Generation Potential Research Articles

Investigating the sustainable energy generation potential of an invasive weed: Lantana camara.

Lantana camara, one of the world's top ten most invasive species, was initially cultivated for ornamental use. However, it spread uncontrollably across the fallow areas and agricultural lands, threatening approximately 44% of Indian forests. Its invasion disrupts ecosystems by suppressing nearby plant growth through allelopathy and poses toxicity risks to grazing ruminants. It significantly increases forest fire risk by adding large amounts of combustible biomass, particularly dried L. camara. Despite efforts to control it using mechanical, chemical, and biocontrol methods, the results have been largely unsatisfactory, with associated costs estimated at $18,000 per square kilometre. Considering these challenges, recent research explored the potential of L. camara as a bioenergy resource. TheL. camarabriquettes exhibit a heating value of approximately 20MJ/kg with a low sulphur (0.5%), nitrogen (1%), and ash content (2%), making them suitable for decentralised energy production. Furthermore, bioethanol production fromL. camarahydrolysate has shown promising results, yielding 0.33g/g withPichia stipitisand0.47g/g with Saccharomyces cerevisiae, which is comparable to other lignocellulosic feedstocks. Additionally, the gasification of L. camara using a downdraft gasifier produced syngas with a lower heating value (LHV) of 6.4MJ/Nm3. These findings demonstrate that using L. camara for bioenergy production presents a dual solution, addressing the growing demand for renewable energy and managing invasive species. This review aims to critically evaluate the potential and challenges associated with the different energy production pathways for L. camara, highlighting its role in sustainable energy generation.

Solar Species: Energy Optimization of Urban Form Through an Evolutionary Design Process

This paper proposes design guidelines to enhance energy efficiency and energy generation potential in active solar buildings. Additionally, it presents a variety of optimized urban forms characterized by attributes such as shape, layout, and number of buildings on the plot. These urban configurations are classified into solar species, each associated with a distinct range of high passive and active solar potential. These results were achieved by developing and applying a simulation-driven, multi-objective optimization technique for the early-stage design of a residential building cluster in a temperate climate. This method leverages both passive and active energy indicators, employing a genetic algorithm to identify optimal forms that maximize active solar potential while also minimizing operational energy demand. The approach utilizes a parametric modelling routine that relies on vertical cores and horizontal connections to produce design iterations featuring irregular geometry, while ensuring structural continuity and means of egress. The findings reveal a significant variability in onsite energy generation, with optimized solutions differing by a factor of 2.5 solely based on shape, underscoring the critical role of active solar potential. Taken together, these results hint at the descriptive and predictive capabilities of these solar species, making them a promising heuristic model for characterizing urban form in relation to energy performance.

The Impact of Generative Artificial Intelligence on Education: A Comparative Study

Generative Artificial Intelligence (GAI), also known as creative AI, is a technology capable of independently producing original and creative content, such as text, images, videos, music, or code. It uses machine learning algorithms to analyze and learn from a vast amount of data to generate similar or even innovative content. This type of AI is used in several fields, such as health for the production of new drugs, design to generate adaptive designs, video games to create immersive environments, and artificial intelligence to generate training data for new networks. Seizing the opportunities offered by AI innovations in education is crucial. We cannot afford to let time pass without doing anything and without benefiting from this technique. It is time to enhance traditional learning methods and adopt more innovative approaches that take advantage of the potential of AI, like content and exam generation, and also improve students' learning experience by exploiting their closeness to the technology. This paper aims to answer the question of when and how to use artificial intelligence responsibly, considering it as a technology and limiting its excessive use. Additionally, we present the impact of GAI through a comparative study of student performance in three exam types: a classical MCQ exam written by the teacher, an adaptive MCQ exam generated by GAI from a topic, and a third MCQ exam generated by GAI based on the content of the provided documents. This study is conducted on students undergoing a software testing training program. By evaluating their performance in the AI-generated adaptive MCQ exams and the traditional MCQ exam, we can gain insights into the potential of this technology to enhance educational practices and provide personalized learning experiences. This research will contribute to the ongoing discussion on responsible and effective utilization of GAI in education, paving the way for future advancements in the field.

