Ultrafast Conversion of Water and Oxygen Molecules With Dissociation of Hydrogen Bonding Effect to Achieve Extra-High Energy Efficiency of Secondary Metal-Air Batteries.

Metal-air secondary batteries with ultrahigh specific energies have received vast attention and are considered new promising energy storage. The slow redox reactions between oxygen-water molecules lead to low energy efficiency (55-71%) and limited applications. Herein, it is proposed that the MIL-68(In)-derived porous carbon nanotube supports the CoNiFeP heteroconjugated alloy catalyst with an overboiling point electrolyte to achieve the ultrahigh oxidation rate of water molecules. Structural characterization and density functional theory calculations reveal that the new catalyst greatly reduces the free energy of the process, and the overboiling point further accelerates the dissociation of O─H and hydrogen bonds, and the release of O2 molecules, achieving an extra-low overpotential of 110 mV@10 mA cm-2 far lower than commercial Ir/C catalysts of 192 mV at 125 °C and state-of-the-art. Furthermore, the energy efficiency of assembled rechargeable zinc-air batteries begins to break through at 85 °C, jumps at 100 °C, and reaches ultrahigh energy efficiency of 88.1% at 125 °C with an ultralow decay rate of 0.0068% after 150 cycles far superior to those of reported metal-air batteries. This work provides a new catalyst and electrolyte joint-design strategy and reexamines the battery operating temperature to construct higher energy efficiency for secondary fuel cells.

Energy security and its determinants in New Zealand.

New Zealand relies on imported fossil fuels for about 38% of its primary energy. The country's energy demand is expected to grow due to population and economic growth, which will put more pressure on the energy system. Besides, resource scarcity, energy price volatility, and environmental challenges have made energy security a major concern for New Zealand and other countries. Given the lack of significant research on the effects of energy security factors in New Zealand, this study aims to shed light on the primary determinants of energy security using the dynamic autoregressive distributed lag method based on time series data from 1978 to 2021. The study found that a long-run link exists between energy security and energy intensity (energy efficiency), renewable energy use, fossil fuel consumption, and global oil prices. Real GDP, renewable energy consumption, and energy security were found to improve energy security, while fossil fuel consumption and world oil prices had a negative impact. The study also revealed a one-way causality from real GDP, fossil fuel consumption, and renewable energy use to energy security. In contrast, the relationship between energy intensity and energy security is bidirectional. Simulation results showed that global crude oil prices have a lower impact on energy security compared to other variables and are most responsive to a 5% shock in fossil fuel consumption, followed by economic growth.

Unified control of diverse actions in a wastewater treatment activated sludge system using reinforcement learning for multi-objective optimization.

The operation of modern wastewater treatment facilities is a balancing act in which a multitude of variables are controlled to achieve a wide range of objectives, many of which are conflicting. This is especially true within secondary activated sludge systems, where significant research and industry effort has been devoted to advance control optimization strategies, both domain-driven and data-driven. Among data-driven control strategies, reinforcement learning (RL) stands out for its ability to achieve better than human performance in complex environments. While RL has been applied to activated sludge process optimization in existing literature, these applications are typically limited in scope, and never for the control of more than three actions. Expanding the scope of RL control has the potential to increase the optimization potential while concurrently reducing the number of control systems that must be tuned and maintained by operations staff. This study examined several facets of the implementation of multi-action, multi-objective RL agents, namely how many actions a single agent could successfully control and what extent of environment data was necessary to train such agents. This study observed improved control optimization with increasing action scope, though control of waste activated sludge remains a challenge. Furthermore, agents were able to maintain a high level of performance under decreased observation scope, up to a point. When compared to baseline control of the Benchmark Simulation Model No. 1 (BSM1), an RL agent controlling seven individual actions improved the average BSM1 performance metric by 8.3 %, equivalent to an annual cost savings of $40,200 after accounting for the cost of additional sensors.

The nexus between foreign direct investment, economic progress, and quality of institutions in fostering sustainable energy efficiency: Evidence from BRICS economies.

