Energy Cost Research Articles

Degradation of carbamazepine in piezo-activation of persulfate systems: Comparison of PDS and PMS

The choice of persulfate (PS) is crucial to the degradation and energy efficiencies for piezo-activation of PS (PE-PS) systems. Herein, a comparison between piezoelectrically activated peroxydisulfate (PDS) and peroxomonosulfate (PMS) using BaTiO3 are comparatively investigated for carbamazepine (CBZ) degradation. By contrast, the PE-PMS system achieves better degradation and mineralization of CBZ, as well as higher reaction stoichiometric efficiencies (%RSE). Nevertheless, the energy consumption of the PE-PDS system is only 9.69% of the PE-PMS system. The quenching test, chemical probe determination, and electron spin resonance (EPR) analysis suggest that the concentration of •OH, SO4∙-, and 1O2 except ∙O2- of the PE-PMS system, is higher than that of the PE-PDS system. The piezoelectric current density and carrier separation efficiency of the PE-PMS system are higher than that of the PE-PDS system, demonstrating that the piezoelectrically activated PMS has higher reactivity than piezoelectrically activated PDS. The ∙O2- and •OH are the primary ROS in the PE-PDS system, while 1O2 and •OH are the major ROS in the PE-PMS system. Additionally, CBZ is effectively degraded by both PE-PMS and PE-PDS systems from actual water sources. This work compares the performance, energy cost, and mechanism of PE-PMS and PE-PDS systems systematically. Hopefully, all of that is designed to tender a helpful reference for the choice of PS in piezocatalytic research and application.

A novel PEMFC-GT-ST power generation system employing supercritical water gasification of coal for enhanced hydrogen production

This study presents an innovative coal-fired power generation system that integrates a Proton Exchange Membrane Fuel Cell (PEMFC), Gas Turbine (GT), and Steam Turbine (ST) with a supercritical water gasification (SCWG) process designed to efficiently convert coal into hydrogen. A pivotal feature of this system is the first-time integration of advanced two-step SCWG technology with PEMFC, which significantly boosts hydrogen yield, thereby elevating overall efficiency. By effectively addressing the thermodynamic limitations of the Carnot cycle, the proposed system achieves an impressive coal-to-electricity conversion efficiency of 51.61%. Economic viability is demonstrated through a competitive levelized cost of energy (LCOE) of $0.052 per kWh and a rapid payback period of approximately 2.87 years. Furthermore, the study analyzes the impact of various operational parameters—such as coal feed capacity, water-to-coal ratio, operating temperatures of the PEMFC, fuel utilization rates, airflow, and hydrogen bypass flow—on system performance. The findings not only advance the efficiency of coal-to-electricity conversion but also contribute valuable insights to the evolution of sustainable coal-fueled power generation technologies.

Comparison of Controlled-Z operation and beam-splitter transformation for generation of squeezed Fock states by measurement

In the present paper, we investigate the protocol for the generation of squeezed Fock states by measuring the number of particles from a two-mode entangled Gaussian state using a beam splitter and a controlled-Z operation. From two different perspectives, we analyzed two entanglement transformations in the protocol. We evaluated the energy costs and resource requirements of the analyzed schemes. Furthermore, we studied the impact of experimental imperfections on the non-Gaussian states generated by measuring the number of particles. We explored the effects of photon loss and imperfect detectors on the squeezed Fock state generation protocol.

Enhancing Mineral Exploration Programs Through Quantitative XRD: A Case Study from the Gumsberg Polymetallic Sulphide Deposits, Sweden

As challenges in precious and base metal exploration intensify due to the diminishing availability of high-grade ore deposits, rising demand, energy costs, and stricter regulations towards net-zero carbon activities, advanced techniques to enhance exploration efficiency are becoming increasingly critical. This study demonstrates the effectiveness of quantitative X-ray diffraction (QXRD) with Rietveld refinement, coupled with multivariate statistical analysis (including agglomerative hierarchical clustering, principal component analysis, and fuzzy analysis), in characterizing the complex mineralogy of strata-bound volcanic-associated limestone-skarn Zn-Pb-Ag-(Cu-Au)-type sulphide deposits (SVALS). Focusing on 113 coarse rejects from the Gumsberg project located in the Bergslagen mining district in central Sweden, the research identified five distinct mineralogical clusters corresponding to polymetallic base metal sulphide mineralization, its proximal alteration zones, and variably metamorphosed host rocks. The results reveal significant sulphide mineralization, ranging from disseminated to massive occurrences of sphalerite, pyrrhotite, pyrite, and galena, with trace amounts of secondary minerals like anglesite in certain samples indicating weathering processes. The study also identifies rare minerals such as armenite, often overlooked in traditional geological logging. These findings underscore the potential of QXRD to enhance resource estimation, optimize exploration strategies, and contribute to more efficient and sustainable mineral exploration programs. The accuracy of QXRD was cross-validated with geological logs and geochemical data, confirming its reliability as a mineralogical discrimination tool.

