High Energy Cost Research Articles

Electrolyte optimization for sodium-sulfur batteries

Due to high theoretical capacity, low cost, and high energy density, sodium-sulfur (Na-S) batteries are attractive for next-generation grid-level storage systems. However, the polysulfide shuttle leads to a rapid capacity loss in sodium-sulfur batteries with elemental sulfur as the cathode material. Most previous studies have focused on nanoengineering methods for creating stable Na anodes and S cathodes. A proven strategy to mitigate the shuttle effect is to covalently bond elemental sulfur to a polymeric backbone and use it as the active ingredient instead of elemental sulfur. In this regard, we synthesized sulfurized polyacrylonitrile (SPAN) cathodes. In addition to the electrodes, electrolyte selection is crucial for sodium sulfur batteries with long cycle life, high energy densities, and rate capabilities. Thus, we explored various electrolyte compositions; specifically organic solvents such as propylene carbonate (PC), dioxolane (DOL), dimethoxyethane, and diglyme (DIG) were mixed in different proportions to create electrolyte solvents with both ethers and carbonates to promote the formation of bilateral solid electrolyte interphase (SEI). This bilateral SEI strategy has been employed to prevent polysulfide shuttle and dendrite growth in lithium-sulfur batteries. Sodium bis(trifluoromethanesulfonyl)imide (NaTFSI) was chosen as the electrolyte salt. The prepared coin cells were tested for rate capability and capacity retention, and the results have been analyzed. High initial discharge capacity of ∼740 mAh g−1 with ∼66% capacity retention over 100 cycles was observed for 0.8M NaTFSI in PC50DOL50 (v/v). The cell with 0.8M NaTFSI in PC50DIG50 has exhibited strong capacity retention of 74.60% with excellent Coulombic efficiency of 99%. Molecular dynamics (MD) simulations were carried out to further understand these results.

Numerical modeling and dynamic response analysis of an integrated semi-submersible floating wind and aquaculture system

Floating wind turbines (FWT) are facing the challenges of high cost of energy. Integrating floating wind with offshore aquaculture cages presents a mutually beneficial approach to address cost concerns and maximize overall benefits. This study introduces and numerically models an integrated system, encompassing a self-designed semi-submersible FWT coupled and an aquaculture cage (FWT + AC). Comprehensive, fully coupled aero-hydro-servo-elastic-mooring models for this integrated system are established. Subsequently, the numerical model of the FWT is validated against wave basin model tests. The research further discusses the coupled dynamic response characteristics of the integrated system for both the FWT with and without the aquaculture cage. The results highlight that integrating aquaculture cages leads to an increase in the mean surge response by up to 12.6%, while significantly reducing pitch motion by as much as 7.69% under combined wind, wave, and current conditions. Moreover, the mean mooring loads, particularly for line 4 which faces the direction of wind, wave, and current, do not exhibit an increase of more than 15%. This integration not only enhances the stability of wind power generation under different wind degree, but also minimally impacts the average power generation efficiency, showing a variation of only about 2% under rated wind speed. The findings provide beneficial theoretical support for the design of the integrated FWT + AC system, demonstrating the potential of such integrated systems in promoting offshore sustainable development and enhancing wind energy utilization efficiency.

Cost-based optimization, feasibility study, and sensitivity analysis of forward osmosis/crystallization/reverse osmosis with high-temperature operation for high-salinity seawater desalination