City-wide Solar Radiation Potential Analysis by Coupling Physical Modelling and Machine Learning

Abstract. Addressing the challenges posed by climate change and meeting urban energy demands is of utmost importance in today's world. Building Integrated Photovoltaics (BIPV) emerges as a crucial solution for energy conservation and carbon emissions reduction in urban environments. However, traditional methods of assessing solar radiation on buildings using physical models are often computationally intensive and time-consuming. This paper introduces a novel hybrid approach that integrates physical model-based solar radiation calculation with machine learning techniques to analyze Solar Radiation Potential (SRP) across city-wide building infrastructure. The proposed approach precisely evaluates the SRP of representative blocks by leveraging computing-intensive physical models integrated with 3D building data. Subsequently, two machine learning models are developed to effectively predict the SRP of building roofs and facades across the entire city. To validate the efficacy of this approach, an experiment was conducted in Shenzhen, China, yielding insightful results. The findings reveal that Shenzhen has a huge potential for BIPV solar power generation, with mean annual total building roof and facade solar radiation values of 9.22*107 kwh and 2.47*108 kwh, respectively. It can be further observed that relying solely on rooftop installations is insufficient to meet electricity demand. This study not only provides an innovative alternative for city-wide SRP estimation by combining physical modeling and machine learning but also offers valuable insights for fostering low-carbon urban environments and informing data-driven and model-driven urban planning and management strategies.

Measuring rising heat and flood risk along the belt-and-road initiative

China’s global infrastructure financing flagship, the Belt and Road Initiative (BRI) encompasses countries hosting over 60% of the global population and one-third of worldwide GDP. It is based mainly on long-term loans that will mature decades into the future, and timely repayments are only possible if they remain commercially viable. But despite its vast global scope, little is known about the climate risks that could imperil the operations of BRI projects over the next few decades, and, consequently, threaten their long-term sustainability. We narrow this gap by estimating the impacts of future climate change on 217 BRI projects across 70 countries and 9 sectors in two dimensions. First, the effects of increased heat stress on human physical work capacity are calculated using the wet bulb globe temperature and an assessment of the workload for each selected BRI project. Second, the potential structural damages from more frequent flooding incidents are measured by return period (RP), where a shorter RP signals heightened risk. Both have direct impacts on human productivity and infrastructural integrity, which are essential to maintaining the operational viability and financial stability of BRI projects. We compared projected changes on both measures for the mid- and late-twentieth centuries (2041–2060 and 2081–2100) to the historical baseline (1981–2010). We found that BRI projects face escalating vulnerability to climatic risks on both counts. The results underscore a broad variance across different future carbon emission scenarios measured under the Shared Socioeconomic Pathways (including SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). BRI’s aggregated climatic risks are substantially elevated under a high carbon emission scenario compared to a low emission scenario. By the end of the twentieth century, labor workability under SSP3-7.0 (31%) could potentially decrease three times more compared to SSP1-2.6 (10%). Under an intermediate-emission scenario (SSP2-4.5), floods with a historical return period of 10 years could have a return period of 5 years in the future. Significantly hampering the utilization and economic return generation potential of infrastructure projects. In addition, regional geography contributes to risk heterogeneity, with 100-year floods occurring every 15 years in South Asia and every 24 years in Sub-Saharan Africa. Such climate risk implications, potentially overlooked by development financiers, represent significant risks to the sustenance of the BRI, estimated to be worth $1 trillion.