Is stochastic thermodynamics the key to understanding the energy costs of computation?

The relationship between the thermodynamic and computational properties of physical systems has been a major theoretical interest since at least the 19th century. It has also become of increasing practical importance over the last half-century as the energetic cost of digital devices has exploded. Importantly, real-world computers obey multiple physical constraints on how they work, which affects their thermodynamic properties. Moreover, many of these constraints apply to both naturally occurring computers, like brains or Eukaryotic cells, and digital systems. Most obviously, all such systems must finish their computation quickly, using as few degrees of freedom as possible. This means that they operate far from thermal equilibrium. Furthermore, many computers, both digital and biological, are modular, hierarchical systems with strong constraints on the connectivity among their subsystems. Yet another example is that to simplify their design, digital computers are required to be periodic processes governed by a global clock. None of these constraints were considered in 20th-century analyses of the thermodynamics of computation. The new field of stochastic thermodynamics provides formal tools for analyzing systems subject to all of these constraints. We argue here that these tools may help us understand at a far deeper level just how the fundamental thermodynamic properties of physical systems are related to the computation they perform.

Research on Intelligent Energy System and Power Metering Optimization Based on Multi-objective Optimization Decision Algorithm

In this paper, the intricate problem of optimizing power metering within an intelligent energy system, utilizing a multi-objective optimization decision-making algorithm, is thoroughly explored. Given the current energy landscape, achieving efficient energy utilization and environmental sustainability has become a focal point of research. As a pivotal aspect of future energy management, the precision and optimization of power metering in intelligent energy systems directly influence the effectiveness and cost of energy consumption. To begin, this paper delves into the fundamental principles and application backdrop of intelligent energy systems, highlighting the significance of power metering in such systems. Subsequently, addressing the multi-objective optimization challenges in power metering, a novel optimization method based on a multi-objective optimization decision algorithm is introduced. This algorithm achieves comprehensive optimization of power metering, encompassing multiple objectives such as power cost reduction, enhanced energy efficiency, and environmental protection. The experimental results underscore the remarkable performance of this algorithm, which not only elevates the precision of power metering but also achieves substantial savings in energy costs and significantly boosts energy efficiency. Furthermore, the algorithm exhibits robust adaptability, making it capable of addressing power metering optimization challenges across diverse scenarios. Finally, this paper discusses the practical application prospects of the optimization algorithm in intelligent energy systems, and points out the direction of future research. The research in this paper provides new ideas and methods for the optimization of power metering in intelligent energy systems, and has important theoretical and practical significance for promoting the intelligence and refinement of energy management.

Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries

Lithium batteries have revolutionized energy storage with their high energy density and long lifespan, but challenges such as energy density limitations, safety, and cost still need to be addressed. Crystalline materials, including Ni-rich cathodes and lithium anodes, play pivotal roles in the performance of high-energy-density lithium batteries. Understanding the micro-scale behavior and degradation mechanisms of these materials is crucial for improving macro-scale battery performance. Simulation methods, particularly machine learning (ML) techniques, have become indispensable tools in elucidating these intricate processes because of great efficiency and acceptable accuracy. ML methods depend on descriptors, which bridge the gap between crystal structures and input matrices of models. These descriptors encode essential atomic-level details in crystal structures, enabling predictions of material properties and behaviors relevant to lithium batteries. This paper reviews and discusses the diverse array of descriptors employed in the simulation of crystalline materials for lithium batteries with high energy density. Case studies highlight the effectiveness of different descriptors in simulating cathode behaviors such as Li/Ni disordering, screening of stable LiNi0.8Co0.1Mn0.1O2 (NMC811) configurations, and lithium deposition behaviors at the anode interface. The discussed descriptors can also be applied to other crystalline cathode, anode, and electrolyte materials in lithium batteries and advance the development of lithium batteries with superior energy density.