Seawater desalination plants using reverse osmosis (RO) in high temperature and high-salinity conditions, such as in the Middle East, face challenges such as limited achievable recoveries and high energy costs. This study evaluated the economic feasibility under seawater and high-salinity conditions by introducing a forward osmosis/crystallization/reverse osmosis (FO/Cry/RO) hybrid process. The optimization results were then used to compare the specific water cost (SWC), specific energy consumption (SEC), recovery, and permeate quality of the hybrid process with two different draw solutes and a conventional seawater RO process. In addition, a sensitivity analysis was conducted to determine how individual process parameters and operating conditions affect the economic feasibility of the proposed process under seawater and high-salinity conditions. The FO/Cry/RO hybrid process achieved a higher recovery with a lower SWC and SEC as the feed concentration increased compared to the conventional RO process. Utilizing waste heat further increased the economic advantages of the proposed process. In addition, much higher permeate quality can be obtained depending on the type of draw solute. The results of this study revealed that the proposed hybrid system outperformed the conventional RO process in terms of both energy efficiency and cost-effectiveness for seawater desalination under high-temperature and high-salinity conditions.

Fabrication of Living Entangled Network Composites Enabled by Mycelium.

Organic polymer-based composite materials with favorable mechanical performance and functionalities are keystones to various modern industries; however, the environmental pollution stemming from their processing poses a great challenge. In this study, by finding an autonomous phase separating ability of fungal mycelium, a new material fabrication approach is introduced that leverages such biological metabolism-driven, mycelial growth-induced phase separation to bypass high-energy cost and labor-intensive synthetic methods. The resulting self-regenerative composites, featuring an entangled network structure of mycelium and assembled organic polymers, exhibit remarkable self-healing properties, being capable of reversing complete separation and restoring ≈90% of the original strength. These composites further show exceptional mechanical strength, with a high specific strength of 8.15MPag.cm-3, and low water absorption properties (≈33% after 15 days of immersion). This approach spearheads the development of state-of-the-art living composites, which directly utilize bioactive materials to "self-grow" into materials endowed with exceptional mechanical and functional properties.

An Intelligent Optimized Control System for Induction Heating Application

In the modern era, induction heating (IH) technology is utilized for industrial functions like medical applications and domestic applications. Furthermore, efficient uses such as control topologies, magnetic component design, and power electronics have high demand as well as cost-effective with respect to advanced technology. However, IH technology directly targets the power converter and coil so it has wasted higher amount of heat. Also, high power dissipation in an IH system leads to high energy costs and reduced system performance. Hence, various approaches are developed to minimize power dissipation. However, they cannot reduce power dissipation in the induction coil. Thus, a novel Decision-based Squirrel Control System was developed in this article to decrease the power wastage in induction coil optimally. The developed model’s squirrel fitness function monitors power wastage and compensates them by tuning it. The developed model is executed in MATLAB/Simulink, and the results are determined. In terms of rising time, settling time, overshoot, and error rate. Here, the proposed work can achieve 0.075s resting time, 1.02s settling time, 0.05s overshoot, and 0.29% of error rate. The comparative assessment proves that the presented approach attained better results than others.

An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price

Home energy systems (HESs) face challenges, including high energy costs, peak load impact, and reliability issues associated with grid connections. To address these challenges, homeowners can implement solutions such as energy management, renewable resources, and energy storage technologies. Understanding consumption patterns and optimizing HES operations are crucial for effective energy management. As a primary step, addressing these concerns requires an efficient forecasting tool to predict home energy demand and electricity prices. Due to the complexity of big data, and uncertainties involved in forecasting, machine learning (ML) methods are necessary. In this study, we develop a hybrid machine learning approach, utilizing one year of data on home energy demand and prices to address the challenge of forecasting home energy consumption. A comprehensive comparison of different deep and non-deep ML models highlights the superiority of the proposed hybrid approach. The performance of these models, measured using metrics such as RMSE, MAE, R2, and RT (running time), are compared. Finally, an optimized hybrid XGBoost (XGB) ML model that combines price and energy demand forecasting is introduced. The proposed ML method’s parameters are optimally determined using Particle Swarm Optimization. The hybrid ML model’s performance is evaluated in predicting both energy demand and consumption prices using historical data from diverse households with various features and consumption patterns. The results indicate that the hybrid ML model achieves accurate predictions for energy consumption and prices, with improvements in RMSE (up to 36.6%), MAE (up to 36.8%), and R2 (up to 3.9), as compared to conventional ML methods. This research contributes to sustainable energy practices by providing an effective tool for forecasting energy consumption and associated costs in the dynamic landscape of home energy systems.