Jerusalem Artichoke: Nitrogen Fertilization Strategy and Energy Balance in the Production Technology of Aerial Biomass

Jerusalem artichoke (Helianthus tuberosus L.) is a plant with considerable potential for energy generation due to its rapid growth, high biomass yield, and resistance to environmental stresses. The aim of this study was to determine the influence of the nitrogen fertilization strategy on the yield and energy balance in the production technology of Jerusalem artichoke (JA) in a perennial cropping system. The article presents the results of a three-year experiment which was conducted in Poland to determine the effect of different N rates (0, 50, 75, and 100 kg ha−1) supplied with mineral fertilizers and liquid digestate on the energy balance in the production of JA aerial biomass. The experiment had a randomized block design with three replications. The demand for energy in JA cultivation reached 16.2–26.3 (year 1) and 2.9–14.6 GJ ha−1 (years 2 and 3). Energy inputs in the cultivation technology were reduced by 17–19% (year 1) and 35–47% (years 2 and 3) when mineral fertilizers were replaced with digestate. Jerusalem artichoke yields were lowest in the technology without fertilization (12.5 Mg ha−1 DM). Dry matter yield increased significantly (by 43–55%) after the application of 75 kg N ha−1, regardless of fertilizer type. The energy output of biomass peaked (230.1 GJ ha−1) in response to a mineral fertilizer rate of 75 kg N ha−1. In turn, the highest energy gain (218.5 GJ ha−1) was noted after the application of digestate at a rate equivalent to 75 kg N ha–1. The energy efficiency ratio was highest in the technology without fertilization (20.1) and after the application of digestate at a rate equivalent to 75 kg N ha−1 (19.7). Regardless of the factors that limit agricultural production, the energy balance of JA biomass production was most favorable when JA was fertilized with digestate at a rate equivalent to 75 kg N ha−1. The results of this study may pave the way for future research on novel agronomic strategies for sustainable bioenergy production, including nutrient recycling.

Locating the suitable large-scale solar farms in China's deserts with environmental considerations

Desert areas offer rich solar resources and low land use costs, ideal for large-scale new energy development. However, desert ecosystems are fragile, and large-scale photovoltaic (PV) power facilities pose ecological risks. Current assessments of PV plant sites in deserts lack consideration of wind-sand hazards and ecological impacts. In this study, we have developed a new large-scale photovoltaic (PV) site selection model that integrates the analytic hierarchy process with geographic information system technology, and applies it to the desert regions of China. The results show that the potential for large-scale PV power plants in China's deserts is significant, with 69.4 % of the region assessed as medium or higher. The most suitable area is 12.7 × 104 km2 (7.6 % of the overall study area), mainly centered in the Tibetan Plateau's Qaidam Basin Desert and the deserts of northern China, characterized by favorable solar resources, climate, and terrain. Across all regions, gravel deserts are recognized as more suitable for the construction of large-scale PV power projects than sandy deserts. Considering varying PV installation density scenarios with an installed capacity potential of 36.4–84.9 TW and system costs ranging from 10.0 to 33.5 trillion USD, the study estimates an annual solar power generation potential of 47–110 PWh which is 1.7–3.9 times the global electricity demand. Carbon emissions could be reduced by 26.8–62.6 gigatons annually, offsetting 73–170 % of global emissions. Covering just 4.8–11.5 % of China's desert area (8 × 104–19.4 × 104 km2) would meet the projected 2025 electricity needs of the country. This study lays the groundwork for spatial planning and benefit assessment of large-scale PV projects in desert regions, and reduces conflicts between PV plant construction and local ecosystem.

Development of Students’ Research Skills in the Context of Multilingual Education

The requirements to research activity and intellectual needs of students increase significantly in the modern conditions of modernization, namely in the conditions of multilingual education. Because the intellectual and scientific potential of the younger generation represents the main factor of future development of every civilized society. In the process of transition to the norm of the new generation, one of the requirements to graduate university is the formation of research abilities. Development of research skills of students in conditions of multilingual education is the subject of the article. The authors analyze the concept of research work, identify the structure of research activity and substantiate the importance and necessity of teaching of research skills. The authors define the concept of research skills and its categories. The research skills can be used to activate students' research potential.

Associations of coagulation parameters and thrombin generation potential with the incidence of type 2 diabetes: mediating role of glycoprotein acetylation.