Synthesis and Characterization of an Alkali-Activated Binder from Blast Furnace Slag and Marble Waste

This study reports an alkali-activated binder including blast furnace slag (BFS) together with marble waste (MW). Cement is an industrial product that emits a significant amount of CO2 during its production and incurs high energy costs. MW is generated during the extraction, cutting, and processing of marble in production facilities, where dust mixes with water to form a settling sludge. This sludge is an environmentally harmful waste that must be disposed of in accordance with legal regulations. In this study, a substantial amount of MW, a by-product with considerable environmental and economic impacts worldwide, was utilized in the production of a binder through the alkaline activation of BFS. In doing this, different experimental parameters were tested to obtain the best binder samples according to workability and mechanical properties. Then, some experiments such as drying shrinkage determination, strength testing, and microstructure analyses were fulfilled through samples with the best values. The findings supported the improvement of the rapid-setting property of BFS by means of the addition of MW. MW reduced the time-dependent drying shrinkage values of BFS by 55%, especially in slag alkaline activation systems with a low or moderate alkali activator content. The substitution of MW (≤50%) in BFS increased flexural and compressive strengths (4.5 and 61.7 MPa), while a reference sample contained BFS only. Although the use of MW did not create a new phase, it contributed to a C-S-H bonding structure during the alkali activation of BFS in a microstructure analysis.

General Industrial Process Optimization Method to Leverage Machine Learning Applied to Injection Moulding

ABSTRACTThe development of machine learning (ML) technologies is driving changes across many sectors. In industrial settings, this is called the fourth industrial revolution and encompasses several technologies pushing the boundaries of industrial automation. In this study, a general industrial process optimization (GIPO) methodology is formulated in the context of Industry 4.0 and tested on an industrial injection moulding machine (IMM). GIPO aims to encourage the practical inclusion of industrial artificial intelligence at all levels of the manufacturing process, while enabling industrial equipment to adapt to a changing processing environment. Special attention is given to the generality of the methodology so that it can be extended to other applications. In the example case study presented here, GIPO combines K‐nearest neighbours classification and nearest neighbours optimization methods to optimize an injection moulding process effectively. Practical implementation conducted on the IMM demonstrates a novel methodology to leverage data mining and ML methods in a real‐world setting to improve production quality, production time and energy cost.

Water–Energy Nexus-Based Optimization of the Water Supply Infrastructure in a Dryland Urban Setting

Managing water supply systems is essential for developing countries to face climate variability in dryland settings. This is exacerbated by high energy costs for pumping, water losses due to aging infrastructures, and increasing demand driven by population growth. Therefore, optimizing the available resources using a water–energy nexus approach can increase the reliability of the water distribution network by saving energy for distributing the same water. This study proposes a methodology that optimizes the Water Distribution Network (WDN) and its management that can be replicated elsewhere, as it is developed in a data-scarce area. Indeed, this approach shows the gathering of WDN information and a model to save energy by optimizing pump schedules, which guarantee water distribution at minimal operational costs. The approach integrates a genetic algorithm to create pumping patterns and the EPANET hydraulic simulator to test their reliability. The methodology is applied for a water utility in the dryland urban setting of Lodwar, Turkana County, Kenya. The results indicate a potential reduction in energy costs by 50% to 57% without compromising the supply reliability. The findings highlight the potential of WEN-based solutions in enhancing the efficiency and sustainability of data-scarce water utilities in dryland ecosystems.

