative teaching method, students not only gain knowledge but also enjoy the fun of immersive learning.

Engineering method for quantifying the coupling effect of transmission tower-line system under strong winds

For transmission tower-line (TL) systems, the coupling effect between line cables and towers under strong winds is significant. This paper presents a method to quantify the coupling effect. Assuming that effective separation of line cables and towers is attainable, this work transforms the coupling effect into the transferred load from the line cable to the target tower, the coupling participation mass of the line cable, and the additional damping. The effective separation conditions are defined through an optimization method minimizing the residual errors of the wind-induced response and dynamic characteristics between the separated bodies and the TL system. A typical TL system is considered and analyzed for its structural dynamic characteristics and wind-induced response. Particularly, the quantities associated with the coupling effect of the TL system are estimated. It reveals that the transferred dynamic load component parallel to the line cable which is overlooked in current codes is significant and highly sensitive to the separation boundary conditions of line cables. Furthermore, the coupling participation mass of the conductor is more prominent than that of the ground wire. The proposed method is feasible for quantifying the TL coupling effect and incorporating it into the wind-induced response analysis of transmission line structures.

Design and Visual Implementation of a Regional Energy Risk Superposition Model for Oil Tank Farms

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks.

Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.

In Emergency Departments (EDs), triage is crucial for determining patient severity and prioritizing care, typically using the Manchester Triage Scale (MTS). Traditional triage systems, reliant on human judgment, are prone to under-triage and over-triage, resulting in variability, bias, and incorrect patient classification. Studies suggest that Machine Learning (ML) and Natural Language Processing (NLP) could enhance triage accuracy and consistency. This review analyzes studies on ML and/or NLP algorithms for ED patient triage. Following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we conducted a systematic review across five databases: Web of Science, PubMed, Scopus, IEEE Xplore, and ACM Digital Library, from their inception of each database to October 2023. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Only articles employing at least one ML and/or NLP method for patient triage classification were included. Sixty studies covering 57 ML algorithms were included. Logistic Regression (LR) was the most used model, while eXtreme Gradient Boosting (XGBoost), decision tree-based algorithms with Gradient Boosting (GB), and Deep Neural Networks (DNNs) showed superior performance. Frequent predictive variables included demographics and vital signs, with oxygen saturation, chief complaints, systolic blood pressure, age, and mode of arrival being the most retained. The ML algorithms showed significant bias risk due to critical bias assessment in classification models. NLP methods improved ML algorithms' classification capability using triage nursing and medical notes and structured clinical data compared to algorithms using only structured data. Feature engineering (FE) and class imbalance correction methods enhanced ML workflows' performance, but FE and eXplainable Artificial Intelligence (XAI) were underexplored in this field. Registration and funding. This systematic review has been registered (registration number: CRD42024604529) in the International Prospective Register of Systematic Reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=604529 . Funding for this work was provided by the National Council for Scientific and Technological Development (CNPq), Brazil.

Valorization of marble sludge waste in biodiesel production using a central composite design.

This work addresses the scarcity of energy resources and environmental issues by concentrating on the synthesis of biodiesel by the transesterification of waste cooking oil with methanol. Marble sludge (MS), a novel heterogeneous catalyst, was used to speed up the rate of reaction. The catalyst's physical and chemical characteristics were thoroughly examined using a variety of methods, including X-ray diffraction, X-ray Fluorescence, SEM, particle size distribution, and BET analysis. Using the MS catalyst, the study investigated the impact of important parameters on the yield of biodiesel from waste cooking oil with the aid of response surface methodology using Design-Expert version 13 software. These parameters included temperature (50-70℃), reaction time (1-4h), catalyst concentration (1-5 wt%), and methanol-to-oil molar ratio (5-20mol/mol). Optimization of the parameters was performed for economic targets to lower the production cost of biodiesel. The results showed that a methanol-to-oil molar ratio of 20:1, a catalyst of 5 wt%, and a reaction time of 1h at 57℃ were the ideal parameters for obtaining a biodiesel yield of 93.5%. The resultant biodiesel revealed promising characteristics, such as a flash point of 160℃, a kinematic viscosity of 4 mm2/s, and a density of 0.871g/cm3. The study demonstrates the significant consequences and real-world advantages of using rational engineering methods to use MS as a very effective, stable, and easily recoverable catalyst for the long-term, sustainable generation of biodiesel from waste cooking oil.