Hypercoagulability is characterized by abnormal elevations of coagulation factor levels and increased thrombin generation potential. Prior studies demonstrated links between impaired glucose metabolism, endothelial dysfunction, and hypercoagulability. However, the associations between hypercoagulability and incident type 2 diabetes as well as its underlying mechanism remain unclear. We aimed to assess the associations between coagulation parameters including coagulation factor (F) VIII, FIX, FXI, fibrinogen, thrombin generation potential (lag time, endogenous thrombin potential [ETP], peak, time-to-peak, velocity) and incident type 2 diabetes, and to study the underlying mechanism by examining the mediating role of glycoprotein acetylation (GlycA). In the Netherlands Epidemiology of Obesity study, we applied a Cox Proportional-Hazards Model in 5718 participants after adjustment for confounders. We further conducted a mediation analysis investigating the mediation effect of GlycA on the observed associations. During a median follow-up of 6.7 years, 281 incident type 2 diabetes diagnoses were reported. Compared with the lowest quartile, hazard ratio (95% confidence interval) of the highest quartile was 2.47 (1.48-4.14) for FIX, 1.37 (0.85-2.20) for FVIII, 1.11 (0.76-1.63) for FXI, 0.98 (0.65-1.48) for fibrinogen, 1.56 (1.07-2.28) for ETP, 1.84 (1.23-2.74) for peak, 1.59 (1.08-2.33) for velocity, 0.92 (0.62-1.38) for lag time, and 1.21 (0.86-1.70) for time-to-peak. GlycA mediated only a small proportion of all observed associations. In conclusion, elevated levels of coagulation factor and thrombin generation potential are associated with incident type 2 diabetes, suggesting the involvement of hypercoagulability in the pathogenesis of type 2 diabetes.

This Microtubule Does Not Exist: Super-Resolution Microscopy Image Generation by a Diffusion Model.

Generative models, such as diffusion models, have made significant advancements in recent years, enabling the synthesis of high-quality realistic data across various domains. Here, the adaptation and training of a diffusion model on super-resolution microscopy images are explored. It is shown that the generated images resemble experimental images, and that the generation process does not exhibit a large degree of memorization from existing images in the training set. To demonstrate the usefulness of the generative model for data augmentation, the performance of a deep learning-based single-image super-resolution (SISR) method trained using generated high-resolution data is compared against training using experimental images alone, or images generated by mathematical modeling. Using a few experimental images, the reconstruction quality and the spatial resolution of the reconstructed images are improved, showcasing the potential of diffusion model image generation for overcoming the limitations accompanying the collection and annotation of microscopy images. Finally, the pipeline is made publicly available, runnable online, and user-friendly to enable researchers to generate their own synthetic microscopy data. This work demonstrates the potential contribution of generative diffusion models for microscopy tasks and paves the way for their future application in this field.

Synthetic Image Data Generation via Rendering Techniques for Training AI-Based Instance Segmentation

Synthetic image data generation has gained popularity in computer vision and machine learning in recent years. The work introduces a technique for creating artificial image data by utilizing 3D files and rendering methods in Python and Blender. The technique employs BlenderProc, a rendering tool for generating artificial images, to efficiently create a substantial amount of data. The output of the method is saved in JSON format, containing COCO annotations of objects in the images, facilitating seamless integration with current machine-learning pipelines. The paper shows that the created synthetic data can be used to enhance object data during simulation. The method can enhance the accuracy and robustness of machine-learning models by modifying simulation parameters like lighting, camera position, and object orientation to create a variety of images. This is especially beneficial for applications that require significant amounts of labelled real-world data, which can be time-consuming and labour-intensive to obtain. The study addresses the constraints and potential prejudices of creating synthetic data, emphasizing the significance of verifying and assessing the generated data prior to its utilization in machine learning models. Synthetic data generation can be a valuable tool for improving the efficiency and effectiveness of machine learning and computer vision applications. However, it is crucial to thoroughly assess the potential limitations and biases of the generated data. This paper emphasizes the potential of synthetic data generation to enhance the accuracy and resilience of machine learning models, especially in scenarios with limited access to labelled real-world data. This paper introduces a method that efficiently produces substantial amounts of synthetic image data with COCO annotations, serving as a valuable resource for professionals in computer vision and machine learning.