This paper analyzes the relationship between Foreign Direct Investment (FDI), economic growth, and institutional quality to maintain sustainable energy efficiency in BRICS. The objective of our study is to decompose which elements collectively impact the uptake of sustainable energy practices. A comprehensive dataset and an advanced econometric model Data Envelopment Analysis (DEA) are employed to investigate the dynamics at play. It has been done through comprehensive research to understand these FDI mechanisms driving the sustainable energy transition, bringing forth the fundamental role of strong institutions and sustained growth. In contrast to existing models, the analysis incorporates institutional quality, providing a fresh perspective on the impact of this factor on FDI and economic development in the BRICS economies. Findings show the crucial position FDI holds in developing sustainable energy and the institutional structure's effectiveness in accomplishing the current objectives. We have kept the position of economic growth, which serves as the essential driver for environmentally friendly use of energy resources. Our results have shown that FDI in sustainable energy is a requisite for economic growth improvement and the need for such progress to be supported by effective institutions to facilitate intra-regional investments.

Signatures of Bayesian inference emerge from energy-efficient synapses.

Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mechanisms cost energy. We examined four such mechanisms along with the associated scaling of the energetic costs. We then embedded these energetic costs for reliability in artificial neural networks (ANNs) with trainable stochastic synapses, and trained these networks on standard image classification tasks. The resulting networks revealed a tradeoff between circuit performance and the energetic cost of synaptic reliability. Additionally, the optimised networks exhibited two testable predictions consistent with pre-existing experimental data. Specifically, synapses with lower variability tended to have (1) higher input firing rates and (2) lower learning rates. Surprisingly, these predictions also arise when synapse statistics are inferred through Bayesian inference. Indeed, we were able to find a formal, theoretical link between the performance-reliability cost tradeoff and Bayesian inference. This connection suggests two incompatible possibilities: evolution may have chanced upon a scheme for implementing Bayesian inference by optimising energy efficiency, or alternatively, energy-efficient synapses may display signatures of Bayesian inference without actually using Bayes to reason about uncertainty.

Cu Based Dilute Alloys for Tuning the C2+ Selectivity of Electrochemical CO2 Reduction.

Electrochemical CO2 reduction is a promising technology for replacing fossil fuel feedstocks in the chemical industry but further improvements in catalyst selectivity need to be made. So far, only copper-based catalysts have shown efficient conversion of CO2 into the desired multi-carbon (C2+) products. This work explores Cu-based dilute alloys to systematically tune the energy landscape of CO2 electrolysis toward C2+ products. Selection of the dilute alloy components is guided by grand canonical density functional theory simulations using the calculated binding energies of the reaction intermediates CO*, CHO*, and OCCO* dimer as descriptors for the selectivity toward C2+ products. A physical vapor deposition catalyst testing platform is employed to isolate the effect of alloy composition on the C2+/C1 product branching ratio without interference from catalyst morphology or catalyst integration. Six dilute alloy catalysts are prepared and tested with respect to their C2+/C1 product ratio using different electrolyzer environments including selected tests in a 100-cm2 electrolyzer. Consistent with theory, CuAl, CuB, CuGa and especially CuSc show increased selectivity toward C2+ products by making CO dimerization energetically more favorable on the dominant Cu facets, demonstrating the power of using the dilute alloy approach to tune the selectivity of CO2 electrolysis.

Recent Developments in AI and ML for IoT: A Systematic Literature Review on LoRaWAN Energy Efficiency and Performance Optimization.

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance.

A Survey on Green Enablers: A Study on the Energy Efficiency of AI-Based 5G Networks.

In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in 5th-generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs), such as user experience, quality of service (QoS), or other important aspects of the network. Advanced wireless technologies have been integrated into 5G network designs at multiple network layers to address this difficulty. The integration of emerging technology trends, such as machine learning (ML), which is a subset of artificial intelligence (AI), and AI's rapid improvements have made the integration of these trends into 5G networks a significant topic of research. The primary objective of this survey is to analyze AI's integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

Evaluation of regional comprehensive development efficiency under low-carbon policy: based on optimized DDF-GML combined with unsupervised clustering method.

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist-Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.

Factor mismatch measurement of resource-based cities: Evidence from all-round optimization total factor productivity decomposition framework.