Why aren't we teaching this? Smart local energy systems and the young person's perspective

AbstractWith the UK government's target of Net Zero by 2050, alongside the rising cost of energy in the UK, it is imperative that public opinion aligns with and promotes affordable, greener energy systems. Within this dialogue, young people's voices and lived experience are needed to deepen the impact of energy policy intervention strategies. This article stresses the importance of a formal curriculum that informs future generations on the subjects of Smart Local Energy Systems (SLES), and the digitalisation advances of energy systems. This paper explores the development of a toolkit to educate and engage young people in these subjects and reviews young people's attitudes and understanding from three schools in England, UK. We find through an interpretive qualitative analysis of both images drawn by the young people, and insights gathered from focus groups, that (i) for these young people, SLES are a useful innovation regarding energy management and distribution, (ii) young people recognise that the education system as it currently stands is not aware of, or informed by smart energy technologies and (iii) that despite being a generation that is the most integrated with personal assistance technology, these young people have significant concerns over the reliability and safety of AI in SLES. These findings are set alongside calls for widespread education in key stages 4 and 5 (ages 14–18) around the subject of SLES, and their benefits to wider society, thereby enabling a future that empowers young people to make change, encourage engineering and computer science‐based career options and secure a fairer, cleaner, sustainable energy transition in the future.

Theoretical Study of the Influence of Electroconvection on the Efficiency of Pulsed Electric Field (PEF) Modes in ED Desalination

Pulsed electric field (PEF) modes of electrodialysis (ED) are known for their efficiency in mitigating the fouling of ion-exchange membranes. Many authors have also reported the possibility of increasing the mass transfer/desalination rate and reducing energy costs. In the literature, such possibilities were theoretically studied using 1D modeling, which, however, did not consider the effect of electroconvection. In this paper, the analysis of the ED desalination characteristics of PEF modes is carried out based on a 2D mathematical model including the Nernst–Planck–Poisson and Navier–Stokes equations. Three PEF modes are considered: galvanodynamic (pulses of constant electric current alternate with zero current pauses), potentiodynamic (pulses of constant voltage alternate with zero voltage pauses), and mixed galvanopotentiodynamic (pulses of constant voltage alternate with zero current pauses) modes. It is found that at overlimiting currents, in accordance with previous papers, in the range of relatively low frequencies, the mass transfer rate increases and the energy consumption decreases with increasing frequency. However, in the range of high frequencies, the tendency changes to the opposite. Thus, the best characteristics are obtained at a frequency close to 1 Hz. At higher frequencies, the pulse duration is too short, and electroconvective vortices, enhancing mass transfer, do not have time to develop.

Modeling of hybrid renewable resources and comprehensive feasibility analysis of grid interactive energy system for commercial building: a case study

ABSTRACT The growing energy demand in commercial buildings and businesses poses significant environmental challenges due to the dependence on fossil fuel-based energy. To address this issue, increasing the use of renewable energy sources and decreasing reliance on fossil fuels is crucial. Commercial buildings, in particular, have many opportunities to harness energy from renewable resources, such as solar, wind, and biomass. This study analyzed historical energy consumption data from a commercial building using MATLAB to forecast future energy demands. A range of machine learning algorithms were employed to accurately predict energy consumption, including Ensemble Boosting, Ensemble Bagging, Decision Trees, Support Vector Machines (SVM), and Neural Networks (NN). A Hybrid Renewable Energy (HRE) system was designed to meet the building’s energy needs, integrating renewable sources with grid-interactive inverters. Performance metrics for the forecasting models were calculated and documented, followed by a detailed techno-economic and environmental feasibility analysis of the HRE system. Different levels of renewable energy penetration were explored to optimize HRE designs using load-following dispatch algorithms tailored to the commercial building’s specific needs. The study revealed that achieving a renewable energy proportion of 12.6% resulted in the lowest cost of electricity (COE) at $0.135 and a Net Present Cost (NPC) of $8.84 million. Additionally, a reliability study using load flow techniques confirmed the stability of the optimized Hybrid Renewable Energy system. The energy costs for varying levels of renewable penetration, ranging from 10% to 40% is also explored in this research. In summary, transitioning to renewable energy sources in commercial buildings can reduce environmental impact while ensuring a sustainable energy supply.

Rationalization of Energy Expenditure: Household Behavior in Poland

Background: The implementation of the EU climate and energy policy, along with changes in the legal environment, has led to a significant increase in energy prices in Poland. Consequently, energy expenditures are now a larger part of household budgets. These rising energy costs and the evolving legal landscape are compelling households to invest in energy-saving solutions and modify their energy consumption habits. This article aims to identify the activities of households in Poland regarding the rationalization of energy expenditures. It formulates the following research hypothesis: households invest in energy-saving appliances to rationalize energy expenditures and/or change their behaviors to reduce energy consumption. Methods: The paper is based on primary research conducted using an online questionnaire survey on a sample of 331 respondents in Poland in March and April 2023. Results: A classification tree algorithm was used to identify the level of investment activities and behavioral changes made by households to reduce energy expenditures. The authors found that low-income households and people who fear further energy price increases are the first of all to change their behaviors for more energy-efficient ones. Medium- and high-income households take investment measures. They replace household appliances with more energy-efficient ones and install heat pumps and photovoltaic panels. These investments are motivated by responsible consumption, environmental protection, cleanliness, and the ease of use of the appliances.

