Energy Consumption Efficiency Research Articles

A sprinkler- water cycle PEC-MFC system with bimetallic monatomic Co-Fe/g-C3N4/ACF cathode achieves efficient removal of gas trimethylamine

Volatile organic compounds (VOCs) air pollutants are the precursors of ozone and PM2.5, all harmful to human health. To reduce the equipment volume further and improve the treatment capacity at room temperature, a novel microbial fuel cell (MFC) system, with a sprinkler and water recycle module, was designed to integrate bio-electricity generation from biodegradation and photocatalytic oxidation of VOCs over cathodic diatomic Co-Fe/g-C3N4 doped activated carbon felt (Co-Fe/g-C3N4/ACF). The removal of trimethylamine in both the gas and liquid phase was proved (VOCs concentration in air ≈ 0.93 g m−3, the removal capacity reached ≥ 119.97 mg gcat-1h−1). Through the sprinkler-water cycle, the trimethylamine molecules in air transferred and diffused to the liquid phase, then acted as the only carbon source for the electrogenic bacteria, that supplied the electricity to drive photoelectric catalysis under light illumination. The high efficiency (>2086 mg gcat-1h−1) and low energy consumption in the new system at room temperature means broad application prospect in VOCs treatment.

Enhanced capacitive deionization via modulating local electron of electro-adsorption sites in a Trace-Fe-modified carbon framework

In order to alleviate the crisis of freshwater resources, it is essential to explore a clean, efficient, and low-consumption water treatment technology. As a kind of electro-adsorption water purification technology, capacitive deionization (CDI) has drawn continuously growing attention because of its advantages. The ion adsorption capability of CDI is closely related to the electrodes. Unfortunately, the ion adsorption capacity of common carbon materials is limited and far from reaching the rapidly increasing requirements of fresh water treatment. In this study, Fe-N-HPC electrode materials with trace Fe uniformly dispersed in N-doped hollow carbon polyhedral frameworks were tactfully prepared by a simple in-situ growth and pyrolysis method. The structure and morphology of the heterostructure material were verified by various analysis. Electrochemical tests showed the Fe-N-HPC electrode had an enhanced performance with a higher specific capacitance and fast ions diffusion coefficient. The Fe-N-HPC showed excellent CDI performance, with an enhanced desalinization capacity (34.38 mg/g, 500 mg/L NaCl solution, 1.2 V), and regenerative stability after 100 adsorption–desorption cycles. In addition, it also exhibited higher charge efficiency, energy efficiency and lower specific energy consumption. The removal mechanism of Na+ on the electrode and the participation of trace Fe can modulate the local electron of the electro-adsorption sites were reasonably verified by ex-situ XRD, Raman, and XPS characterizations, which finally facilitated the electro-adsorption property. This work presents a promising scheme for the advance of high efficiency capacitive desalination system.

Enhancing diesel engine performance and emission reduction through hydrogen enrichment in algal biodiesel blends.

The introduction of hydrogen into the engine could enhance its combustion efficiency and emission characteristics. The current study examines the attributes of compression ignition (CI) engines by introducing hydrogen into a biodiesel blend derived from algae. The improved thermal properties of hydrogen, when combined with algae biodiesel, significantly affect the performance, combustion, and emissions of dual-fuel engines. A study was conducted to evaluate the impact of hydrogen enrichment levels of 5%, 10%, 15%, and 20% of the nozzle volume on a biodiesel blend fuel. In comparison to diesel, algal biodiesel reduces emissions of unburned hydrocarbons (HC), carbon monoxide (CO), and oxygen (O2) by 5.19%, 3.61%, and 2.83%, respectively, while increasing nitrogen oxide (NO) emissions by 4.73%. In contrast to biodiesel, diesel demonstrated superior brake thermal efficiency (BTE) and lower specific energy consumption (SEC). Injecting hydrogen into A20 blend fuel at volumes of 5%, 10%, 15%, and 20% results in a respective increase in brake thermal efficiency of 2.65%, 2.97%, 3.50%, and 4.15%. The addition of hydrogen gas to biodiesel blends further enhances their combustion qualities, leading to elevated peak cylinder pressure, temperature, and heat release rate. The results indicate that A20H5, A20H10, A20H15, and A20H20 fuel reduced CO emissions by 3.75%, 8.75%, 12.5%, and 16.25%, respectively, compared to the A20 blend. In the same vein, HC emissions decreased by 5.76%, 10.29%, 15.52%, and 18.98%, respectively, as compared to A20 fuel. However, NO emissions rose by 5.36%, 10.20%, 15.28%, and 23.23%, respectively, for A20H5, A20H10, A20H15, and A20H20 test fuels. Ultimately, the utilization of algal biodiesel and hydrogen enrichment in diesel engines was proven to substantially reduce pollutants while increasing efficiency. This study contributes valuable insights into the intersection of renewable fuels, hydrogen enrichment, and engine technology, with the potential to drive significant advancements in sustainable transportation and environmental conservation.