In-depth analysis of the factors affecting the transformation of resource-based cities can provide effective support for the transformation and development of resource-dependent regions. How to comprehensively identify the factors affecting the transformation of resource-based cities is a complex problem. This study starts from the total factor productivity model and focuses on the two core basic factors that affect the transformation process of cities reliant on resources. Economic benefits and energy efficiency, respectively, from the economic benefit analysis framework and energy efficiency analysis framework for reconstruction, the two frameworks are combined with the use of distorted prices of resource elements to solve the problem that the synergistic effect of economic benefits and energy efficiency can not be measured. In order to quantitatively analyze the factors that affect the development efficiency of cities reliant on resources under the single or synergistic effect of the comprehensive framework, this study optimizes the directional distance function from three perspectives: exogenous weight, direction vector endogeneity, and absolute distance transformation relative distance, thus achieving an accurate assessment transformation efficiency of cities reliant on resources. Considering the impact of the new coronavirus epidemic, this study only selected the data of resource-based cities from 2003 to 2018, and found through model calculation that the impact on the transformation of cities reliant on resources: (1) Labor mismatch is mainly achieved by affecting the structure about the production of resource-based enterprises and industrial human resources; (2) Capital mismatch is mainly realized by affecting the production of resource-based enterprises; (3) Energy mismatch is mainly achieved by affecting high energy consumption enterprises and low production technology level enterprises. Further research shows that the main objects of these factors are the four parts of production technology level, energy consumption, total factor productivity and industrial structure. Through these contents, they affect environmental efficiency and deeply affect the transformation process of resource-based cities.

Urban green total factor energy efficiency and its decomposition under a multidimensional heterogeneity framework: a case study of the Yellow River Basin, China.

Assessing and monitoring the green total factor energy efficiency (GTFEE) of cities while considering technology heterogeneity is crucial for the development of energy-conservation and emission-reduction policies. Considering that the heterogeneity of production technologies encompasses several dimensions, this paper proposes a 3E3S (Economy-Environment-Energy-Society-Science-Space) heterogeneity framework and integrates it with the improved meta-frontier global SBM-undesirable to analyze GTFEE and its decomposition. Empirical analysis of cities in the Yellow River Basin of China (YRBC) highlights the following: (1) The 3E3S heterogeneity framework facilitates the classification of all cities into three distinct groups, a finding that contrasts significantly with previous outcomes documented in the literature that relied solely on criteria such as geographic location. (2) The three groups identified under the meta-frontier exhibit substantial energy-saving potentials of 24.49%, 35.17%, and 52.46%, respectively. Additionally, there are spatiotemporal variations in GTFEE, with cities located in the central part of YRBC, particularly those in Shanxi province, demonstrating poor GTFEE performance. (3) The decomposition analysis of GTFEE indicates that technological progress plays a pivotal role in enhancing GTFEE on the whole, albeit with varying approaches for improving GTFEE depending on individual cities.

Towards a low-carbon and beautiful world: assessing the impact of digital technology on the common benefits of pollution reduction and carbon reduction.

When ecology thrives, civilization thrives, and when ecology declines, civilization declines. Based on panel data from 30 provinces in China from 2000 to 2021, this study used marginal abatement costs to estimate the co-benefits of pollution reduction and carbon reduction. Two-way fixed effect and two-stage intermediary effect models were used to evaluate the impact of digital technology on co-benefits and its indirect channels. The results indicated that China's total carbon emissions maintained a steady growth trend, while air pollution showed a fluctuating declining trend. Reaching peak carbon neutrality calls for more innovative solutions. Under joint emission reduction efforts, the study revealed marginal abatement cost savings of 535.8 million yuan/million tons and 6216.5 million yuan/µg/m3 for carbon reduction and pollution reduction, respectively. Most importantly, the study confirmed that joint emission reduction programs can reduce environmental governance costs more than individual emission reductions can, and the co-benefits increased from 37.983 to 44.757. The co-benefits generally showed a trend of fluctuation and increases and had the characteristics of phased transformation. Intragroup differences and cross-overlapping between regions made regional differences in co-benefits obvious. The subversive, permeable, and integrated features of digital technology have resulted in the all-around transformation of the economy and society, and the new technology-economy paradigm has significantly improved co-benefits. The conclusion remains valid after robustness testing and controlling for endogeneity problems. The results of the mechanism analysis suggest that digital technology can indirectly improve synergies through the intermediary channels of fostering green technology innovation, reducing energy consumption intensity and improving the energy structure.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

CVD Grown CNTs-Modified Electrodes for Vanadium Redox Flow Batteries.

