Increasing Energy Demand Research Articles

Metabolomic Exploration of Colorectal Cancer Through Amino Acids and Acylcarnitines Profiling of Serum Samples

Background/Objectives: Colorectal cancer (CRC) represents one of the most prevalent forms of cancer, with high mortality rates. The aim of this study was to observe and understand the metabolic changes in CRC through targeted metabolomics. Methods: Samples collected from 58 CRC patients and 35 healthy individuals have been analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), targeting two classes of metabolites: amino acids and acylcarnitines. Results: Statistical analysis revealed 26 significantly modified (p-value < 0.01; |FC| > 1.2) metabolites in CRC patients compared to the control group and 22 between colon cancer and control, whereas 8 metabolites differed only significantly between rectal cancer and healthy patients. Some of these significantly modified metabolites characterize cancer-specific adaptations, such as increased energy demand, increased tumor invasiveness, capabilities to promote amino acid synthesis, and tumor resistance against acute immune response. Moreover, receiver operator characteristic (ROC) analysis revealed that a set of two acylcarnitines (C6DC and C4-OH) can differentiate between CRC patients and healthy individuals with a high degree of confidence (AUC 0.837). Conclusions: By implementing a metabolomics approach targeting amino acids and acylcarnitines, several metabolic alterations induced by CRC have been highlighted. Even though these modifications are not specific enough to act as disease markers, they might prove useful for evaluating patient status.

Development of Maintenance Management System for Real-Time Monitoring of Power Transformer to Improve Availability Performance: The Case of Tagamenda TANESCO Grid Substation

Power transformers are critical assets in TANESCO's grid substations, playing a vital role in ensuring a reliable and uninterrupted power supply. However, the growing challenges of ageing infrastructure, increasing energy demand, and reactive maintenance practices often lead to unplanned outages, higher operational costs, and reduced transformer availability. This dissertation focuses on the Development of a Maintenance Management System for Real-Time Monitoring of Power Transformers at TANESCO grid substations, with the goal of improving availability performance. The study explores the design and implementation of a Real-Time Monitoring Management System (RTMMS), leveraging advanced sensors, data analytics, and predictive maintenance techniques. The RTMMS continuously monitors critical parameters such as temperature, oil levels, dissolved gases, and load conditions, providing real-time insights into transformer health. By integrating predictive analytics, the system identifies potential faults early, enabling timely interventions and reducing downtime. Additionally, it supports data-driven maintenance planning, enhances operational reliability, and extends transformer lifespan. The proposed system addresses challenges such as high initial costs, data management complexity, and resistance to technological change through phased deployment, secure cloud solutions, and comprehensive training programs. The expected benefits include improved transformer availability, cost efficiency, enhanced grid sustainability, and a shift from reactive to proactive maintenance practices. This research underscores the transformative potential of real-time monitoring systems in modernizing maintenance management and aligns TANESCO with global best practices in energy sector innovation and operational excellence.

Metabolomic and proteomic changes in leaves of rubber seedlings infected by Phytophthora palmivora.

Phytophthora palmivora, an oomycete pathogen, induces leaf fall disease in rubber trees (Hevea brasiliensis), causing significant economic losses. Effective disease management requires an understanding metabolic dynamics during infection. This study employed untargeted metabolomic and proteomic analyses to investigate the response of rubber seedling leaves to P. palmivora infection. Metabolomic profiling revealed 1702 and 979 metabolite peaks in positive and negative ionization modes, respectively, with 212 metabolites identified after duplicate removal. Principal component analysis (PCA) demonstrated distinct metabolic profiles between infected and non-infected leaves. Volcano plots indicated significant changes in 90 metabolites (P<0.05, fold-change ≥2), with 20 showing increased levels and 70 showing decreased levels in infected leaves. Pathway analysis highlighted nine metabolic pathways, with alanine, aspartate, and glutamate metabolism being the most impacted. Proteomic analysis identified 391 proteins, with 283 in infected leaves and 253 in control leaves. Among these, 145 were common to both conditions, suggesting their roles in maintaining homeostasis and responding to stress. Unique proteins in infected leaves were linked to oxidative phosphorylation, ATP synthesis, and metabolic adjustments, reflecting the increased energy demands. Control samples showed proteins related to growth and photosynthesis. Integrating metabolomic and proteomic data revealed significant alterations in energy metabolism pathways in response to infection. These findings enhance our understanding of rubber seedlings' defense strategies against P. palmivora, with implications for improving plant resistance and disease management strategies.

