Energy-related Problems Research Articles

Construction and Sampling of Alloy Cluster Expansions—A Tutorial

Crystalline alloys and related mixed systems make up a large family of materials with high tunability that have been proposed as the solution to a large number of energy-related material design problems. Because of the presence of chemical order and disorder in these systems, neither experimental efforts nor computational methods alone are sufficient to span the inherently large configuration space. Therefore, fast and accurate models are necessary. To this end, cluster expansions have been widely and successfully used for the past few decades. Cluster expansions are generalized Ising models designed to predict the energy of any atomic configuration of a system after training on a small subset of the available configurations. Constructing and sampling a cluster expansion consists of multiple steps that have to be performed with care. In this tutorial, we provide a comprehensive guide to this process, highlighting important considerations and potential pitfalls. The tutorial consists of three parts, starting with cluster expansion construction for a relatively simple system, continuing with strategies for more challenging systems such as surfaces, and closing with examples of Monte Carlo sampling of cluster expansions to study order-disorder transitions and phase diagrams. Published by the American Physical Society 2024

A solid acid derived from fishbone catalyzes the hydrolysis of cellulose into nanocellulose

The necessity to look into waste biomass resource regeneration has increased due to growing environmental and energy-related problems. This study successfully developed an innovative fishbone-derived carbon-based solid acid catalyst using the carbonation-sulfonation method, which was subsequently applied to catalyze the hydrolysis of cellulose to produce nanocellulose. The data analysis reveals that the sulfonation treatment affects the microstructure of the catalyst, resulting in a decline in its specific surface area (134.48 m2/g decreased to 9.66 m2/g). However, this treatment doesn't hinder the introduction of acidic functional groups. In particular, the solid acid catalyst derived from fishbone exhibited a total acid content of 3.76 mmol/g, with a concentration of -SO3H groups at 0.48 mmol/g. Furthermore, the solid acids originating from fishbones manifested remarkable thermal stability, exhibiting a mass loss of <15 % at temperatures up to 600 °C. Moreover, the catalyst displayed exceptional catalytic performance during the cellulose hydrolysis reaction, achieving an optimum nanocellulose yield of 45.7 % at an optimized reaction condition. An additional noteworthy feature is the solid acid catalyst's impressive recyclability, maintaining a nanocellulose yield of 44.87 % even after undergoing five consecutive usage cycles. This research outcome underscores an innovative approach to for the sustainable utilization of waste biomass resources.

Energy use in residential buildings for sustainable development: The fifth Solar Decathlon Europe revelations

This research focuses on achieving sustainable development in residential buildings with energy use. Under the influence of the energy crisis and related problems, research on residential buildings for less energy use has great potential. The literature review, according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses, and including VOSviewer analysis, shows the research is increasing and meaningful. Solar Decathlon buildings are used as the main objects in this research. The fifth Solar Decathlon Europe energy use technologies are examined through onsite investigation and online searching. The Analytic Hierarchy Process method for multi-criteria decision analysis is used for sustainability assessment. Moreover, the Ladybug and ClimateStudio plugins simulated respectively the annual solar radiation and the best angle for receiving it. The main findings show that 34 kinds of technologies used in these buildings can be classified into two categories in three directions. Passive technologies should be applied and prioritized, but generating renewable energy is also important. Some infrequently used technologies are not insignificant. The research shows that the combination of technologies decides sustainability performance, but the quantity used does not. Furthermore, energy use also needs to be balanced and coordinated in combination with architectural aesthetics. This research on energy use in residential buildings is beneficial for achieving sustainable development.