High-performance hydrogel membranes with superior environmental stability for harvesting osmotic energy

A collection of innovative nanofluidic systems has been developed to harness Gibbs free energy and convert it into osmotic energy. Nevertheless, these systems frequently encounter challenges such as low output power density, inadequate mechanical stability, and diminished performance in extreme conditions. In this work, we utilized a simple thermal polymerization method to fabricate a charged phytic acid (PA)–acrylic acid (AA)–3-sulfopropyl acrylate potassium salt (SPAK) hydrogel (PASH) membrane with anti-drying and anti-freezing properties, alongside mechanical stability. Using a silicon window with a testing area of 3 × 10−8 m2, a high power density of 30.94 W/m2 was achieved in artificial seawater and river water environments (0.5/0.01 M NaCl). Notably, an even higher power density of 103.9 W/m2 was attained in artificial saline lake and river water environments (5/0.01 M NaCl). Meanwhile, the PASH membrane demonstrated outstanding performance in low temperatures and various acidic and alkaline environments, offering the potential for osmotic power generation in extreme conditions. This work provides essential theoretical foundations and experimental evidence for the development of sustainable osmotic energy conversion technologies, contributing to the advancement and innovation in the field of blue osmotic energy.

The substantial generation of photochemically produced reactive intermediates (PPRIs) in algae-type zones from one large shallow lake promoted the removal of organic pollutants

Photochemically produced reactive intermediates (PPRIs) are ubiquitously present in aquatic systems and hold significant importance in biogeochemical cycles. The photochemical reaction of dissolved organic matter (DOM), known as photosensitizers upon irradiation, is the main pathway for PPRIs generation. However, the PPRIs produced by algal-derived organic matter (ADOM) and their environmental effects remains elusive. This study confirmed that substantial PPRIs were generated by ADOM in the algal-derived areas. UV absorption spectra, fluorescence spectra and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were then indicated a significant correlation between the molecular weight of DOM and the quantum yield of PPRIs, with lower molecular weight of DOM exhibiting a higher potential for PPRIs generation. Orthogonal partial least squares (OPLS) were used to build novel multivariate predictive models for indicating the PPRIs production in algae-type zone. Also, the higher concentrations of PPRIs could significantly removal different kinds of organic pollutants, such as bisphenol A (BPA), sulfadiazine (SDZ) and 17α-ethinylestradiol (EE2). Quenching experiments further elucidated that 3DOM⁎ was the key specie for pollutants degradation, serving as the precursor to generate a series of PPRIs. This study highlighted the importance of PPRIs generated from ADOM in the natural attenuation of pollutants and provided a new insight for understanding the self-purification in aquatic system.

Turbine system vs engine generator in the production of energy by methane in the pacific of Mexico: a technical, economic, and environmental analysis.

This paper aims to analyze the potential of energy production using methane from organic waste as a sustainable alternative to mitigate the environmental impact of energy generation from fossil fuels and the generation of Municipal Solid Waste (MSW). To carry out the analysis, two technologies were evaluated from technical, economic, and environmental perspectives. The LandGEM model was used to estimate the methane generation potential. The amount of energy produced, along with the respective financial indicators, was also calculated. The results showed that for an average feed of 671,892 tons per year of waste, 6160 ft3/min of CH4 would be generated at a maximum peak in the tenth year, with an annual average of 4735 ft3/min. Additionally, 0.831 million metric tons of CO2 equivalent would be avoided. In terms of power generation, a combined cycle micro-turbine system with an installed capacity of 11.35 MW would be feasible and would yield an Internal Rate of Return (IRR) of 35% and a Net Present Value (NPV) of $11,608,006 USD. For an engine-generator system, it was not possible to verify profitability due to a significant increase in capital and O&M costs. These results are intended to provide reference information to assist in the decision-making process related to the implementation of projects aligned with the Sustainable Development Goals within the framework of the 2030 Agenda.