Renewable Energy from Solid Waste: A Spherical Fuzzy Multi-Criteria Decision-Making Model Addressing Solid Waste and Energy Challenges

With rapid urbanization and industrialization, Vietnam is facing many challenges in solid waste management and increasing energy demand. In this context, the development of renewable energy from solid waste not only solves the problem of environmental pollution but also makes an important contribution to energy security and sustainable economic development. Solid waste to energy is a system of solid waste reatment by thermal methods, in which the heat generated from this treatment process is recovered and utilized to produce energy. Site selection is one of the biggest challenges for renewable energy projects. In addition to technical factors, this decision must also consider environmental impacts, including protecting ecosystems, minimizing noise, and limiting impacts on public health. To solve this problem, multi-criteria decision making (MCDM) methods combined with fuzzy numbers are often used. These methods allow planners to evaluate and balance competing factors, thereby determining the most optimal location for the project. In this study, the authors proposed a Spherical Fuzzy Multi-Criteria Decision-making Model (SFMCDM) for site selection in solid waste-to-energy projects. In the first stage, all criteria affecting the decision-making process are defined based on literature review, experts and triple bottom line model (social, environmental, and economic), and analytic hierarchy process (AHP), and fuzzy theory is applied for calculating the weights in the second stage. The weighted aggregated sum product assessment (WASPAS) method is utilized for ranking four potential locations in the final stage. The contribution of the proposed process is its structured, systematic, and innovative approach to solving the location selection problem for renewable energy projects. Choosing the right location not only ensures the success of the project but also contributes to the sustainable development of renewable energy.

Boston Consulting Group Matrix-Based Equilibrium Optimizer for Numerical Optimization and Dynamic Economic Dispatch

Numerous optimization problems exist in the design and operation of power systems, critical for efficient energy use, cost minimization, and system stability. With increasing energy demand and diversifying energy structures, these problems grow increasingly complex. Metaheuristic algorithms have been highlighted for their flexibility and effectiveness in addressing such complex problems. To further explore the theoretical support of metaheuristic algorithms for optimization problems in power systems, this paper proposes a novel algorithm, the Boston Consulting Group Matrix-based Equilibrium Optimizer (BCGEO), which integrates the Equilibrium Optimizer (EO) with the classic economic decision-making model, the Boston Consulting Group Matrix. This matrix is utilized to construct a model for evaluating the potential of individuals, aiding in the rational allocation of computational resources, thereby achieving a better balance between exploration and exploitation. In comparative experiments across various dimensions on CEC2017, the BCGEO demonstrated superior search performance over its peers. Furthermore, in dynamic economic dispatch, the BCGEO has shown strong optimization capabilities and potential in power system optimization problems. Additionally, the experimental results in the spacecraft trajectory optimization problem suggest its potential for broader application across various fields.

Bias-free solar-driven ammonia coupled to C3-dihydroxyacetone production through photoelectrochemistry.

Conversion of solar energy into value-added chemicals through photoelectrochemistry (PEC) holds great potential for advancing sustainable development but limits by high onset potential which affects energy conversion efficiencies. Herein, we utilized a CuPd cocatalyst-modified Sb2(S,Se)3 photocathode (CuPd/TSSS) to achieve an ultra-low onset potential of 0.83 VRHE for photoelectrochemical ammonia synthesis. Meanwhile, we achieved unbiased NH3 production by synthesizing major value-added C3-dihydroxyacetone (DHA) through glycerol oxidation on the BiVO4 photoanode with the loading Pd cocatalyst, instead of a traditional solar water oxidation reaction. The PEC integrated system stably produced 11.98 µmol cm-2 of NH3 and 201.9 mmol m-2 of DHA over 5 h with ~80% faradaic efficiency without applying additional bias. In situ analysis and theoretical calculations confirmed high catalytic activity for ammonia synthesis at the CuPd/TSSS photocathode and enhanced selectivity for DHA at the Pd/BiVO4 photoanode. This design represents a breakthrough in directly utilizing solar energy, nitrate-containing wastewater, and biomass waste for ammonia and highly value-added C3 production, which addresses increasing energy demands while decreasing environmental impact.