Wind-Powered Desalination on Islands: A Review of Energy–Water Pathways

Water scarcity is a global problem that particularly affects islands located in arid regions or regions with limited water resources. This issue has prompted the development of non-conventional water sources such as fossil fuel-powered desalination systems. Concern about the high energy and environmental costs associated with this type of facility has created the ideal framework for the proliferation of desalination projects powered by renewable energies, especially wind energy due to the multiple advantages it offers. This article provides a bibliometric analysis to identify the advances made in wind-powered desalination on islands. While many studies explore wind-powered desalination, none compile references specific to islands. This paper analyses islands’ desalination needs and showcases wind-powered systems, exploring their types and uses. Firstly, the most relevant international scientific journals are identified to allow the subsequent selection and quantitative and qualitative analysis of articles directly dealing with wind-powered desalination systems. A total of 2344 articles obtained from the Scopus database were analyzed, of which 144 including 181 case studies were selected. Among the results of this study, an increasing year-on-year trend is observed in the number of published studies tackling wind-powered desalination. Finally, this paper presents a series of maps showing the most relevant facilities, projects, and data in this field, and provides an overview of the lessons learned in the decarbonization of desalination.

Gait Factor on the Energy Harvesting for a Simple Biped Robot

To design walk-fast and energy-efficient robots, there has been lots of work in the last decade examining the locomotion dynamics of a passive biped. As the walking environment or system parameter changes, an energy use efficient robot may become inefficient. A possible approach to increase the energy efficiency is through the ability to harvest the energy used during the locomotion. The paper’s main goal is to investigate the relations between walking speed, the locomotion energy consumption of a passive biped, and the ability to retrieve the lost energy as locomotion energy efficiency varies. Piezoelectric bimorphs were attached to the feet of the biped to harvest energy via exploiting the acceleration excitations induced vibrations at the instant foot lift and heel strike. It is found that as a foot-to-hip mass ratio increases, the stable periodic-1 (P1) walking gait becomes slower and more energy costing. Also it means more available energy to harvest, although the retrieved energy is much smaller compared to the locomotive energy. Once the foot-to-hip mass ratio passes the periodic doubling (PD) point, P1 walking gaits will become limped P2 walking gaits, and the high energy cost situation alleviates, which also means less available energy to harvest. On the other hand, if the foot-to-hip mass ratio is fixed and the slope angle increases, the walking will experience sequences of PD bifurcations, and the walking gaits go through P1, P2, P4, P8, and chaotic walking. As the walking gaits change, the average walking efficiency, average locomotion energy consumption, and average harvested energy grow as the slope becomes deeper.

Impact of pulsed electric fields on membrane disintegration, drying, and osmotic dehydration of foods

AbstractDrying is one of the oldest and most conventional methods used to preserve foods. Unique traditional methods have been applied for the drying of plant‐based food products. However, the disadvantage of this approach is the low drying rate, high energy, and processing costs accompanied by the loss of nutritional quality in food products. As a substitute, pulsed electric field (PEF) is a modern and promising technique that has various advantages over thermal treatments. Nowadays, it has become an area of innovation to meet increasing industrial demands. Application of PEF during drying and osmotic dehydration has beneficial impacts on the extension of shelf‐life, increases in storage stability, and improvement of nutritional quality. Upscaling the use of PEF from laboratory to industrial scale while maintaining productivity is an area for further research and development. The aim of this commentary review is to give an overview of a newly proposed PEF process, which improves drying rate, mass‐energy transfer, retention of bioactive compounds, and osmotic dehydration in food. PEF processing led to achieving the innovative development of a wide range of foods and was extended to solve environmental issues. Drying food products is the most promising area in the food industry which needs improvement in terms of achieving energy efficiency and cost‐effectiveness to dry food products with the maintenance of nutritional properties to adequate levels. In this regard, PEF could be a suitable and novel drying method to overcome the limitations of conventional drying methods which might open new horizons for valuable research in manufacturing dried fruits and vegetables on an industrial scale.