Characteristics of Ozone Photochemical Reaction and Emission Reduction Strategies in Summer and Autumn in Shangqiu

Recently, ozone （O3） pollution in Shangqiu has become increasingly prominent, especially in summer and autumn, crucially affecting the local environmental air quality. Based on the monitoring data of O3 pollution days from the Environmental Monitoring Station in June and September 2022 （representing summer and autumn） in Shangqiu, an observation-based model （OBM） was used to study the causes and photochemical reaction characteristics of O3 pollution in the city and precursor emission reduction strategies were studied. The observation results indicated that during summer in Shangqiu, the ρ（O3） and O3 daily maximum 8 h moving concentrations [ρ（MDA8-O3）] were 149.7 μg·m-3 and 195.4 μg·m-3, whereas in autumn, ρ（O3） and ρ（MDA8-O3） were 119.8 μg·m-3 and 173.9 μg·m-3, respectively； the O3 concentration in summer was significantly higher than that in autumn. Ozone sensitivity research showed that the generation of O3 in summer and autumn in Shangqiu was controlled by volatile organic compounds （VOCs）. Among them, oxygen-containing volatile organic compounds （OVOCs）, aromatic hydrocarbons, and alkenes contributed the most to the ozone generation potential （OFP） and ·OH reactivity （L·OH）, and the control must have been strengthened. The OBM simulation results indicated that the maximum O3 generation rates in summer and autumn were 23.0×10-9 h-1 and 13.6×10-9 h-1, with maximum net generation rates of 17.4×10-9 h-1 and 10.4×10-9 h-1 and the maximum and maximum net generation rates of O3 in summer were 1.68 times higher than those in autumn, indicating that the photochemical reactions in summer were significantly stronger than those in autumn. Compared with that in summer, the generation of O3 in autumn was greatly influenced by regional inputs from other regions or cities, with a maximum input of 14.2×10-9 h-1. The prevention and control of O3 pollution in the summer and autumn seasons in Shangqiu should mainly focus on controlling VOCs. The reduction ratio of VOCs/nitrogen oxides （NOx） in autumn should be greater than that in summer and the reduction ratios of 3∶1 in summer and 4∶1 in autumn could be adopted for control.

Organic geochemistry and 1D-basin modeling in the Taranaki Basin, New Zealand: Implications for deltaic-source rocks of the cenozoic oil and condensate reservoirs

The Taranaki Basin in New Zealand is an area of active exploration for oil and condensate. This research focuses on integrating geochemical characteristics and 1-D basin modeling to Late Cretaceous to Miocene source rock systems, along with oil and condensate data from fifty-seven wells in the Taranaki Basin. The geochemical study reveals that the oil and condensate samples were generated from clay-rich source rocks, containing mixed organic matter, with large amounts of terrestrial organic matter input. These source rocks were deposited in fluvial to fluvio-deltaic environments under oxic conditions. The presence of oleanane in both oil and condensate samples suggests that the source rocks had a significant terrestrial component and deposited during the Late Cretaceous to Cenozoic. Using various biomarker proxies, oil-source rock correlation along with 1-D basin modeling revealed that the oil and condensate were mainly derived from the Late Cretaceous Rakopi and Paleocene Farewell formations at different maturity stages. The oils were generated within the peak-mature oil window, while the condensates primarily resulted from the secondary cracking of oil taking place in the source rock within the gas generation window. This finding is consistent with the 1-D basin modeling results. The model shows that the Paleocene Farewell source rock has achieved the primary stage of oil generation (0.55–0.95 Easy %Ro), contributing to most of the discovered oils in the Cenozoic reservoir rocks. Meanwhile, the Late Cretaceous Rakopi source rock reached the gas window with a higher vitrinite reflectance of more than 1.30 Easy %Ro, indicating greater gas generation potential.

Geochemical characterization of Upper Cretaceous organic-rich deposits: Insights from the Azraq Basin in Jordan