A review on CO&lt;sub&gt;2&lt;/sub&gt; capture over novel adsorbents: Progress in robust zeolite adsorbent development

Indubitably, the combustion of fossils fuels has really hampered the preservation of the environment as it raises the content of CO2 in the atmosphere which consequentially results in global warming. Adsorption process remains the popular technique owing to its cost-effectiveness, faster reaction rates and flexible design. This review detailed the research progress in preparation of modified zeolite-based and novel adsorbents towards enhanced CO2 capture. In addition, the review presents an overview on available techniques of capturing CO2 and mechanism of reaction. Large surface area, distinctive mechanical characteristics and uniform dispersion of the exchangeable cations in the porous framework is prerequisite for high adsorption capacity and stability over zeolite materials. Novel nanostructured and polymeric zeolite composite materials seem promising because they offer solutions to energy-related problems while also contributing to environmental preservation. It is anticipated that this review could offer a conclusive roadmap in the pursuit of a cost-effective, industrially potent adsorbent suited for enhance CO2 capture.

Novel variants conceptional technology to produce eco-friendly sustainable high octane-gasoline biofuel based on renewable gasoline component

The creation of green engine fuels from renewable and sustainable gasoline additives is essential as a consequence of ecological concerns and the energy problem. Bio-alcohols are currently a promising sustainable fuel substitute for gasoline in automotive transportation. In this perspective, the ongoing investigation intends to evaluate the influence of sustainable and renewable petrol additives, like isopropanol, which has outstanding anti-detonation properties and great chemical and physical characteristics on reformable naphtha (HSRN) and light naphtha (LSRN), which possess low chemical and physical characteristics, and low octane rating. Additionally, the second goal is to create biofuel with an eco-octane number of high by blending other gasoline components than those previously indicated. Several gasoline fuels and experimental setups compliant with European standard norms were used to carry out the current work. The results reported that gasoline RON92, RON95, and RON98 were produced based on isopropanol as a renewable and sustainable resource. Moreover, the quantity and quality of light naphtha were maximized. Likewise, the laboratory findings reported that the antiknocking efficiency could be ranked sequentially as isopropanol > reformate > isomerate > fluidized catalytic cracked gasoline (FCC) > PRF-70 > LSRN > HSRN by octane rating. Furthermore, the Reid vapor pressure (RVP) for petrol RON92 was bigger than gasoline RON95 and RON98, unlike the density at 15 °C for gasoline RON 98 was bigger than gasoline RON92 and RON95. Finally, major problems like energy, water scarcity, and the environment affect people all around the world. Two of the earlier environmental and energy-related problems will be resolved by using these sustainable and renewable additions.

Enhanced selectivity of photoreduction CO2 over Ag/C3N5 Schottky heterostructure by the SPR effect

The development of effective photocatalysts for CO2 photocatalytic reduction is a crucial step in resolving environmental and energy-related problems. The Schottky heterostructure of Ag/C3N5 has been constructed to improve its photocatalytic activity by utilising the surface plasmon resonance effect of Ag. Notably, Ag/C3N5 exhibited a remarkable 100% selectivity for CH4 without the use of any sacrificial reagents under the condition of visible light, and the productivity reached 1.1 μmol·g−1·h−1 as compared with 0.4 μmol·g−1·h−1 for pure C3N5. Experiments and density functional theory (DFT) calculations have proven that when Ag and C3N5 come into contact, the Schottky junction formed between them promotes the aggregation of photogenerated electrons to the Ag surface, which is favourable to the efficiency of electron–hole separation. Simultaneously, Ag acts as the active site of the reaction to promote CH4 generation. We expect that this simple method based on low-cost C3N5 can be extended to the preparation of metal–C3N5 heterojunctions and applied to CO2 photocatalytic reduction processes with high product selectivity.

A Drug Discovery Approach to a Reveal Novel Antioxidant Natural Source: The Case of Chestnut Burr Biomass.