Multi-objective stochastic-robust based selection-allocation-operation cooperative optimization of rural integrated energy systems considering supply-demand multiple uncertainties

The carbon neutrality goal places greater demands on integrated energy system (IES) in rural areas. In line with this trend, a multi-objective stochastic-robust based selection-allocation-operation cooperative optimization model is established for planning of rural IES in this paper. Firstly, this paper construct three typical IESs at three levels (user, township and user-township interaction) and make economic selection. Secondly, in order to coordinate the objectives of technology-economy-environment and solve the multiple uncertainties existing in the system, a multi-objective stochastic-robust planning model is proposed. In the model, the objective function introduces indicators such as total cost, primary energy resource efficiency and renewable energy consumption rate. Then constructing stochastic hierarchical scenarios to characterize the stochastic uncertainty of parameters in the time dimension. And introducing typical scenarios into the box sets which characterize the supply-demand sides uncertainty. Finally, a sensitivity analysis is performed to discuss the influence of robustness parameters and biogas prices. The annual total cost of the user-township interaction system is 53.8 % and 8.4 % lower than that of the other two systems, respectively. And from the allocation results of selected system, primary energy resource efficiency is 0.57, total cost is 1.81 × 106 USD and renewable energy consumption rate is 99.46 %. In addition, from the operation results, it is analyzed that the output of the equipment is perfectly balanced with the user's load. Finally, it is obtained by sensitivity analysis that uncertainty budget and biogas price have an impact on the selection of discrete equipment, the capacity of continuous equipment and the total cost. And the optimal capacity of the system under different situations is given.

Nucleobase-mediated hydrogen bonding crosslinked highly sulfonated poly(ether ether ketone) electrodialysis membranes for efficient waste acid recovery

Electrodialysis is a promising technology for waste acid recovery with the advantages of low energy consumption and high efficiency. In this work, two nucleobase-modified highly sulfonated poly(ether ether ketone) with adenine (SPEEK-50A) and thymine (SPEEK-50T) were successfully synthesized. Then, a series of blending SPEEK-50Ax/50Ty membranes with varying molar ratios of these two functionalized SPEEKs were fabricated by solution casting. The hydrogen bonding crosslinked structure of ‘A-T base pairs’, as well as the acid-base interactions between the incorporated nucleobases and SO3H groups of the polymer backbone, not only enhanced the mechanical properties and thermal stability of the blending membranes, but more importantly, it successfully addressed the over-swelling issue of highly sulfonated SPEEK membrane. The SPEEK-50Ax/50Ty membranes exhibited tensile strengths between 59.3 and 72.3 MPa, which is 2.2–2.6 times that of the pristine SPEEK membrane. And the water uptake of the blending membranes reduced from 116 % to 14.6–19.2 %, while the swelling ratio decreased from 27.1 % to 5.86–6.60 %. The proton affinity of the multiple basic N atoms in nucleobases, as well as the ‘A-T base pairs’ hydrogen bonding network and acid-base interactions between nucleobases and SO3H groups, all can serve as effective proton transport channels. Additionally, the charge repulsion effect of protonated nucleobases and the crosslinking structure is beneficial to the permselectivity. Therefore, the fabricated blending SPEEK-50Ax/50Ty membranes exhibited superior H+ flux (1.66–1.73 mmol m−2 s−1) and enhanced H+/Fe2+ permselectivity (40.3–43.2). The SPEEK-50A0.5/50T0.5 membrane demonstrated a H+ flux of 1.70 mmol m−2 s−1 and a H+/Fe2+ permselectivity of 43.2, which is 2.8 times that of the pristine SPEEK membrane, surpassing both commercially available and recently reported membranes. Therefore, the nucleobase-modified blending SPEEK-50Ax/50Ty membranes might be potential candidates for the efficient recovery of acid from pickling wastewater.