Nano- and Micro-SiO2 With Integrated Green Chemistry-Based Superhydrophobic Coating for Robust Antifouling and Anticorrosion Properties.

With increasing energy demands, the need for coating materials with exceptional superhydrophobic properties has grown substantially. However, the widespread use of fluorinated compounds, solvents, and polymer-based synthetic materials has led to heightened levels of microplastics and pollutants. Here, we used a self-curing, solvent-free, and recyclable polyester polyol polymer material combined with (5 and 6.5 μm) micro- and nanosized SiO2 (μ-SiO2 and n-SiO2) particles to create superhydrophobic coatings with contact angles above 170° and low roll-off angle. They were applied for self-cleaning, antifouling, and anticorrosion purposes and tested for stability in hot water, steam, and ultrasound. Both μ-SiO2 particles mixed with n-SiO2 exhibited excellent improvement in antifouling properties. Furthermore, 5 μm SiO2 incorporated with n-SiO2 demonstrated significantly higher resistance in a 62-cycle sandpaper abrasion test and maintained a contact angle above 150°, whereas this angle was lower for the 6.5 μm SiO2 coating after 30 cycles. These results suggest that 6.5 μm SiO2 offers less resistance to applied force due to its irregular roughness. However, in scenarios with lower forces, such as water drop tests, both coatings easily withstand a drop count of 3000. Additionally, electrochemical polarization curve analysis, AC impedance analysis, and seawater immersion tests confirmed the robust corrosion resistance of the superhydrophobic material.

Combined Thermal Index Development for Urban Heat Island Detection in Area of Split, Croatia

Urban heat islands (UHIs) are a phenomenon of temperature rising within urban areas relative to their rural counterparts. UHIs are becoming an increasingly common problem in large cities, which appear due to excessive urbanization and reductions in natural cover and vegetation. This phenomenon negatively affects the quality of life of the residents of the affected city, causing discomfort, reducing air quality, and increasing energy demand. In this study, UHIs were detected and analyzed in the city of Split, Croatia, using data from the Landsat 8 and 9 satellite missions and ground-based measurements of air and land surface temperatures, which were conducted in July, August, and September 2024. This research compares ground-based and sensor-based temperatures, and their analysis results in the proposal of a new index: the Combined Thermal Index. The main feature of this new index, which combines measured and perceived temperature, is to improve the understanding of the impact of UHIs in the city (Split), compared to existing indices. So far, LST and air temperature have not been combined, nor have they been combined with human perception of temperature, which is important in the case of UHIs because it is these people who will ultimately feel a rise in temperature.

Enhancing Ammonia Synthesis and CO Hydrogenation to Methanol via Nonthermal Plasma Catalysis: A Focus on Catalyst Pore Structure and Active Site Design

The increasing energy demand, climate change and sustainable development necessitate the transition from fossil fuels to renewable sources (such feedstock and energy). Green hydrogen is one of the key components in sustainable energies/fuels being crucial for reducing carbon emissions and achieving carbon neutrality. However, hydrogen storage and transportation are challenging, and chemical hydrogen storage reactions such as ammonia synthesis and CO2 hydrogenation (to methanol) are considered promising solutions. Nonthermal plasmas (NTPs) are partially ionized gases with various energetic species, which are produced under ambient conditions being able to facilitate efficient chemical reactions under mild conditions. Importantly, NTP technologies can theoretically utilize green electricity produced by renewable energies (such as solar and wind) showing significant low-carbon potential. Previous studies have shown the catalysts design has an important impact on the performance of catalytic reactions under NTP conditions, and hence this aspect deserves attention. Here this mini review comments on the application of NTP catalytic technologies in ammonia synthesis and CO2 hydrogenation to methanol, with a special focus on catalyst pore structure and active site design. The critical summary and perspective can serve as the most current snapshot of the relevant research fields in NTP technologies, helping their further development.