Currently, many environmental and energy-related problems are threatening the future of our planet. In October 2022, the Worldmeter recorded the world population as 7.9 billion people, estimating that there will be an increase of 2 billion by 2057. The rapid growth of the population and the continuous increase in needs are causing worrying conditions, such as pollution, climate change, global warming, waste disposal, and natural resource reduction. Looking for novel and innovative methods to overcome these global troubles is a must for our common welfare. The circular bioeconomy represents a promising strategy to alleviate the current conditions using biomass-like natural wastes to replace commercial products that have a negative effect on our ecological footprint. Applying the circular bioeconomy concept, we propose an integrated in silico and in vitro approach to identify antioxidant bioactive compounds extracted from chestnut burrs (an agroforest waste) and their potential biological targets. Our study provides a novel and robust strategy developed within the circular bioeconomy concept aimed at target and drug discovery for a wide range of diseases. Our study could open new frontiers in the circular bioeconomy related to target and drug discovery, offering new ideas for sustainable scientific research aimed at identifying novel therapeutical strategies.

Ni catalysts modified with mesoporous silica support: The role of Pr2O3 and MgO on the methanation of CO2

One way to solve environmental and energy related problems is to use carbon dioxide as a raw material along with renewable H2 to make useful chemicals and goods from those alternative energy sources. Several compositions of xNi-MCM-41/MgO-Pr2O3 were used for CO2 methanation via the hydrothermal synthesis method. Techniques such as TEM, H2-TPD, XRD, SEM, and BET were used to study the influence of the mechanism of the catalyst during CO2 methanation. The best performance was obtained for the 1.5 % Ni-MCM-41/MgO-Pr2O3 catalyst at 400 °C and normal atmospheric pressure, where the CO2 conversion efficiency and selectivity were 87.4 and 99.9 %, respectively. It was thought that the synergistic effects between the Ni active sites, the promoter, and the support were important for the high reactivity of the catalysts. These effects included better Ni dispersion and more CO2 adsorption sites due to the inclusion of MgO-Pr2O3. The promoted catalyst retained its high reactivity after 30 h on stream as observed during the stability test.

Engineering Design Process (EDP)-Based Learning to Enhance High School Students' Creativity in Alternative Energy Topics

The energy issue is one of the 17 Sustainable Development Goals (SDGs), which is a global development agenda that all countries, including Indonesia, must follow. The curriculum plays a crucial role in enhancing students' energy literacy. It is expected that with the energy literacy possessed by students, they will have a good understanding of energy, which will encourage them to use energy wisely. Additionally, it is hoped that creative ideas from students will emerge to address energy-related problems. Based on this background, this research aims to enhance students' creativity in addressing energy issues. The research method employed is a quantitative research method with a Pretest-Posttest Nonequivalent Control Group Design, which is a form of Quasi Experimental Design. The subjects in this study are twelfth-grade science students (XII MIPA) selected through purposive sampling and divided into two groups, namely the control group and the experimental group. The control group uses a project-based learning model, while the experimental group uses project-based learning based on the engineering design process. The number of samples for the experimental group is 33 students, and for the control group, it is 34 students, making a total of 67 research subjects. The creative thinking skills of students are measured using a descriptive test that includes indicators of creative thinking skills, while student creativity in their products is measured using an assessment sheet for creative products based on presentations and testing of creative products carried out at the end of the learning process. The data analysis results show that there is an improvement in students' creative thinking skills, as indicated by the average N-Gain achievement of 53.3%, falling into the moderate category. Additionally, the creativity of student products in all creative product indicators is rated as highly creative, with an average score of 89. The Mann Whitney U Test results indicate a significant difference in the improvement of creative thinking skills and student creativity in their products

Two‐dimensional MXenes as Emerging Materials: A Comprehensive Review

AbstractBecause of their peculiar two‐dimensional layered microstructure, the existence of numerous functionalities on the surface and excellent electrical, thermal and optical features, MXenes are regarded as promising candidates for solving energy and environment related problems. It is noted that energy conversion and storge capability of MXenes could be raised by altering their dimensions, structure, surface chemistry and chemical composition. Therefore, it is critical to recognize how one can boost the relationship between structure and property from applied viewpoint. In the present study, we reviewed the synthesis, properties and potential applications of MXenes. Furthermore, several properties of MXenes including structural, chemical, optical, mechanical and thermal have been explored. In addition, the potential applications of MXenes in various areas such as photocatalysis, gas sensing, supercapacitors, electrocatalysis and environmental remediation have also been discussed. Based on reported works, it can clearly be noticed that features and potential applications of MXenes may be further improved by applying many alteration and functionalization strategies. This study also focuses on the current progresses and future prospective relating to MXene based composites, which will surely assist the scientists who are doing work in areas of academia and material sciences.

Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications.

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade.

Maximization of WSN Based IoT Systems Lifetime by Minimized Intra-cluster Transmission Distance Clustering Protocol

Interney of Things (IoT) enabled by Wireless Sensor Network (WSN) is the principal idea behind target tracking, environment survelance, and patients monitoring systems in which human attentions are very crucial for round the clock. Since the sensor nodes that constitute the IoT is power constrained, it is suffering energy related problems which further badly affect the lifetime of the core sensor network. A well-knows topology management and routing scheme called Clustering is widely used for WSNs in maximizing the network lifetime due to its intrinsic characteristics. Clustering solves the energy constrained issues of WSN by providing a local infrastructure like arrangement to manage the network and resources in suitable manner. Various clustering approaches have been proposed so far by scientific community to address energy issues of WSN. But these existing approaches fail to provide required clustering output to improve lifetime by balancing the energy consumption in efficient manner. In this work, we propose a Minimized Intra-cluster Transmission Distance Clustering Protocol (MITDCP) to improve lifetime of WSN by innovatively clustering and intelligently placing the Base Station (BS). Innovative clustering involves a FCM (Fuzzy C Means) with Cluster Balancing algorithm to create balanced clusters. Then the proposed work makes use of back off timer weighted with residual energy to select and rotate Cluster Head (CH). Simulations show that our proposed work has achieved significant improvement in lifetime of WSN beneath the IoT systems when compared with Improved Energy Efficiency Clustering Protocol (IEECP).

Rating the Sustainable Energy Problems in Manufacturing Businesses and Selecting the Best Energy Security Policy: The Case of Samsun

Today, economic development and development are extremely important. With the increasing need for energy in both the world and Turkish economy, the importance of ensuring the sustainability of the energy resources used and the security of energy supply is also increasing. Implementation of a sustainable energy system; It includes important factors such as efficient energy use together with sufficient, economical, safe and environmentally compatible energy supply. Energy is one of the main factors to consider in discussions of sustainable development. The main dimensions of sustainability in energy production are environmental protection and the economically and socially sustainable supply of energy resources in a way that is reliable, sufficient and financially accessible in the long run. For this reason, it is important for Turkey to identify sustainable energy problems and determine the right energy policy. In this study, the determination of sustainable energy problems and the problems of determining the right energy security policy in manufacturing enterprises in Samsun province in Turkey were made with multi-criteria decision making methods. As a result of this research, the most ideal energy security policy was “Resource Sharing” in the selection created by using the degrees of sustainable energy related problems in manufacturing enterprises. On the other hand, the most ideal energy security alternatives were realized as Resource Sharing (A4)&gt;&gt; Source Distribution (A3)&gt;&gt; Resource Diversification (A1)&gt;&gt; Price Security (A2), respectively.

Low-Temperature Synthesis of Cu-Doped Anatase TiO2 Nanostructures via Liquid Phase Deposition Method for Enhanced Photocatalysis.