Towards stable and efficient nitrogen removal in wastewater treatment processes via an adaptive neural network based sliding mode controller

Advanced controllers often offer an innovative solution to proper quality control in wastewater treatment processes (WWTPs). However, nonlinearity and uncertain disturbances usually make the conventional control strategies inadequate or impossible for the stable operations of WWTPs. To guarantee the stability of ammonia nitrogen concentration (SNH) control in WWTPs, a direct adaptive neural networks-based sliding mode control (ANNSMC) strategy has been proposed in this article. A sliding mode controller is designed and implemented with the help of an adaptive Neural Network (ANN), named Radial Basis Function Neural Network (RBFNN), which can approach the desired control law accurately. Also, the stability of a system installed with the ANNSMC is analyzed by using the Lyapunov theorem, which ensures system robustness and adaptability. Additionally, to deal with high energy consumption and low treatment efficiency problems in the wastewater denitrification processes, this paper proposes a dual-loop denitrification control strategy and validates it in the Benchmark Simulation Model No.2 (BSM2) platform. The strategy can strengthen the denitrification efficiency by collaborating the SNH with nitrate nitrogen (SNO) concentration in the WWTPs properly. The experimental results demonstrate that the proposed strategy can obtain remarkable stability and robustness, reducing energy consumption effectively compared with other standard and advanced control strategies.

The relationship between energy consumption and GDP in countries of the world

The article provides analysis of the influence of energy consumption on GDP in various regions and countries of the world (America, Europe, Post-Soviet space, Middle East, Africa, Asia-Pacific region). It is shown that: 1) there is a direct linear causal relationship between the growth of energy consumption and economic development in the absence of external effects in the field of energy efficiency; 2) external factors that contribute to the efficiency of energy consumption – ​institutional environment and scientific and technological achievements in the field of energy efficiency; change of the structure of the economy in favor of less energy-intensive industries. Models based on linear algebra, such as the Leontief model, can be used to describe the relationship between energy consumption and economic growth. Energy consumption in different sectors of the economy can have both positive and negative relationships with economic growth. A reduction of the share of industry and agriculture does not guarantee a simultaneous decline of energy consumption in the service sector. There is a direct linear relationship between energy consumption in the services sector as well as in the transportation sector and economic development of different countries around the world. Increased energy consumption in the transportation sector which supports economic development is connected with an increase of the well-being of the population in several countries. The inverse relationship shown by some of the study results does not indicate that all countries studied are more energy efficient. This outcome reflects only the presence of external influences that encourage energy efficiency. Energy saving is not a limiting factor for economic growth, but it does not occur in economic system without a set of external incentives in different sectors of economy.

Mechanical properties, microstructure and life-cycle assessment of eco-friendly cementitious materials containing circulating fluidized bed fly ash and ground granulated blast furnace slag

To reduce CO2 emission from the cement industry and solve environmental issues caused by the accumulation of circulating fluidized bed fly ash (CFA), this study aimed to prepare eco-friendly cementitious materials using CFA and ground granulated blast furnace slag (GGBS) as supplemental cementitious materials. The hydration properties, reaction kinetics, microstructure and life-cycle assessment of blended cements were investigated. The results showed that the compressive strengths of the blended cement samples containing 10 % CFA and 20 % GGBS increased by 7.3 % and 5 % at 7d and 28d, respectively, compared to the pure cement paste. The hydration process was characterized using isothermal calorimetry and analyzed by the Krstulovic-Dabic (K-D) model. The blended cement paste with the best compressive strength exhibited the highest total hydration heat release throughout the hydration process, with extended reaction times. The synergistic effect of CFA and GGBS promoted the formation of C-(A)-S-H gel, consuming more portlandite and leading to a decrease in the most probable pore size and an increase in gel pore volume in the pore structure. From the perspective of cost, economy, and environment, the normalized strength cost, energy consumption efficiency index, and CO2 intensity index can be reduced by 16.1 %, 27.1 %, and 33.3 % respectively. This work contributes to a better understanding of the synergistic mechanism in the CFA-GGBS blended cement system, and serves as a significant reference for the development of economical and eco-friendly cementitious materials.