Tween-20 induced self-healing deposition towards ultra-stable Zn electrode

Zn-ion battery has revealed potential application in grid energy storage due to the high energy density, cost effectiveness and intrinsic safety. However, its large-scale application is hindered by the unstable Zn electrode caused by the continuous zinc dendrites growth. Herein, Tween-20 is selected as a functional additive of electrolyte to enhance the stability of Zn electrode through inducing self-healing Zn deposition. The Tween-20 molecules with rich carbonyl and ether groups preferentially adsorb on the top surface of vertically grown Zn nanosheets formed by initial corrosion, guiding the Zn deposition between them, and finally constructing smooth surface, demonstrating a self-healing feature. Simultaneously, the absorbed Tween-20 can prevent water molecules and SO42− from directly contacting the zinc surface, reducing the zinc corrosion and thus enhance the stability of Zn electrode. As a result, the Zn||Zn symmetric battery using the modified electrolyte with Tween-20 displays super long-lifespan of more than 6500 h at a current density of 1 mA cm−2 with a capacity of 0.5 mAh cm−2. Even in the practical applications, the electrolyte containing Tween-20 can also enable the Zn||MnO2 battery to cycle more than 2500 times almost without decay at 1.8 A g−1.

Optimizing convolutional neural networks for IoT devices: performance and energy efficiency of quantization techniques

This document addresses some inherent problems in Machine Learning (ML), such as the high computational and energy costs associated with their implementation on IoT devices. It aims to study and analyze the performance and efficiency of quantization as an optimization method, as well as the possibility of training ML models directly on an IoT device. Quantization involves reducing the precision of model weights and activations while still maintaining acceptable levels of accuracy. Using representative networks for facial recognition developed with TensorFlow and TensorRT, Post-Training Quantization and Quantization-Aware Training are employed to reduce computational load and improve energy efficiency. The computational experience was conducted on a general-purpose computer featuring an Intel i7-1260P processor and an NVIDIA RTX 3080 graphics card used as an accelerator. Additionally, a NVIDIA Jetson AGX Orin was used as an example of an IoT device. We analyze the feasibility of training on an IoT device, the impact of quantization optimization on knowledge transfer-trained models and evaluate the differences between Post-Training Quantization and Quantization-Aware Training in such networks on different devices. Furthermore, the performance and efficiency of NVIDIA’s inference accelerator (Deep Learning Accelerator - DLA, in its 2.0 version) available at the Jetson Orin architecture are studied. We concluded that the Jetson device is capable of performing training on its own. The IoT device can achieve inference performance similar to that of the more powerful processor, thanks to the optimization process, with better energy efficiency. Post-Training Quantization has shown better performance, while Quantization-Aware Training has demonstrated higher energy efficiency. However, since the accelerator cannot execute certain layers of the models, the use of DLA worsens both the performance and efficiency results.

Efficient and economical removal and recovery of phosphorus via a novel dual-electrode electrochemically induced crystallization process: Performance and crystallization mechanisms