The application ozone within the food industry, mode of action, current and future applications, and regulatory compliance.

The food industry faces numerous challenges today, with the prevention and reduction of microbial contamination being a critical focus. While traditional chemical-based methods are effective and widely used, rising energy costs, the development of microbial tolerances, and growing awareness of the ecological impact of chemical biocides have renewed interest in novel biocides. Ozone, in both its gaseous and aqueous forms, is recognized as a potent disinfectant against bacteria, viruses, and fungi due to its high oxidation potential. Our review highlights several studies on the applications of ozone within the food industry, including its use for surface and aerosol disinfection and its capacity to reduce viable Listeria monocytogenes, a pertinent foodborne pathogen harbouring environmental and biocide stress tolerances and biofilm former. We also explore the use of ozone in food treatment and preservation, specifically on blueberries, apples, carrots, cabbage and cherry tomatoes. While ozone is an effective disinfectant, it is important to consider material incompatibility, and the risks associated with prolonged human exposure to high concentrations. Nevertheless, for certain applications, ozone proves to be an efficacious and valuable alternative or complementary method for microbial control. Compliance with the Biocide Products Regulation (BPR) will require ozone device manufacturers to produce proven efficacy and safety data in line with British standards based on European standards (BS EN), and researchers to propose adaptations to account for ozone's unique properties.

Metamodeling the deposition process in oil preprocessing to optimize heat exchanger cleaning: a deep neural network approach

The accumulation of deposits in heat exchangers during oil preprocessing, known as fouling, compromises thermal efficiency and increases maintenance and energy costs. This study aimed to develop effective strategies for managing these deposits to optimize heat exchanger operations. Metamodeling combined with Artificial Intelligence was employed, using machine learning models to process sequential data, which is crucial for understanding the evolution of deposits over time. Two machine learning models, Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM), were trained and evaluated. The DNN achieved a Mean Squared Error (MSE) of 0.05345, outperforming the LSTM, which had an MSE of 0.8763. The results suggest that the DNN was more effective in predicting fouling, although both models showed signs of overfitting. Advanced monitoring technologies, such as IoT and remote sensing, as well as predictive maintenance, were found to be essential for improving operational efficiency and reducing costs, highlighting the importance of digitalization and automation in oil preprocessing.

Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system

With a target of 35 % renewable energy in the country’s energy mix by 2030, the goal of this research is to support the Libyan government’s plan to maximize the use of environmentally friendly and sustainable energy sources. This will be accomplished by making effective use of the region’s plentiful natural resources. The feasibility of wind and solar energy has been established by local research, and the presence of highlands that can store pumped hydropower (PHS) makes hybrid renewable energy systems (HRES) even more feasible. These systems might offer a sizable edge over competitors in the regional energy market. In this study, PHS was utilized to stabilize electricity production in addition to storing energy. A hydropower turbine provides electricity to the load, and the PHS system is powered by a PV/wind supply. By lowering the volatility of wind and photovoltaic power generation, the suggested system could offer a more dependable renewable energy source without requiring complicated control mechanisms. This strategy could encourage a wider adoption of renewable energy, which could be especially advantageous for developing nations. Under particular load and climate conditions, the energy production of different solar PV array and wind turbine farm configurations was estimated using the System Advisor Model (SAM) software. The ideal HRES configuration consists of a 290 MW wind farm, a 154 MW solar PV field, and a 508 MW PHS system. This configuration will provide 100 % of the annual electrical demand of 513 GWh, plus a 20 % safety margin. The levelized cost of energy (LCOE), estimated at $211.65/MWh, is reduced by this configuration. It is anticipated that the $929.02 million initial investment will be recovered in 11 years, and the system will turn a profit in the following 9 years. Also, the planned HRES will stop 532.17 tons of CO2 emissions from being released each year.