Titanium dioxide (TiO2) photocatalysis can harness the energy from sunlight, providing a solution to many green- and energy-related problems. In this study, we aimed to produce Cu doped TiO2 (Cu-TiO2) structures at a low temperature (~70 °C) under atmospheric pressure based on liquid phase deposition. The products prepared with Cu nitrate exhibited anatase-phase TiO2 with the presence of Cu, and the particles showed a waxberry-like structure. Changing the Cu nitrate concentration allowed control of the atomic concentration; we confirmed ~1.3 atm.% of Cu ions in the product when we applied 10 mM in the precursor solution. By doping Cu, the light absorption edge shifted to 440 nm (~2.9 eV), and we proved the photocatalytic reaction through action spectral measurement. We observed the decomposition of acetaldehyde into CO2 on Cu-TiO2 photocatalysts, which produced optimized improvements in photocatalytic activity at Cu dopant levels between 0.2 and 0.4 atm.%. This study demonstrates that the liquid phase deposition technique can be used for doping metallic ions into TiO2, which shows promise for preparing novel and unique nanomaterials as visible light photocatalysts.

A Transfer Learning Framework for Predictive Energy-Related Scenarios in Smart Buildings

Human activities and city routines follow patterns. Transfer learning can help achieve scalable solutions toward the realization of smart cities accounting for similarities between regions, domains, and activities. In this study, we propose a transfer learning-based framework for smart buildings to test this hypothesis in energy-related problems. Our framework has two major components: the network creation and the transferable predictive model. In order to create the network that groups buildings sharing characteristics, we evaluated two strategies: a novel clustering algorithm for mixed data, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -prod, and clustering the image-based representation of time series. Then, a combination of long short term memory and convolutional neural network was trained on the centroids of the clusters for energy consumption prediction. The coefficient of variation of the root mean squared error (CVRMSE) of the predictions in such clusters vary between 3.85% and 58.85%. The obtained parameters were transferred to the rest of the buildings for predictive purposes, finding accurate results in buildings with little data. Our framework deals with insufficient training data since parameters from scenarios with more sensors can be received. It also carries out state-of-the-art performance on three datasets from different sources having in total 533 rooms/buildings and two energy efficiency domains: consumption prediction reducing the CVRMSE in a 21.6%, and air conditioning usage prediction moving from a 4.18% to a 0.28% CVRMSE. Our framework extracts more knowledge from available IoT deployments, so that smartness could be spread between environments at a fewer cost given that less individual effort will be needed.

Big data and artificial intelligence application in energy field: a bibliometric analysis.

This paper uses bibliometrics to characterize the knowledge systems of big data, artificial intelligence (AI), and energy based on the Science Citation Index Extension (SCI-E) and Social Science Citation Index (SSCI) of the Web of Science from 2001 to 2020. Results show that China is the country with the highest number of publications (1115), accounting for 29% of the total; however, the most influential country in the field is the USA, with an h-index of 75. The Chinese Academy of Sciences publishes the largest number of papers (104) and plays a vital role in the collaboration network. The study also reveals that the IEEE Access is the most productive journal (195) in terms of the number of publications, and engineering is the most popular discipline (1526). The key theoretical foundation includes deep learning (293), big data (105), energy consumption (79), and reinforcement learning (40). The application of big data and AI in the field of energy focuses on smart grid, energy consumption, and renewable energy. Early research frontiers involve optimization and prediction of energy-related problems using the genetic algorithm and neural networks. Since 2013, energy big data have gained prominence. At present, machine learning, deep learning, and fog computing are frequently combined with energy saving. In the future, big data and AI will be utilized to promote the application of renewable energy and energy-saving renovation of buildings. These findings can help researchers understand the developmental trends and correctly grasp the research direction and method of the emerging interdisciplinary field.

Construction of core−shell MOF@hydroxide hybrids with strong hetero-interface interaction for photocatalytic hydrogen evolution

Metal−organic framework (MOF)-based photocatalysts enabling the separation of photogenerated electron−holes pairs for efficient photocatalytic hydrogen production have been the focus of considerable attention lately as a solution to reduce energy related problems. In this study, the Ni2+ etching of a typical Ti-based MOF(NH2-MIL-125) to synthesize a defective NH2-MIL-125@Ni (OH)2 photocatalyst is reported. Ni2+ was selected to hydrolyze and etch the NH2-MIL-125 to obtain defective NH2-MIL-125@Ni (OH)2 was a strong hetero-interface interaction heterojunction, while maintaining the NH2-MIL-125 structure. The construction of defects increased the number of active sites, and the Ni (OH)2 cocatalyst further enhanced the photogenerated electron-holes separation efficiency. Under visible light for a duration of 4 h, H2 production reached a rate of 502.32 μmol g−1.