Effect of Ni Incorporation in KCoPO4 on the Charge Storage Capacity of KCo1 − xNixPO4 (0 ≤ x ≤ 0.5) Electrodes for the Fabrication of High‐Performing Hybrid Supercapacitors

ABSTRACTFulfilling the increasing energy demands of the world through renewable energy sources requires the utilization of a highly efficient large‐scale electrochemical energy storage device. A hybrid supercapacitor (HSC) that consists of a battery‐type electrode coupled with a counter‐capacitive electrode, while in principle offering supercapacitor‐like power, cyclability, and higher energy density, can be a potential device for large‐scale energy storage to cater to the energy needs through renewable energy sources. The KCo0.5Ni0.5PO4 electrode demonstrated notably enhanced electrochemical performance, attributed to the synergistic interaction of Co2+ and Ni2+ ions in a phosphate framework. The incorporation of redox‐mediated diffusive charge storage through the incorporation of Ni2+ on the Co2+ site resulted in a large‐scale charge storage capacity, coupled with capacitive‐type surface charge storage on the KCo1−xNixPO4 electrodes. The KCo0.5Ni0.5PO4 delivers 173 mAh/g (capacitance: 1038 F/g) at a current density of 0.5 A/g in an aqueous 2 M KOH electrolyte, accompanied by cyclic stability up to 5000 cycles. HSC mode consists of activated carbon as the negative electrode along with KNi0.5Co0.5PO4 as the positive electrode, displaying high energy density and power density of 183.7 Wh/kg and 7952 W/kg, respectively, in 2 M aqueous KOH electrolyte. The superior performance in HSC mode makes KCo0.5Ni0.5PO4 a potential positive electrode for the development of high‐performing HSCs.

Gga-miR-34b-3p targets calbindin 1 to regulate cellular calcium ion homeostasis during eggshell calcification in chicken uterus.

Improving eggshell quality in poultry is a key breeding goal, and identifying genetic markers that regulate eggshell calcification is essential for accelerating genetic advancements. This study focused on identifying the keys genes and molecular mechanisms that regulate eggshell calcification in the chicken uterus. The results showed that rapid eggshell mineralization began approximately 4 h after the egg enters the uterus, corresponding with observed morphological and histological changes in the uterine tissue. This is associated with increased energy demands and the production of ion transport proteins. Transcriptome analysis identified calbindin-1 (CALB1), ATPase plasma membrane Ca2+ transporting 2 (ATP2B2), and gga-miR-34b-3p as differentially expressed during eggshell formation. CALB1 and ATP2B2 were predicted targets of gga-miR-34b-3p, with roles in maintaining cellular calcium ion balance. A dual-luciferase reporter assay confirmed that gga-miR-34b-3p directly targeted inhibited CALB1 expression, although no significant changes in the luciferase activity were observed with the co-transfection of ATP2B2 wild-type and gga-miR-34b-3p mimic. Validation experiments showed significant increases in CALB1 and ATP2B2 mRNA and protein levels of CALB1 and ATP2B2 in the chicken uterus during eggshell calcification, with CALB1 predominantly expressed in the cytoplasm of uterine tubular gland cells. Furthermore, primary uterine tubular gland cells, identified using immunofluorescence for Cytokertin 18, demonstrated that silencing CALB1 and ATP2B2 increased intracellular Ca2+ concentration in these cells. Taken together, these findings suggest that the gga-miR-34b-3p/CALB1 regulatory axis maintains calcium ion homeostasis in the uterine tubular gland cells, to facilitate continuous and efficient eggshell calcification and thereby enhancing eggshell quality in chickens.

Serum NMR metabolomics in distinct subtypes of hematologic malignancies.