Design of photovoltaic and battery energy storage systems through load demand characterization: A case study in Thailand

The integration of photovoltaic (PV) system at behind the meter has gained popularity due to the growing trend toward environmentally friendly energy solutions. Coupling PV systems with battery energy storage systems (BESS) addresses the uncertainties of PV energy production while enhancing energy management. Load characteristics have influence on PV and BESS design both in technical and economic aspects. This paper presents a comprehensive analysis of load demand characterization methodologies tailored for the design of PV and BESS. The fundamental load properties such as daily maximum power, minimum power, and load energy, categorizing loads based on their daily and annual variation degrees are investigated. The techno-economic performance of PV-only and PV+BESS systems is analyzed, focusing on metrics such as excess energy, peak limiting performance, and economic feasibility using the levelized cost of energy (LCOE). The findings of this study reveal three distinct load characteristics based on daily variation: moderate (50–70 %), moderate to high (70–80 %), and high (80–90 %). Load profiles with moderate variation (50–70 %) demonstrate economic feasibility when sized with a PV-only system matching maximum power demand. With BESS prices reduced to less than 140 USD/kWh, these loads could also integrate a 1C-BESS sized at half capacity under the same control strategy, resulting in lower excess energy and LCOE. Conversely, loads with higher variation (>70 %) and lower self-consumption rates (<70 %) face fewer viable options for both PV-only and PV+BESS configurations, especially if BESS costs remain high. These loads require custom-designed solutions and more extensive load analysis. Through analysis, this study establishes the relationship between load characteristics and system design suitability for the loads studied. The findings underscore the importance of incorporating load demand characterization techniques in the design systems, highlighting the need for tailored approaches based on specific load characteristics and economic viability.

Dynamic web-based GIS tool for pre-feasibility evaluation of renewable energy projects

The high energy consumption and the large amount of emissions associated with roads life cycle makes it necessary for the administrations involved to invest in the implementation of renewable energy technologies on adjacent lands so that the net balance between CO2 emitted and CO2 saved is zero, thus helping to decarbonize the transport networks. To facilitate the first step in the long process leading up to the construction of wind and solar farms, a user-friendly tool is here presented that allows an early-stage evaluation of renewable energy projects. The ENROAD web-based GIS tool, through a functional, modern, intuitive, and ergonomic graphical user interface aimed at non-specialized users, optimizes the use of photovoltaic and wind technologies based on shading and wake losses, respectively, taking into account the solar radiation and wind conditions of the location and the geometry of the area selected by the user. In addition, the tool provides a thorough financial analysis, incorporating the effects of user-defined long-term interest rates, inflation rates, loan costs and efficiency losses on the Levelized Cost of Energy as well as traditional financial metrics, enabling users to conduct insightful if-then simulations. While this tool cannot have the precision of the in-depth study needed for the design of complex renewable energy facilities, it can facilitate the decision making by the administrations. Finally, a case study and a validation section have been included to demonstrate the technical and financial evaluation provided by ENROAD and ensure that the results provided are reasonable and accurate enough.

Facile and Environmentally Friendly Synthesis of Ga2O3/MFI Catalysts for Propane Dehydrogenation.

A GaOx-based catalyst is recognized as a promising catalyst for dehydrogenation of light alkanes. Conventionally, impregnation and calcination are necessary processes for the loading of GaOx. However, the incomplete impregnation of gallium salt solution would generate wastewater, and the calcination for the dissociation of gallium salt would release harmful gases, such as SOx, NOx, and HCl. Meanwhile, the high temperature results in an excessive energy cost and undesired GaOx particle aggregation. Here, we report a facile and environmentally friendly method for the synthesis of GaOx-based catalysts. The gallium salt solution was replaced directly with liquid gallium (LG). Through a simple physical mixing method at room temperature, uniform GaOx nanoparticles with diameters of around 3.5 nm were loaded onto the surface of silicalite-1 (S-1). With the optimal GaOx/MFI catalyst, the propane conversion and propylene selectivity reached 22.9 and 90.1%, respectively, in the propane dehydrogenation reaction. The work offers a clean and economical strategy utilizing liquid metal (LM) as an impregnation solution for the preparation of GaOx-based catalysts.