Vanadium redox flow batteries (VRFBs) are of considerable importance in large-scale energy storage systems due to their high efficiency, long cycle life and easy scalability. In this work, chemical vapor deposition (CVD) grown carbon nanotubes (CNTs)-modified electrodes and Nafion 117 membrane are utilised for formulating a vanadium redox flow battery (VRFB). In a CVD chamber, the growth of CNTs is carried out on an acid-treated graphite felt surface. Cyclic voltammetry of CNT-modified electrode and acid-treated electrode revealed that CNTs presence improve the reaction kinetics of V3+/V2+ and VO2+/VO2+ redox pairs. Battery performance is recorded for analysing, the effect of modified electrodes, varying electrolyte flow rates, varying current densities and effect of removing the current collector plates. CNTs presence enhance the battery performance and offered 96.30% of Coulombic efficiency, 79.33% of voltage efficiency and 76.39% of energy efficiency. In comparison with pristine electrodes, a battery consisting CNTs grown electrodes shows a 14% and 15% increase in voltage efficiency and energy efficiency, respectively. Battery configured without current collector plates performs better as compared to with current collector plates which is possibly due to decrease in battery resistance.

Application of triple-branch artificial neural network system for catalytic pellets combustion.

On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to move to biofuels, including biomass. This paper examines the impact of placing a catalytic system in a retort boiler to minimize emissions of greenhouse gases, dust and other pollutants when burning pellets. The effect of platinum, and oxides of selected metals placed on the deflector as a solid catalyst was studied. Based on the experimental data, a branched artificial neural network was constructed and trained. The routing of three parallel topologies made it possible to achieve high accuracy while keeping the input data relatively simple. The system showed an average error of 3.54% against arbitrary test data. On the basis of experimental data as well as predictions returned by the artificial neural network, recommendations were shown for the catalysts used and their amounts. Depending on the biomass from which the pellet was produced, the experiment suggested the use of titanium or copper oxides. In the case of the neural network, it was able to select a better system, based on platinum, improving emission reductions by up to more than 19%, depending on the type of pellet used.

Can forests realize the carbon neutrality dream? Evidence from a global sample.

The enlarge in economic activities and the urban population at the global level has brought about an increase in the demand for energy, food, and natural resources, as well as an exacerbation in global climate change concerns. In this respect, it is important to ensure the balance between global climate change and global economic activities. Therefore, a wide literature has emerged that searches for alternative solutions to improve climate change and carbon dioxide (CO2) emissions. The majority of existing studies emphasize the importance of renewable energy sources in environmental improvement efforts. Few studies highlight the importance of forestation in environmental improvement efforts, highlighting the non-linear effects of forestation. To fill this gap, this study uses panel data from 181 countries between 1990 and 2022 and evaluates the non-linear impact of economic growth, forest extent, energy efficiency, and urban growth on per capita CO2 emissions using a dynamic panel threshold and dynamic panel quantile threshold methods. Furthermore, we extend the model and conduct robustness tests examining the non-linear threshold effects of renewable and non-renewable energy consumption on per capita CO2 emissions. Our findings provide pieces of evidence that forest extents are an alternative solution to renewable energy use and energy efficiency in environmental improvement efforts.

A puzzle on environmental quality and energy security.

i) This study examines the determinants of environmental quality. It is not possible to fully analyze the complex network that emerges from the set of interactions of these determinants, both with each other and with environmental security. Indeed, a number of variables and relationships hidden in the background of the puzzle such as 'game theoretical interactions between economies on energy security', characterize this network. However, this study, which includes energy security and environmental quality simultaneously, may open the door to revealing the key patterns of the current network. ii) This study, which investigates the network between environmental problems and energy security, provides empirical evidence that these two variables may well evolve by positively affecting each other under some conditions. iii) Using the current and sophisticated econometric methods such as CDw + based on Juodis and Reese (2022) test and CS-ARDL Model, over a panel of top 20 energy-using countries in the period 1980-2018, the empirical analysis of the article shows that an increase in energy security risk positively affects environmental quality in aggregate by motivating increased energy efficiency, triggering environmental awareness and regulations, and stimulating research and development activities for clean energy etc. Technologies. Therefore, this study concludes that potential policies and reforms, including reducing fossil fuel consumption, increasing energy efficiency in distribution and consumption, encouraging investments in clean energy are of key importance in making energy security sustainable in the long term by increasing environmental quality.