Hematologic malignancies encompass a diverse array of subtypes, contributing to substantial heterogeneity that poses challenges in predicting clinical outcomes. Leveraging the capabilities of nuclear magnetic resonance holds substantial promise in the detection of serum biomarkers and individual metabolic alterations in patients. This study involved the analysis of the sera from patients with acute myeloid leukemia, chronic lymphocytic leukemia, and non-Hodgkin lymphoma to investigate the affected metabolites and their associated pathways. The quantitative one-dimensional (1D) 1H nuclear magnetic resonance method was employed to identify alterations. Metabolite annotations were validated using two-dimensional (2D) analyses. Discriminating chemometric models and receiver operating characteristic curves were created using the MetaboAnalyst platform. The findings revealed significant alterations in the serum levels of amino acid catabolism products, citrate cycle intermediates, and phospholipids. The acute myeloid leukemia group showed differences in glucogenic amino acids related to the glycolysis pathway, whereas the chronic lymphocytic leukemia and non-Hodgkin lymphoma groups displayed variances in fumarate and acetate levels linked to the citrate cycle pathway. In the leukemia groups, higher levels of products from the protein degradation pathway were observed. The biomarker panels for each malignancy group exhibited outstanding discrimination from controls. Healthy individuals differed distinctly from patients, indicating commonly observed metabolic adaptation patterns among frequent hematologic malignancies. The small cohort study using nuclear magnetic resonance metabolomics in various hematologic malignancy subtypes revealed significant changes in serum amino acid and protein degradation end-product levels, suggesting prolonged leukocyte lifespan and increased energy demand.

Understanding muscle energy expenditure variations following selective dorsal rhizotomy while maintaining consistent energy consumption.

Increased energy demands during walking is a recurrent issue for children with cerebral palsy (CP). Given the high incidence of spasticity in these children, several authors have analyzed the impact of selective dorsal rhizotomy (SDR) on energy consumption during walking, typically showing minimal changes post-SDR. To further investigate muscle behavior after SDR, our recent study identified alterations in individual muscle force production without changes in muscle activation during walking. This suggests that children with CP may experience a more favorable dynamic scenario for developing sub-maximal muscle forces after SDR, due to reduced spasticity unlocking joint movement. Thus, this raises questions about whether these changes in muscle force production could lead to increased muscle energy expenditure, which may not be fully reflected in overall energy consumption. The aim of this study was to build upon our previous research on muscle behavior after SDR by evaluating the surgery's impact on individual muscle energy expenditure during walking, using neuro-musculoskeletal simulations. Our research compared two matched groups comprising 81 children with CP: those who underwent SDR and those who did not. Our results showed no significant changes in overall energy consumption or total muscle energy expenditure in either group post-surgery. However, we observed alterations in individual muscle energy expenditure during walking in the SDR group compared to children with CP who received other treatments. Compared to the findings from our first study, we observed a significant decrease in spasticity of the plantarflexor muscles, an improvement in ankle joint angle, an increase in individual muscle force during walking, and no statistically significant changes in energy expenditure of the gastrocnemius and soleus muscles post-SDR. These findings, along with the absence of changes in muscle activity post-SDR, support the hypothesis that muscle tissue alterations contribute to energy deficits observed in children with CP during walking.

Towards machine learning applications for structural load and power assessment of wind turbine: An engineering perspective

Over the past decades, the increasing energy demand has accelerated the construction of wind farms, raising higher expectations for precise load and power assessments in wind turbine performance. Traditional methods, which rely on analytical wake models and performance curves, often fail to adapt to complex inflow scenarios, leading to significant inaccuracies in predicting turbine loads and power output. This research addresses these challenges by introducing a novel two-phase framework for various phases of wind farm planning and development, using the NREL 5MW baseline wind turbine as a case study. The first part involves deriving recommended values for simplified thrust modulation factors at the preliminary design phase, enabling swift evaluation of maximum and fatigue thrust loads crucial for wind farm optimization. The second part focuses on designing and training a machine learning model at the detailed design phase. A gradient-boosting-based framework based on LightGBM provides comprehensive methods for assessing wind turbine load and power, enhancing the precision and efficiency of these assessments. The proposed model achieves significant improvements in predictive accuracy, achieving mean R-Squared of 0.995, 0.988, and 0.995 for power, peak load, and damage equivalent load evaluation, respectively. The framework streamlines the assessment process, enhancing both the accuracy and speed of power and load evaluations for wind farm design. This is expected to reduce computational costs and improve the effectiveness of downstream tasks, such as layout optimization and wake steering.