Cryogenic-membrane separation process for helium extraction and ethane co-production from natural gas

In order to address the defects of high energy consumption and low efficiency in the single cryogenic separation process for helium extraction, this paper integrate post-expansion nitrogen cycle refrigeration with membrane separation to establish a novel natural gas cryogenic-membrane helium recovery process. Based on four mainstream processes for ethane recovery from natural gas, an optimal ethane recovery method is selected and incorporated into the proposed cryogenic-membrane helium recovery process. The process is then simulated using ASPEN HYSYS software. The effects of operating parameters on critical equipment energy consumption, helium product concentration and ethane recovery were evaluated. The simulation results indicate that in the proposed helium recovery process, the concentration of refined helium reaches 99.6 %, and the ethane recovery rate is 90.13 %. Compared to the single helium extraction process, the total compressor energy consumption in our process is reduced by 23.10 %, and the integrated energy consumption is reduced by 20.4 %.

Internet of things and deep learning-enhanced monitoring for energy efficiency in older buildings

Retrofitting older buildings for energy efficiency is paramount in today's sustainability and environmental awareness era. Older buildings contribute greatly to energy waste since they typically lack new energy-efficient technology. Reducing carbon emissions, lowering energy bills, and extending the life of these historic landmarks all depend on fixing the energy inefficiency that plagues these buildings. Despite advanced technologies' remarkable progress, the potential of the Internet of Things and deep learning in older buildings has not been unexplored. Major obstacles include expensive and out-of-date infrastructure and the difficulty of incorporating new technology into historically significant structures. Existing research has mostly ignored older infrastructures' unique requirements and limitations in favour of current or newly built services. In addition, comprehensive research on integrating deep learning with the Internet of Things in this specific environment has been lacking. Smart building energy management is made possible by the Internet of Things (IoT) and deep learning. Architectural limitations, outmoded infrastructure, and the necessity for non-invasive retrofitting solutions all contribute to the difficulty of energy monitoring and improvement in older buildings. This research proposes combining the IoT with Deep Learning-enhanced Predictive Energy Modeling (DL-PEM) to make an energy management system that can change and adapt to the needs of older buildings. Data from IoT sensors is collected on occupancy, temperature, lighting, and equipment usage and then analyzed using Deep Learning models to determine the most efficient energy consumption patterns. Beyond its energy-saving potential, this method has many potential uses. Spotting and fixing structural problems before they become major can improve occupant comfort, reduce maintenance costs, and pave the way for predictive maintenance. Integration with the Smart grid and demand response programs can be facilitated, too, improving the reliability of the power system as a whole. Our IoT and Deep Learning-based energy management solution optimizes energy usage, reduces expenses, and mitigates environmental impact in older buildings, as shown by extensive simulation studies. The system's performance is compared to more conventional methods, and its flexibility is evaluated in various building and usage contexts. The experimental outcomes show the suggested DL-PEM model increases the energy demand forecasting analysis, energy consumption analysis, thermal comfort optimization analysis, seasonal variation analysis, and occupancy data analysis.

Research on time-energy optimal trajectory planning of articulated heavy-duty robot

Articulated heavy-duty robots are more and more widely used in the construction environment. Aiming at the problems of low working efficiency and high energy consumption of this kind of robot, a multi-objective adaptive trajectory planning method combining path optimization and trajectory optimization is proposed to improve the working efficiency of the robot and reduce energy consumption. Firstly, based on kinematics and dynamic analysis considering friction, the energy consumption model of robot trajectory is constructed. Then, according to the posture of the key points in the known workspace, the optimal path points in the joint space are obtained by a path point solution method considering the joint load characteristics. On this basis, a multi-objective model considering running time and energy consumption is established to plan the interpolation trajectory of the joint space of the quintic B-spline curve. Finally, constrained by the velocity, acceleration and torque of the joint of the manipulator, the optimal solution is obtained by using the elite non-dominated sorting genetic algorithm (NSGA-II), and the optimal weight factor is selected by a multi-objective adaptive optimization method to obtain the optimal position-time series. The experimental results show that compared with the quintic polynomial trajectory optimization method, the energy consumption of the proposed method is reduced by 10.90% under the same working efficiency. The research results of this paper provide a new optimization idea for other target trajectory planning.