A Review on the Implementation and Effectiveness of Lean Manufacturing Strategies for Industrial and Service Sectors

Ensuring a neat, clean, organized, safe, and productive workplace is essential for every organization. Waste and abnormalities may lead to a high loss of productivity and, consequently, capital, along with a significant compromise to the health and safety of the stakeholders. To overcome such challenges, Lean manufacturing is implemented worldwide. The main aim of this technique is to reduce or eliminate certain types of waste (such as those related to motion, transportation, overproduction, waiting time, etc.) that may contribute to financial loss for the companies. The Lean technique is hybridized with other industrial engineering methods such as DMAIC, Six Sigma, Kanban, etc., depending on the situation. This paper reviews some of the vital work in the last five years on implementing Lean manufacturing for various industrial, commercial, and service setups. It first introduces Lean manufacturing, discusses its implementation procedure and tools and techniques, and presents an analysis of previous attempts made by researchers and engineers using Lean to minimize waste, maximize productivity and efficiency, and enhance quality. The article ends with concluding remarks and highlighting the directions for future research. The novelty of this article lies in the fact that along with a basic introduction and review of the last five years’ literature, it also sheds light on current trends; recent developments; Lean integration with Six-Sigma, Kanban, Kaizen; automation and digital technology interventions to increase the effectiveness of Lean manufacturing; contribution of Lean in sustainable development.

Multi-Criteria Decision Making in Chemical and Process Engineering: Methods, Progress, and Potential

Multi-criteria decision making (MCDM) is necessary for choosing one from the available alternatives (or from the Pareto-optimal solutions obtained by multi-objective optimization), where the performance of each alternative is quantified against several criteria (or objectives). This paper presents a comprehensive review of the application of MCDM methods in chemical and process engineering. It systematically outlines the essential steps in MCDM including the various normalization, weighting, and MCDM methods that are critical to decision making. The review draws on published papers identified through a search in the Scopus database, focusing on works by authors with more contributions to the field and on highly cited papers. Each selected paper was analyzed based on the MCDM, normalization, and weighting methods used. Additionally, this paper introduces two readily available programs for performing MCDM calculations. In short, it provides insights into the MCDM steps and methods, highlights their applications in chemical and process engineering, and discusses the challenges and prospects in this area.

Live or Let Die: MILSTD 1472 Type Usability Checklists in an Agile Development World

Traditional Human System Integration (HSI) processes are at risk of being eliminated given the industry driven velocity and continuous delivery methods of Agile and Development to Operations Engineering. One such process at risk is usability standard checklists like MIL-STD 1472. The DoD Design Criteria Standard—Human Engineering, or MIL-STD 1472 is a military standard that provides guidelines for the ergonomic design of military systems and equipment. It is primarily focused on ensuring that military systems are designed to optimize human performance, safety, and comfort. However, it can be time-consuming to use given that it contains over 4,400 compliance items in its entirety. In an Agile DevOps environment, the typical delivery of products is 2 weeks with some software products boasting deployments every 11 min. Incorporation of usability and rigorous human factors checklists in the design of products using a DevOps environment is a challenge given the shorter timelines and smaller increments between product development and deployment combined with the expertise and hours needed to execute the checklists. The need for regression testing, the act of re-checking items to make sure new features have not created unforeseen usability issues, exacerbates the problem. Software quality testing for each release demands automatic methods to reduce the efforts of recursive testing. Industry standard methods in agile engineering utilize heuristics for quick turn low-cost usability inspections. This paper describes the derivation of a set of usability heuristics based on MIL-STD 1472 which can be used to identify potential human factors issues more quickly, with less cost, and reduced amount of expertise required. The paper also gives an example of a case study which incorporated automatic checking to support usability experts keeping pace with the high velocity pipelines of software development in an agile DevOps environment.

Strain Engineered Semiconductor Nanomembranes for Photonic and Optoelectronic Applications

AbstractWhen semiconductor crystals are made into the form of thin sheets, i.e., nanomembranes, their properties and behaviors may be remarkably different from their bulk counterparts. A plethora of fascinating science and important applications arise consequently, some of which are investigated in recent years, and there are still a lot more to be discovered in the future. Nanomembranes (NMs) provide a good platform for strain engineering and offer tremendous research opportunities in photonics and optoelectronics applications. Strain alters energy band line up of a material furthermore influencing its carrier mobility which impacts a wide range of applications. In this article, the strain‐engineered NMs are reviewed using semiconductor materials as a model system for photonics and optoelectronics applications. While many fundamental aspects of NMs are discussed and outlined different methods for strain engineering of NM, the use of semiconductor NMs for photonics and optoelectronics applications is the locus of the present review article.