Remarkable results of energy consumption and CO2 emissions for gasoline and electric powered vehicle.

Increasing concerns about climate change and efforts on reducing reliance on fossil fuels have led to research on electric vehicles for sustainable solutions to increasing energy demands. This study comprehensively analyzes the impact of power plant emissions on the adoption of electric vehicles in relation to air pollution. The main pollutants emitted by power plants and the potential change in emissions with the deployment of electric vehicles are assessed. Energy consumption of the vehicles was calculated. A gasoline-powered and an electric vehicle are modelled in MATLAB Simulink software. The theoretical model results of the main pollutants are compared with the power plant emissions to analyze the effect on major pollutants. This investigation aims to identify the potential CO2 emission and power requirement by transitioning to electric vehicles. Results show that energy consumption and CO2 emissions can be 111% and 82% higher than GPVs depending on the electricity generation technologies.

Research Progress on Oxygen Reduction Catalyst Materials for Proton Exchange Membrane Fuel Cells

The sustainable development of society gives rise to the increasing energy demand, making environmental pollution a problem that cannot be ignored. Among them, fuel cells are attracting attention as efficient and environmentally friendly. Proton exchange membrane fuel cells (PEMFCs) represent an advanced and eco-friendly energy conversion technology with substantial potential in transportation, distributed power generation, and other applications, while their performance can be remarkably impeded by the cathodic oxygen reduction reaction (ORR) which exhibits slow kinetics. Pt-based catalysts are the preferred materials for catalysis but are detrimental to the practical application of fuel cells due to their high cost and instability. Therefore, it is essential to develop stable and low-cost cathode catalysts. This paper reviews the advancements in cathode catalysts for PEMFCs, encompassing traditional platinum-based catalysts and their enhancement strategies, as well as the current status of emerging non-platinum-based catalysts. The paper also outlines future research directions for these cathode catalysts.

Comparison of behavioral responses, respiratory metabolism-related enzyme activities, and metabolomics of the juvenile Chinese mitten crab Eriocheir sinensis with different tolerance to air exposure

Air exposure is a common stressor for Chinese mitten crab (Eriocheir sinensis) during rearing and transport, and air exposure tolerance can serve as a crucial indicator for assessing the quality of juvenile E. sinensis. In this study, juvenile E. sinensis were divided into two groups based on their behavioral responses: Group S, which exhibited strong tolerance to air exposure, and Group W, which exhibited weak tolerance. Immersed crabs, not exposed to air, served as a control group (Group C). Whole body morphological characteristics and enzyme activities related to respiratory metabolism in the hemolymph and anterior gills were compared among the three groups. Non-targeted LC-MS metabolomic analysis was conducted on anterior gills. The results showed that, independent of developmental stage, crabs that were larger and had higher condition factor were more tolerant to air exposure. Additionally, compared to Group W, air exposure had a relatively small effect on glycolysis and anaerobic respiratory metabolic processes in the hemolymph and anterior gills of Group S. In response to air exposure, E. sinensis experienced increased energy demand, and switched from aerobic to anaerobic respiration to increase energy supply. Simultaneously, air exposure induced oxidative stress in the hemolymph and anterior gills. This study enhances our understanding of the response mechanism of E. sinensis to air exposure and provides a theoretical reference for the identification of high-quality juvenile E. sinensis.