Electrochemically induced crystallization is a promising method for removing and recovering P from wastewater but may be hindered by intrinsic limitations, e.g., high energy consumption and cost. To overcome such limitations, a dual-electrode electrochemically induced crystallization process (DEIC) was proposed using an electrochemical reactor equipped with an iron anode and a stainless-steel cathode. Results showed that > 95% P removal was achieved in 20 hours at the experimental conditions of current density = 0.2 mA/cm2, pH = 6.5, Ca2+ = 5.4 mM, and P concentration = 6.5 mM. The specific energy consumption was calculated as 0.82 kWh/kg P, which is much lower than the reported results (7 – 65 kWh/kg P). According to the XRD and SEM-EDS analysis, P removal was achieved in the form of vivianite (Fe3(PO4)2.8 H2O) and CaP crystallization, induced by the releases of Fe2+ and OH– at the anode and cathode, respectively. Vivianite was found to be deposited at the anode with high purity and a regular plane structure. The DFT calculation confirmed that the control of vivianite growth in a plane shape is thermodynamically feasible. Lastly, the economic analysis showed that the operational cost of DEIC (1.07 USD/kg P) was lower than that of conventional chemical precipitation (1.3 – 1.8 USD/kg P), suggesting that the DEIC process could be a viable method to achieve P removal and recovery.

Effect of thermal gas quenching on NOx production by atmospheric pressure rotating arc Plasma: A pathway towards Eco-Friendly fertilizer

Plasma nitrogen fixation allows an alternative ammonia production path compared to the classical industrial Haber-Bosch process, due to the emergence of low-cost and environment friendly renewable energy. However, the high energy cost due to high-temperature reaction condition and reverse reactions in typical “warm” plasmas (gliding arc and rotating gliding arc) lies as a bottleneck of its industrial application. As a possible solution to enhance the efficiency of the process, the effect of thermal gas quenching on nitrogen fixation by rotating arc plasma are investigated. The NOx (i.e. NO, NO2) yield, energy cost, and NOx selectivity are obtained under variable operating conditions (i.e. oxygen fraction in the feed gas, arc length, specific energy input, varying quench gas and quenching ratio). With the help of quenching, the maximum achievable NOx concentration was enhanced from 5.16 to 5.8%. The enhancement is achieved by mitigating loss reactions through quenching and it is supported by the integration of experimental findings, plasma diagnostics using optical emission spectroscopy, and the application of a zero-dimensional chemical kinetic model.

Novel adaptive quantization methodology for 8-bit floating-point DNN training

There is a high energy cost associated with training Deep Neural Networks (DNNs). Off-chip memory access contributes a major portion to the overall energy consumption. Reduction in the number of off-chip memory transactions can be achieved by quantizing the data words to low data bit-width (E.g., 8-bit). However, low-bit-width data formats suffer from a limited dynamic range, resulting in reduced accuracy. In this paper, a novel 8-bit Floating Point (FP8) data format quantized DNN training methodology is presented, which adapts to the required dynamic range on-the-fly. Our methodology relies on varying the bias values of FP8 format to fit the dynamic range to the required range of DNN parameters and input feature maps. The range fitting during the training is adaptively performed by an online statistical analysis hardware unit without stalling the computation units or its data accesses. Our approach is compatible with any DNN compute cores without any major modifications to the architecture. We propose to integrate the new FP8 quantization unit in the memory controller. The FP32 data from the compute core are converted to FP8 in the memory controller before writing to the DRAM and converted back after reading the data from DRAM. Our results show that the DRAM access energy is reduced by 3.07×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} while using an 8-bit data format instead of using 32-bit. The accuracy loss of the proposed methodology with 8-bit quantized training is ≈1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 1\\%$$\\end{document} for various networks with image and natural language processing datasets.

A new cloud-based method for composition of healthcare services using deep reinforcement learning and Kalman filtering

Healthcare has significantly contributed to the well-being of individuals around the globe; nevertheless, further benefits could be derived from a more streamlined healthcare system without incurring additional costs. Recently, the main attributes of cloud computing, such as on-demand service, high scalability, and virtualization, have brought many benefits across many areas, especially in medical services. It is considered an important element in healthcare services, enhancing the performance and efficacy of the services. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. Developing new approaches for discovering and selecting healthcare services in the cloud has become more critical due to the rising popularity of these kinds of services. As a result of the diverse array of healthcare services, service composition enables the execution of intricate operations by integrating multiple services' functionalities into a single procedure. However, many methods in this field encounter several issues, such as high energy consumption, cost, and response time. This article introduces a novel layered method for selecting and evaluating healthcare services to find optimal service selection and composition solutions based on Deep Reinforcement Learning (Deep RL), Kalman filtering, and repeated training, addressing the aforementioned issues. The results revealed that the proposed method has achieved acceptable results in terms of availability, reliability, energy consumption, and response time when compared to other methods.