Improvement in an Analytical Approach for Modeling the Melting Process in Single-Screw Extruders

Most single-screw extruders used in the plastics processing industry are plasticizing extruders, designed to melt solid pellets or powders within the screw channel during processing. In many cases, the efficiency of the melting process acts as the primary throughput-limiting factor. If the material melts too late in the process, it may not be sufficiently mixed, resulting in substandard product quality. Accurate prediction of the melting process is therefore essential for efficient and cost-effective machine design. A practical method for engineers is the modeling of the melting process using mathematical–physical models that can be solved without complex numerical methods. These models enable rapid calculations while still providing sufficient predictive accuracy. This study revisits the modified Tadmor model by Potente, which describes the melting process and predicts the delay-zone length, extending from the hopper front edge to the point of melt pool formation. Based on extensive experimental investigations, this model is adapted by redefining the flow temperatures at the phase boundary and accounting for surface porosity at the beginning of the melting zone. Additionally, the effect of variable solid bed dynamics on model accuracy is examined. Significant model improvements were achieved by accounting for reduced heat flow into the solid bed due to the porous surface structure in the solid conveying zone, along with a new assumption for the flow temperature at the phase boundary between the solid bed and melt film.

Molecular Engineering in the Semiconductor Industry: Progress and Prospects

Abstract. Along with population growth and economic development, human demand for energy and information will be more than ever, and humans need to rely on semiconductor devices to achieve energy storage, signal and other conversions. So how to design and prepare semiconductor materials with better performance, and industrialise and commercialise the materials has become a new hot topic. Typically, semiconductor materials are nanomaterials, and the current mainstream industrial processing is a top-down process, which raises the cost of production and leads to a waste of resources. Because of this, scientists are starting to believe that molecular engineeringa bottom-up strategy based on molecule self-assembly can save resources, energy, and production costs by constructing semiconductor material structures. This paper examines the bottom-up conceptual approach of molecular engineering and discusses the potential challenges in applying molecular self-assembly technology, which is a crucial method in molecular engineering, to the semiconductor industry. It ultimately concludes that molecular engineering plays a significant role in the semiconductor production process due to its advantages and vast potential for development.

Penerapan Model Incremental Delivery pada Pengembangan Aplikasi Sistem Informasi Hotel Cipta Jakarta

Along with the development of the times, we see many hotels experiencing changes. A change that is rarely reviewed as a news topic is the software overhaul that occurs in hotels from offline to online systems. Lay people only see changes in hotels from a visible perspective, without having to know whether the hotel's system and system applications have changed. One of the factors in system development at Hotel Cipta is the need for hotel management to prioritize the development of system applications from offline to online systems, even web-based. It is not easy to overhaul an existing system application compared to creating a new system application. There are several software engineering methods and models that are commonly used to analyze system needs and design systems in the software development process. One of them is the incremental delivery model. This makes it interesting to try to apply the incremental delivery model to the development of the Hotel Cipta information system application by taking place at the Hotel Cipta on KH. Wachid Hasyim Street, Central Jakarta. In this study, the object of research is the Front Office department.

Al modification layer method for enhancing InAlZnO transistors

In this work, a surface engineering method is proposed to boost electrical performance and stability of the InAlZnO (IAZO) transistors, of which an Al modification layer is deposited on surface of the IAZO channel layer. Through analysed by transmission electron microscope and X-ray photoelectron spectroscopy, the IAZO thin film with Al modification layer undergoes a reduction reaction to generate more oxygen vacancies. Hall effects measurement further demonstrates that IAZO thin films with Al modification layer exhibit higher carrier concentrations than the ones without Al modification layer. The IAZO transistors with Al modification layer exhibit better performance and stability, including a field-effect mobility of 4.36 cm2 V−1s−1 (an improvement of about three-fold), a small subthreshold swing of 105.23 mV/decade, a high on-to-off current ratio greater than 107, and a threshold voltage shift of less than 0.50 V under positive and negative gate bias stress. Moreover, the IAZO transistors with Al modification layer demonstrate high thermal stability under 400 °C for 120 min in air atmosphere. Based on this method, the IAZO inverters have been implemented with high voltage gain of 87.55 V/V. This surface engineering technology paves the way for application of high-performance oxide transistors for advanced monolithic three-dimensional integration.

Target-Templated Construction of Functional Proteomimetics Using Photo-Foldamer Libraries.