Ranking the determinants of financial performance using machine learning methods: an application to BIST energy companies

Energy has been a key driver of change globally. As a developing country, Türkiye's increasing energy demand and consumption highlight the growing importance of efficient and sustainable energy management for its future. This study aims to determine the variables of the financial performance of 12 energy companies. Three different models are created with the return on assets, return on equity, and net profit margin as financial performance indicators of 12 firms. 12 financial ratios are used as input variables as determinants of financial performance. In the analysis, 37 quarterly data between 2014Q4-2023Q4 are used as the sample period. In machine learning, 17 different algorithms are considered in the selection of the appropriate model. The findings indicate that the Bagged Tree algorithm achieved successful outcomes for the ROA target variable, the Boosted Tree model demonstrated effective performance for the ROE model, and the Linear SVM algorithm yielded favorable results for the NPM model. According to the result obtained by the LIME method, Liquidity Ratio and Cash Ratio affect the ROA, ROE, and NPM models positively, while inventory turnover affects the models negatively.

Impact of sub-lethal Aclonifen intoxication on biochemical and stress markers on Oncorhynchus mykiss: an integrative assessment of multi-biomarker responses.

Aclonifen is a diphenyl ether herbicide being included in the list of priority substances. Nevertheless, the data related to its sublethal effects on fish are limited. Therefore, the present study has been carried out to investigate the toxic effects of aclonifen in juvenile Oncorhynchus mykiss following 24, 48, 72 and 96hours of application to sublethal concentrations of 12.7, 63.5 and 127μg/L. The application resulted in altered blood biochemistry appearing as hyperglycemia, decreased cholesterol and induced activities of transaminases of ALT and AST. The inhibition of AChE in brain, gill and liver was unimportant revealing its weak potential as anticholinesterase. The induction recorded for SOD, CAT, GPx and GST activities was accompanied with sustained elevation in TBARS and PC levels. It demonstrates both the pro-oxidant potential of aclonifen and oxidation of lipid and proteins resulting in the loss of membrane integrity and protein function. Hyperglycemic condition and decreased protein levels in gill and liver might be proposed as general adaptive responses to compensate increased energy demand. The integrative assessment of multi-biomarker responses shows concentration and duration related rise in calculated indexes. CAT, PC and SOD achieved the maximum scores for brain, gill and liver, respectively. Considering the results, oxidative stress inducing potential and weak anticholinesterase activity along with its disturbing impact on blood biochemistry were evidenced. Moreover, adverse affects observed after short term application on O. mykiss, present the potential risk aclonifen may cause at population level in aquatic ecosystems emphasizing the importance of pesticide regulations to avoid adverse impacts on non-target species.

Hybrid Huff-n-Puff Process for Enhanced Oil Recovery: Integration of Surfactant Flooding with CO2 Oil Swelling

With increasing energy demands and depleting oil accessibility in reservoirs, the investigation of more effective enhanced oil recovery (EOR) methods for deep and tight reservoirs is imminent. This study investigates a novel hybrid EOR method, a synergistic approach of nonionic surfactant flooding with intermediate CO2-based oil swelling. This study is focused on the efficiency of surfactant flooding and low-pressure oil swelling in oil recovery. We conducted a fluorescence-based microscopic analysis in a microchannel to explore the effect of sodium dodecyl sulfate (SDS) surfactant on CO2 diffusion in Texas crude oil. Based on the change in emission intensity of oil, the results revealed that SDS enhanced CO2 diffusion at low pressure in oil, primarily due to SDS aggregation and reduced interfacial tension at the CO2 gas–oil interface. To validate the feasibility of our proposed EOR method, we adopted a ‘reservoir-on-a-chip’ approach, incorporating flooding tests in a polymethylmethacrylate (PMMA)-based micromodel. We estimated the cumulative oil recovery by comparing the results of two-stage surfactant flooding with intermediate CO2 swelling at different pressures. This novel hybrid approach test consisted of a three-stage sequence: an initial flooding stage, followed by intermediate CO2 swelling, and a second flooding stage. The results revealed an increase in cumulative oil recovery by nearly 10% upon a 2% (w/v) solution of SDS and water flooding compared to just water flooding. The results showed the visual phenomenon of oil imbibition during the surfactant flooding process. This innovative approach holds immense potential for future EOR processes, characterized by its unique combination of surfactant flooding and CO2 swelling, yielding higher oil recovery.