A review of electrochemical cells and liquid metal battery (LMB) parameter development

Liquid Metal Battery (LMB) technology is a new research area born from a different economic and political climate that has the ability to address the deficiencies of a society where electrical energy storage alternative are lacking. The United States government has begun to fund scholarly research work at its top industrial and national laboratories. This was to develop liquid metal battery cells for energy storage solutions. This research was encouraged during the Cold War battle for scientific superiority. Intensive research then drifted towards high energy rechargeable batteries, which work better for automobiles and other applications. Intensive research has been carried out on the development of electrochemical rechargeable all-liquid energy storage batteries. The recent request for green energy transfer and storage for various applications, ranging from small-scale to large-scale power storage, has increased energy storage advancements and explorations. The criteria of high energy density, low cost, and extensive energy storage provision have been met through lithium-ion batteries, sodium-ion batteries, and Liquid Metal Battery development. The objective of this research is to establish that liquid metal battery technology could provide research concepts that give projections of the probable electrode metals that could be harnessed for LMB development. Thus, at the end of this research, it was discovered that the parameter estimation of the Li//Cd-Sb combination is most viable for LMB production when compared with Li//Cd-Bi, Li-Bi, and Li-Cd constituents. This unique constituent of the LMB parameter estimation would yield a better outcome for LMB development.

The evaluation of renewable energy alternatives in Turkey using intuitionistic-fuzzy EDAS methodology

In recent years, high energy costs, increases in carbon emissions, and concerns about energy supply security have led countries to prioritize renewable energy sources in their sustainable energy policies. The selection and ranking of alternative renewable energy sources is a critical issue in establishing an effective energy policy and ensuring environmental improvement at the national and global levels. This study evaluates Turkey’s best renewable energy options using the institutional fuzzy assessment based on distance to mean solution (IF-EDAS) method and aims to find Turkey’s best renewable energy alternative. The decision model alternatively uses wind, solar, geothermal, biomass, wave, hydroelectric, and hydrogen energy options. According to the study’s empirical findings, while the best alternative renewable energy source for Turkey is solar energy, the best criterion in terms of criteria evaluation is “Technology Maturity”. The study also carried out sensitivity analysis, and the results were shared.

Critical Review of the Metallurgical Operation of the Greek Nickel Industry and Perspectives, based on the recent Industrial Experience on Smelting Reduction Process

The mining and industrial production of Greek ferronickel industry LARCO have ceased since 30.07.2022. The last LME nickel price crisis, exclusion from bank loaning, extremely high cost of electrical energy, management issues, resulted inter alia, in a prohibitive operational cost. Nevertheless, LARCO faces the challenge of restructuring and reoperation in the future, given that privatization is in progress, including the acquisition of the company’s assets to a joint venture enterprise.
 Within this framework, the current paper aims to contribute to the discussion for developing a new management strategy for the optimization of pyrometallurgical process, focusing on the critical step of smelting reduction. Based on industrial experience concerning open bath submerged arc electric furnaces, factors which critically affect the safety and cost-effectiveness of smelting reduction are detected and presented by means of case studies, being mainly classified as following: i) Optimal raw materials’ feed management, including laterite ores (domestic or not), solid fuels and electrode paste. Moreover, taking for granted the need for Ni-rich ‘foreign’ ores to increase the recovery and reduce the cost of the process, the extent of dependence on Fe-rich domestic ores is investigated, in order a safe and stable operation to be ensured. ii) Focus on preventive maintenance management. Substantial increase of the facilities’ operational index and cost saving is obtained, as proved by operational case studies. iii) Modern human resources management strategy. Their economical footprint is further discussed.