Current methods for proteomimetic engineering rely on structure-based design. Here we describe a design strategy that allows the construction of proteomimetics against challenging targets without a priori characterization of the target surface. Our approach employs (i) a 100-membered photoreactive foldamer library, the members of which act as local surface mimetics, and (ii) the subsequent affinity maturation of the primary hits using systems chemistry. Two surface-oriented proteinogenic side chains drove the interactions between the short helical foldamer fragments and the proteins. Diazirine-based photo-crosslinking was applied to sensitively detect and localize binding even to shallow and dynamic patches on representatively difficult targets. Photo-foldamers identified functionally relevant protein interfaces, allosteric and previously unexplored targetable regions on the surface of STAT3 and an oncogenic K-Ras variant. Target-templated dynamic linking of foldamer hits resulted in two orders of magnitude affinity improvement in a single step. The dimeric K-Ras ligand mimicked protein-like catalytic functions. The photo-foldamer approach thus enables the highly efficient mapping of protein-protein interaction sites and provides a viable starting point for proteomimetic ligand development without a priori structural hypotheses.

The HVAC system engineering method for a new family of unified cabins of combine harvesters

BACKGROUND: Designing a combine harvester cabin climate system is a complex and multi-stage process that includes solving a set of tasks. To solve them, a design engineer must have knowledge and skills in various fields, starting with thermal engineering calculations and ending with experimental research methods and computer modeling, which requires a large amount of intellectual and time resources. Therefore, the task of creating a unified method of engineering of combine harvester cabin HVAC system is highly relevant. AIM: Development of the HVAC system for a unified cabin of a grain harvester and a forage harvester. The designed HVAC system is purposed to create a comfortable microclimate in the cabin of the combine for 2 people in summer and winter operating conditions. METHODS: The methodology of calculation and selection of the combine harvester cabin HVAC system, which includes the development of engineering calculation methods and mathematical modeling of thermodynamic and ventilation processes in the cabin, is considered. RESULTS: The main parameters were determined: heat intakes and heat losses for the grain harvester and forage harvester cabins, which amounted to 2.8 and 2.2 kW for the grain harvester, 2.9 and 2.35 kW for the forage harvester; the required air flow rate supplied to the cabin to ensure a comfortable temperature — 740 m3/h; the percentage of air recirculation regarding the conditions of absence of fogging and creation of excessive pressure in the cabin — 75%; the cooling and heating capacity of the HVAC system, taking into account the operating conditions of the combine, are 7.8 and 6.3 kW respectively. The selection of the main equipment of the HVAC system for a unified cabin for a new family of unified cabins of combines — the BUHLER 1000 MFWD evaporator-heater and the Valeo TM16 compressor are chosen. CONCLUSION: The HVAC system designed in accordance with the presented methodology is capable of ensuring a comfortable cabin air temperature in the range of 22–24 °C at various operating modes of the combine harvester in different regions. In addition, the HVAC system parameters eliminate fogging of the cabin windows and the penetration of dusty air inside due to the created excessive pressure.

Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark

The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R2), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used.

DEVELOPMENT OF AUTOMATED CONTROL SYSTEM AND REGISTRATION OF METAL IN CONTINUOUS CASTING

Context. Modern industrial enterprises face challenges that require introduction of latest technologies to improve efficiency and competitiveness. In metallurgy, one of key stages is continuous casting, where quality of products and economic performance of enterprise depend on accuracy and efficiency of process control. Products made using continuous casting technology are widely used in various industries due to their high mechanical properties, structural uniformity and cost-effectiveness. The development of automated metal management and registration system is becoming not only relevant, but also necessary to ensure stable and efficient production. The problem of improving quality of metal products has always been one of most important tasks in steel industry. Imperfect technological processes, human error and equipment malfunctions can lead to defects in finished metal products. This, in turn, affects final characteristics of products, their durability and reliability. To date, available sources have not yet found complete solution to this problem. Therefore, it is necessary to formulate problem and develop algorithm for operation of automated system for controlling and registering metal in continuous casting. Objective. The goal of work is to develop automated metal management and registration system to improve quality of metal products. Method. To achieve this goal, parametric model was proposed, which is formalized on basis of set theory. The model takes into account key parameters of continuous casting process: material characteristics, structural features of crystallizer, casting modes, metal level in crystallizer, and position of shot stopper. Results. The problem was formulated and key parameters were determined, which are taken into account in system’s algorithm, which made it possible to develop system for controlling parameters of continuous casting to solve problem of improving quality of metal products. Conclusions. To improve quality of metal products and stability of casting process, parametric model was created that is comprehensive, allows optimization of key parameters and ensures accuracy of process control by integrating not only modes of product formation, but also takes into account specific properties of source material (chemical composition of material grade, etc.) and design features of casting plant. Algorithm for automated control system has been developed that takes into account relationships between certain key parameters and ensures optimal control of casting process. Based on proposed complex parametric model and algorithm, automated control and metal registration system was created. The focus of work is on quality and efficiency of metal management and registration in continuous casting, based on modern methods of computer science and engineering. A comprehensive experimental comparison of developed system with commercial analogs in real production conditions was carried out, which allowed us to objectively assess its efficiency and reliability.