An empirical analysis to examine the role of institutions in bridging the gap between environmental policy stringency and energy poverty.

The findings related to strict environmental policies and energy poverty have been found contradictory. Strict environmental regulations may protect the environment by enhancing renewable energy resources but at the same time, major dependent sectors and consumers reliance on non-renewable energy resources face the problem of energy poverty. Moreover, environmental policies either soft are strict and depend on stakeholders' preferences, and such policies are implemented through institutions. Considering these aspects, the current study examines the impact of environmental policy stringency on energy poverty and further examines the role of institutions in bridging the gap between environmental policy stringency and energy poverty for the selected 31 countries from 1996 to 2020. For empirical analysis, Pooled OLS, random effect model, and system generalized methods of moments (GMM) are applied. To check the robustness of baseline models, spatial lag, spatial error, and feasible generalized methods of moments are applied. Furthermore, to examine the mediating role of institutions, we applied the structural equation modeling technique. Empirical analysis shows that an increase in environmental policy stringency significantly increases energy poverty, while institutional proxies significantly decrease energy poverty. The interactive effects of institutional proxies indicate declining effects on energy poverty. More importantly, institutions act as important mediators between environmental policies and energy poverty. Based on the findings, this study recommends soft environmental policies to maintain a balance between a sustainable environment and minimum energy poverty. This study further recommends authorizing and strengthening the institutions to formulate and regulate balanced environmental policies for environmental safety along with reduced energy poverty. This study further recommends increases in urbanization, personal remittances, and enhanced energy efficiency to minimize energy poverty.

Investigation of thermal alkaline pretreatment of primary and waste activated sludge on the energy efficiency of sludge digestion.

Because of the naturally limited anaerobic degradability and limited biogas yield of raw sludge (RS), this study aims to increase the biogas production of primary sludge (PS) and waste activated sludge (WAS) by the integration of thermal alkaline process (TAP). PH 11 is confirmed to be the most suitable pH value for the TAP of both sludges. Moreover, with the pretreatment at pH 11 and 160 °C (6 bar) for 30 min, the investigated PSs and WASs achieved an increased biogas production of up to 81 % and 72 %, respectively. The improved net electricity production of WASs after TAP varied between 15-43 % compared to conventional WAS digestion. However, the TAP of PS at pH 11 enhanced the biogas production by 1-81 %, which did not constantly contribute to an improved net electricity production.

The impact of photovoltaic self-consumption on the daily electricity demand in Spain: Definition of a model to estimate it.

Introduction: A radical shift in energy production is underway worldwide, replacing fossil fuels with renewable sources and causing structural changes in power generation systems. Problem statement: Photovoltaic installations for self-consumption have experienced a steep increase in recent years. They have reached a significant installed capacity to cause a noticeable reduction in consumption from the national grid, which can cause serious management problems. Objectives: In this work, the evolution of the Spanish demand in the last years is analyzed to identify the influence of self-consumption in the overall demand. In addition, a mathematical model is defined to estimate this influence. Methodology: The demand curves of equivalent days in years with high and low installed self-consumption photovoltaic systems have been compared. Then, an estimation of the electricity generated with this source is proposed, with a mathematical model that takes into account data on solar radiation, installed photovoltaic power for self-consumption and other relevant factors. Results: The analysis of the demand has shown a significant reduction of the electricity demand in daylight hours when the number of self-consumption photovoltaic systems increases. Moreover, the proposed model has been able to provide an estimation of the electricity generated with this source. The addition of these estimates to the actual consumption curves of years with a high number of self-consumption installations gives profiles close to those obtained when self-consumption was low. Recommendation: New storage systems need to be implemented and grid management need to be improved to take advantage of the surpluses produced by photovoltaic systems.