Room Occupancy Prediction Using ML

Abstract: Now-a-days buildings are growing rapidly and uses large amount of energy. Most of the energy gets wasted on HVAC systems. Occupancy (presence and number of occupants) is one of the most important factors impacting energy efficiency of HVAC systems. Hence, knowing occupancy information is vital for demand driven HVAC controls, that directly impacts on energy-related building control systems. So, occupancy information without privacy issues (like using cameras) gained importance. Recent works are done on this problem. Existing solutions proposed are yes or no classification, estimating for low count etc using sensors data. Using yes or no classification, the energy consumption may not be saved to a great extent. In this work, we have trained a model which classifies the state of room based on number of occupants (empty, low, fair, high). For this we are using data collected by different sensors (co2, humidity, temperature). We are using classification algorithms to train the model. This information could be used to solve energy related problems in buildings

Designing Novel Strategy to Produce Active Nanohybrids in Sunlight for Purification of Water Based on Inorganic Nanolayers, Magnetic Nanocomposites and Organic Species.

Energy and water related problems have attracted strong attention from scientists across the world because of deficient energy and water pollution. Following this line, new strategy depended on preparing nanolayers of Al/Zn and magnetic nanoparticles of cobalt iron oxides nanocomposite in addition to long chains of hydrocarbons of stearic acid to be used as roofs, fillers and pillars; respectively, to design optical-active nanohybrids in sunlight for removing the colored pollutants from water in few minutes. By using long chains of hydrocarbons of stearic acid, X-ray diffraction (XRD) results and TEM images showed expansion of the interlayered spacing from 0.76 nm to 2.02 nm and insertion of magnetic nanoparticles among the nanolayers of Al/Zn. The optical properties and activities showed that the nanohybrid structure based on zinc oxide led to clear reduction of the band gap energy from 3.3 eV to 2.75 eV to be effective in sunlight. Photocatalytic degradation of the dye of acid green 1 confirmed the high activity of the prepared zinc oxide nanohybrids because of a complete removal of the dye after ten minutes in sunlight. Finally, this strategy was effective for producing photo-active nanohybrids for using renewable and non-polluting energy for purifying water.

Design of cultural emperor penguin optimizer for energy-efficient resource scheduling in green cloud computing environment.

In recent times, energy related issues have become challenging with the increasing size of data centers. Energy related issues problems are becoming more and more serious with the growing size of data centers. Green cloud computing (GCC) becomes a recent computing platform which aimed to handle energy utilization in cloud data centers. Load balancing is generally employed to optimize resource usage, throughput, and delay. Aiming at the reduction of energy utilization at the data centers of GCC, this paper designs an energy efficient resource scheduling using Cultural emperor penguin optimizer (CEPO) algorithm, called EERS-CEPO in GCC environment. The proposed model is aimed to distribute work load amongst several data centers or other resources and thereby avoiding overload of individual resources. The CEPO algorithm is designed based on the fusion of cultural algorithm (CA) and emperor penguin optimizer (EPO), which boosts the exploitation capabilities of EPO algorithm using the CA, shows the novelty of the work. The EERS-CEPO algorithm has derived a fitness function to optimally schedule the resources in data centers, minimize the operational and maintenance cost of the GCC, and thereby decrease the energy utilization and heat generation. To ensure the improvised performance of the EERS-CEPO algorithm, a wide range of experiments is performed and the experimental outcomes highlighted the better performance over the recent state of art techniques.