Product development of sweets chocolate cube using value engineering method

Cocoa is one of the plantation commodities that has an important role in economic activities as a foreign exchange earner. Indonesia is ranked first in Asia with the highest consumption of chocolate, which is 7.3 kg per capita. Chocolates are seen by consumers as a comfort food or stress relief. For consumers who buy chocolate for their own consumption, consuming chocolate in a small size causes consumers to act more impulsively to consume larger quantities. Consumption of chocolate in small sizes (one bite size) gives the impression that the product has a premium image and higher quality. The purpose of this study was to identify the characteristics of chocolate that influence the decisions of product purchasing and to develop one-bite-sized chocolate cubes using the value engineering method. The study used 433 respondents with the criteria of chocolate consumers aged 15-40 years. Based on the research, chocolate bars and molded chocolate are the types of chocolate most often consumed with a frequency of 2-3 times a month. Consumers have the highest complaints when consuming chocolate, namely melted chocolate which makes hands dirty due to its big size and is too hard to bite. Based on the consumer needs analysis, consumers want the development of milk chocolate cube candy with nuts filling. Four alternative cube milk chocolate concepts with a cocoa mass percentage of 50% were prepared using 2 variations in the number of cocoa beans and cocoa fat (40%:10% and 35%:15%) and 2 variations of nuts (almonds and cashews). Based on the hedonic test results, milk chocolate cubes with a formula of 10% cocoa butter and cashew nut filling had the highest product value of 0.013 compared to other formulas. Chocolate color and ease of eating are the attribute weights with the highest weights. It can be concluded that the milk chocolate cube formula is 40% cocoa beans; 10% cocoa butter, cashew nut filling is the selected formula with a production cost of IDR 985.00/g.

3D Bioprinting Tissues: Revolutionizing Organ Transplants

The shortage of organ donors presents a critical challenge in modern healthcare, with demand for organ transplants far outstripping supply. 3D bioprinting, a transformative innovation leveraging biological materials and cells to create tissues and organs, offers a promising solution. This review examines the evolution of 3D bioprinting from early tissue engineering methods to its current role in regenerative medicine and organ transplant applications. It discussed key bioprinting techniques, bio-inks, and scaffold materials that support tissue growth, as well as the technical challenges and ethical considerations faced by researchers. With potential applications ranging from cartilage and skin grafts to fully functional organs, 3D bioprinting stands at the forefront of a new era in organ transplantation. However, barriers such as vascularization, biocompatibility, and regulatory hurdles remain before this technology can be fully integrated into clinical practice. Keywords: 3D bioprinting, organ transplantation, bioink, regenerative medicine, tissue engineering.

Interlayer engineering of V2O5·nH2O by conductive Ni-BTA enabling high-performance aqueous ammonium ion batteries

Aqueous ammonium ion batteries (AAIBs) are of interest due to the low molar mass, small hydration radius, abundant raw materials and high safety of the carrier ammonium-ion. Nevertheless, there are numerous constraints associated with electrode materials that are suitable for ammonium-ion storage. In this study, we design and synthesize a composite comprising of one-dimensional conductive metal-organic skeleton material (1D c-MOF) embedded between layers of hydrated vanadium pentoxide (VOH) with improved ammonium-ion storage. The central ion of the 1D c-MOF is selected to be the nickel ion, while the ligand is 1,2,4,5-benzenetetramine (BTA). The incorporation of Ni-BTA between the vanadium oxide layers results in the formation of a composite (Ni-BTA/VOH) exhibiting enhanced structural stability, augmented layer spacing and elevated conductivity. Furthermore, the dual energy storage mechanisms of VOH and CN rearrangement act in concert to yield a “1+1 > 2” effect, thereby markedly enhancing the ammonium-ion storage. The specific capacity of Ni-BTA/VOH can reach 183 mAh g−1 at 0.2 A g−1, and the retention rate can reach 52.5 % after 500 cycles at 2 A g−1. This work not only proves the potential of Ni-BTA/VOH for widespread application in the field of aqueous batteries, but also provides a new method for structural engineering of VOH with boosted ammonium-ion storage properties.