SSH-MAC: Service-Aware and Scheduling-Based Media Access Control Protocol in Underwater Acoustic Sensor Network

In the framework of the space-air-ground-ocean integrated network, the underwater acoustic sensor network (UASN) plays a pivotal role. The design of media access control (MAC) protocols is essential for the UASN to ensure efficient and reliable data transmission. From the perspective of differentiated services in the UASN, a service-aware and scheduling-based hybrid MAC protocol, named the SSH-MAC protocol, is proposed in this paper. In the SSH-MAC protocol, the centralized scheduling strategy is adopted by sensor nodes with environmental monitoring service, and the distributed scheduling strategy is adopted by sensor nodes with target detection service. Considering the time-varying data generation rate of sensor nodes, the sink node will switch the scheduling mode of sensor nodes based on the specific control packet and adjust the resource allocation ratio between centralized scheduling and distributed scheduling. Simulation results show that the performance of the SSH-MAC protocol, in terms of utilization, end-to-end delay, packet delivery ratio, energy consumption, and payload efficiency, is good.

Systematic Literature Review: Development of The Leach Protocol Algorithm for Efficient Energy Consumption in WSN

Systematic Literature Review: Development of The Leach Protocol Algorithm for Efficient Energy Consumption in WSN

System for monitoring building heat consumption

Relevance. Energy consumption and efficient use of district heating networks affect the environment, society and the economy. Solutions are needed that can significantly reduce specific thermal energy losses. Using a technically and economically feasible way to assess energy efficiency allows these decisions to be made. Currently, assessing the energy efficiency of buildings raises significant questions among specialists. There is not much modern specialized literature in this area. The problem of energy efficiency requires more rigorous attention to urban planning issues. Thus, refined methods for assessing the energy efficiency of buildings make it possible to simultaneously save capital investments and ensure efficient consumption of thermal energy. Aim. To develop a system for assessing the real heat consumption of buildings to ensure optimization of dispatching of centralized heat supply systems in real time. Methods. Computer modeling of the state of heat consumption of buildings with various characteristics and schemes for their connection to centralized heat supply networks; methods of geographic information analysis. Results. The authors have proposed the system for monitoring the heat consumption of buildings. This system allows assessing the energy efficiency of buildings through the maximum use of computer and software technologies. It is shown that the calculated heat flows of buildings used to predict savings when modernizing buildings do not always correspond to the actual heat loads, which can lead to an incorrect assessment of investment projects. The authors obtained graphical dependences of the quantities influencing specific heat losses on various primary independent factors. Conclusions. The constructed system for monitoring the heat consumption of buildings allows, together with the use of geoinformation analysis methods, obtaining a fairly complete picture of the state of heat consumption for city areas and an assessment of the energy characteristics of buildings.

Research on CO2 capture performance of DMEDA water-lean absorbents based on molecular dynamics

In recent years, a large number of studies have shown that amine based water-lean absorbents formed by amines and organic solvents exhibit high capture efficiency and low regeneration energy consumption in CO2 capture. However, there is a lack of research that combines molecular level structure with macroscopic properties. In order to optimize the performance of amine based homogeneous water-lean absorbents by utilizing the properties of organic solvents, and to explore the correlation between the macroscopic properties of the absorbents (CO2 capacity, removal efficiency, regeneration rate, regeneration efficiency, etc.) and the molecular structure of the absorbent components. We used experiments and molecular dynamics to study the linear terminal diamine DMEDA and non-proton polar solvent (DMF, DMPA, NMP, SFL, DMSO) system. It was found that the absorption and desorption performance of water-lean solvents was higher than that of aqueous solutions 30 wt% MEA and 30 wt% DMEDA, among which EFH with DMF as the solvent has better comprehensive performance. The molecular dynamics simulation was built to describe the solution structures of these absorbents. Through RDF calculations, the unique behavior of DMEDA in non-proton polar solvent was revealed on molecular level, it was found that organic solvents tend to aggregate with DMEDA molecules to form large molecular clusters, while CO2 molecules may diffuse through ordered pore structures, resulting in a significant improvement in the capture performance. And it was found that there is a quantitative relationship between the intensity of intermolecular interaction between DMEDA and CO2 and the removal efficiency change in DMEDA water-lean absorbents. A functional relationship was fitted, and a preliminary and rapid method for determining capture performance through molecular dynamics was proposed.

Utilizing Domain Engineering to Achieve High-Polarization Relaxor Ferroelectrics for Low-Consumption Ceramic Capacitors.