Azolla Plant Production and Their Potential Applications

Globally, the human population is growing at an alarming rate, reducing land coverage over time. In the modern world, lifestyle changes, the nature of work, and food habits increase the incidence of serious diseases in animals, and human activity influences the environment, such as the use of chemical fertilizers for agriculture, large terrestrial ecosystems all over the biosphere, such as deforestation of plants, which could not sink from atmospheric CO2, and the production of fuels for energy, which would increase the need for fossil fuels but would also deliver low energy fuels at a high energy cost. To overcome the above problems, Azolla plants perform well since they can be grown at low labor costs, on small plots of land, and for a variety of purposes including animal feed/livestock, poultry and fish production, environmental remediation, biofertilizer and biocontrol for mosquito repellents, carbon sequestration of CO2, and bioenergy effectiveness all year. As a result of their low impact on the environment and human health, Azolla plants are becoming increasingly important. The purpose of this review is to provide evidence of Azolla plant production and its potential role in various applications for a greener, more sustainable approach. This review was progressive in that it assessed and produced peer-reviewed papers related to Azolla plant production and its potential role in different applications for a sustainable greener approach. Based on the findings of reputable educational journals, articles were divided into three categories: methods used to produce the nutritional composition of Azolla, environmental factors that affect the efficiency of Azolla plants, and strain improvement of Azolla for enhanced multipurposes and techniques that are currently being used to meet Azolla plants production and its prospective applications for different sustainable greener approaches. The present findings indicate that Azolla is a rich source of protein, which has a clear benefit in offsetting a portion of the nutritional needs of animal/livestock, poultry, and fish production with what is effectively a low-cost dietary supplement, biocontrol of mosquito repellent, environmental bioremediation, biofertilizer, carbon sequester of CO2, and bioenergy for the potential need of Azolla plant applications. Azolla plants have long been recognized for their benefits in greener and more sustainable lifestyles, as well as quality enhancement and bio-based economy over traditional approaches. Because it relied on natural resources and utility green production, this review’s recovery was chosen as an appropriate and environmentally sound solution for a long and healthy lifestyle.

Global omics study of Tetraselmis chuii reveals time-related metabolic adaptations upon oxidative stress

Microalgae species encounter oxidative stress in their natural environments, prompting the development of species-specific adaptation mechanisms. Understanding these mechanisms can offer valuable insights for biotechnological applications in microalgal metabolic manipulation. In this study, we investigated the response of Tetraselmis chuii, an industrially important microalga, to H2O2-induced oxidative stress. Exposure to 0.5-mM H2O2 resulted in reduced cell viability, and higher concentrations led to a drastic decline. After 1 h of exposure to H2O2, photosynthetic capacity (Qy) was negatively impacted, and this reduction intensified after 6 h of continuous stress. Global multi-omics analysis revealed that T. chuii rapidly responded to H2O2-induced oxidative stress within the first hour, causing significant changes in both transcriptomic and metabolomic profiles. Among the cellular functions negatively affected were carbon and energy flow, with photosynthesis-related PSBQ having a 2.4-fold downregulation, pyruvate kinase decreased by 1.5-fold, and urea content reduced by threefold. Prolonged exposure to H2O2 incurred a high energy cost, leading to unsuccessful attempts to enhance carbon metabolism, as depicted, for example, by the upregulation of photosystems-related PETC and PETJ by more than twofold. These findings indicate that T. chuii quickly responds to oxidative stress, but extended exposure can have detrimental effects on its cellular functions.Key points• 0.5-mM H2O2–induced oxidative stress strongly affects T. chuii• Distinct short- and long-term adaptation mechanisms are induced• Major metabolic adaptations occur within the first hour of exposure