This study focuses on incorporating NaNbO3 (NN) into the Ba0.85Ca0.15Zr0.9Ti0.1O3 (BCZT) lattice to form (1 - x)BCZT-xNN ceramics. Although antiferroelectricity was not observed, an observed domain-movement-diminishment behavior with increasing NN dopant induced the formation of high polarization walls (HPWs) between adjacent C-phases. The 0.90BCZT-0.10NN composition exhibited superior polarization compared to most BCZT-based ferroelectrics, as validated by mathematical derivation. Integration of these findings revealed a Wrec of 3.86 J/cm3 at 360 kV/cm, with a high Wrec/Eb ratio defining energy consumption efficiency in dielectric capacitors. This work introduces a novel approach to fabricating low-consumption dielectric capacitors. Additionally, a significantly high Wrec of 5.36 J/cm3 was achieved with an NN dopant concentration of 0.30.

The Impact of Financial Efficiency and Renewable Energy Consumption on CO2 Emission Reduction in GCC Economies: A Panel Data Quantile Regression Approach

As prominent oil producers, Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world’s attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC’s economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. This study employs quantile regression, a robust statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several essential insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th, −83,537.3) and higher quantiles (90th, −549,002.3). The relationship between the GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth–environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a more substantial negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. This study’s outcomes hold crucial policy implications for GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions.

Performance and Mechanism of Co and Mn Loaded on Fe-Metal-Organic Framework Catalysts with Different Morphologies for Simultaneous Degradation of Acetone and NO by Photothermal Coupling.

VOCs can be used instead of ammonia as a reducing agent to remove NO, achieving the effect of removing VOCs and NO simultaneously. Due to the high energy consumption and low photocatalytic efficiency required for conventional thermocatalytic purification, photothermal coupled catalytic purification can integrate the advantages of photocatalysis and thermocatalysis in order to achieve the effect of pollutants being treated efficiently with a low energy consumption. In this study, samples loaded with Co and Mn catalysts were prepared using the hydrothermal method on Fe-MOF with various morphologies. The catalytic performance of each catalyst was analyzed by studying the effects of their physicochemical properties through various characterizations, including XRD, SEM, BET, XPS, H2-TPR, TEM and O2-TPD. The characterization results demonstrated that the specific surface area, pore volume, high valence Co and Mn atoms, surface adsorbed oxygen and the abundance of oxygen lattice defects in the catalysts were the most critical factors affecting the performance of the catalysts. Based on the results of the performance tests, the catalysts prepared with an octahedral-shaped Fe-MOF loaded with Co and Mn showed a better performance than those loaded with Co and Mn on a rod-shaped Fe-MOF. The conversions of acetone and NO reached 50% and 64%, respectively, at 240 °C. The results showed that the catalysts were capable of removing acetone and NO at the same time. Compared with the pure Fe-MOF without Co and Mn, the loaded catalysts showed a significantly higher ability to remove acetone and NO simultaneously under the combination of various factors. The key reaction steps for the catalytic conversion of acetone and NO on the catalyst surface were investigated according to the Mars-van Krevelen (MvK) mechanism, and a possible mechanism was proposed. This study presents a new idea for the simultaneous removal of acetone and NOx by photothermal coupling.

Efficient methane oxidation to formaldehyde via photon–phonon cascade catalysis

The oxidation of methane to value-added chemicals provides an opportunity to use this abundant feedstock for sustainable petrochemistry. Unfortunately, such technologies remain insufficiently competitive due to a poor selectivity and a low yield rate for target products. Here we show a photon–phonon-driven cascade reaction that allows for methane conversion to formaldehyde with an unprecedented productivity of 401.5 μmol h−1 (or 40,150 μmol g−1 h−1) and a high selectivity of 90.4% at 150 °C. Specifically, with a ZnO catalyst decorated with single Ru atoms, methane first reacts with water to selectively produce methyl hydroperoxide via photocatalysis, followed by a thermodecomposition step yielding formaldehyde. Single Ru atoms, serving as electron acceptors, improve charge separation and promote oxygen reduction in photocatalysis. This reaction route with minimized energy consumption and high efficiency suggests a promising pathway for the sustainable transformation of light alkanes.

Optimization of operation and efficiency of energy consumption in cable transport

El trabajo de investigación realizao se centra en el estudio y monitorización de instalaciones de transporte por cable, como sistema global de transporte, y no como un simple conjunto de subsistemas, dado que uno de los objetivos es su mejora en cuanto a eficiencia global tanto de consumo energético, como de desgaste de sus componentes y rendimiento en cuanto a horas de funcionamiento por elementos transportados