Cretaceous sedimentary successions in Jordan are among the most promising unconventional global petroleum resources. The Upper Cretaceous deposits in the Azraq Basin in Jordan were analyzed using Rock-Eval pyrolysis, stable carbon isotopes, vitrinite reflectance, and biomarkers, as well as 1D basin modeling. This study aims to evaluate source input, depositional environment, burial history and potential for hydrocarbon generation of the Upper Cretaceous organic-rich deposits in Jordan. The Upper Cretaceous samples exhibit variable source rock potential. Maastrichtian samples are very good, primarily oil-prone, while Campanian and Cenomanian samples range from fair to poor, with Turonian samples showing good petroleum generation potential. Thermal maturity analysis of Cretaceous samples reveals higher maturity in Turonian and Cenomanian samples compared to the thermally immature Maastrichtian and Campanian samples based on the maximum temperature of pyrolytic hydrocarbons generation, vitrinite reflectance, production index and biomarker indicators. The molecular fossils data suggest that the organic matter of these source rocks was primarily of marine phytoplankton origin, with considerable contributions from microbial biomass, and deposited under oxygen-depleted bottom water conditions. Additionally, the resemblance in molecular composition between Cretaceous source rocks and crude oil samples suggests a common origin for the hydrocarbons. The highest contents of organic matter have been recorded at the top of the Maastrichtian and at the bottom of the Turonian sections. These findings correspond with Maastrichtian upwelling deposits in North Africa and the Middle East, as well as with deposits from the Cenomanian-Turonian Oceanic Anoxic Event (OAE2).

Strategic approach to accelerating regional bioenergy development: Bioelectricity for emission reduction and sustainability

The western Himalayas hold significant potential for generating bioelectricity from dry pine needle biomass. This approach provides multiple benefits, including forest fire prevention, electricity generation, and emission reduction. However, despite these advantages, the growth of this sector has fallen short of expectations. The projected electricity generation potential from pine needle biomass in this region could lead to a significant annual emission reduction of 1.7 MtCO2e through forest fire prevention and an additional 0.49 MtCO2e by displacing carbon-intensive grid electricity. Regional bioenergy development plays a crucial role in the global energy transition and aligns with SDG 13, "Climate Action," and SDG 7, "Affordable and Clean Energy." By focusing on bioenergy as a renewable and accessible energy source, local and regional communities can contribute to climate action while simultaneously ensuring affordable and clean energy for their communities. Therefore, this paper employs a hybrid SWOT-AHP analysis as a strategic planning tool to achieve emission reduction targets by stimulating regional bioenergy growth. Twenty-nine SWOT factors were identified under four variables, and then the AHP technique was employed to assign priority weights to both SWOT variables and factors. Combining SWOT with AHP analysis provides quantitatively determined priorities for the factors, enabling their objective comparison. Eventually, this study offers a comprehensive perspective that leads to policy recommendations and serves as a valuable resource for relevant stakeholders, policymakers, and researchers seeking to achieve emission reduction goals through bioelectricity generation.

Reliability-aware techno-economic assessment of floating solar power systems

Floating solar photovoltaic systems (FPV) have emerged as a promising technology to harness solar energy on water surfaces. With its numerous benefits, including increased land availability, reduced water evaporation, and improved system cooling, FPV systems hold great potential for sustainable energy generation. However, due to its unique installation and operation in water bodies, the management of aging becomes a critical factor to ensure long-term success. Consequently, reliability analysis plays a pivotal role in predicting and mitigating operational risks and estimating the economic feasibility of FPV projects. In this context, this paper presents a reliability-aware techno-economic assessment approach of FPV systems. The approach is tested with a case study in India, and the results are compared with ground-based photovoltaic (GPV) systems. Here, different failure and repair strategies are taken into account to determine the lifetime performance. Results showed that even though FPV system has higher failure rate, considering standard maintenance, the energy generated by the FPV system is 5.38% higher than similar GPV system. The cost of electricity by the PV system depends on the repair and maintenance. For normal maintenance the levelized cost of electricity (LCOE) for FPV system is calculated as 0.0551 $/kWh which is comparable to the LCOE by GPV systems, while for reduced repair actions, the LCOE of the FPV is higher than the LCOE of the GPV system.

An electrocatalytic iodine oxidations-based configuration for hydrogen and I2/I3− co-productions driven by the Zn-air/iodine battery

Electrochemical water splitting and energy storage are key for a sustainable energy future, despite the challenges related to undesirable overpotentials and high voltage requirements. Herein, we introduce a synergistic approach between a low overpotential hydrogen evolution reaction (HER) and a low voltage zinc-air/iodine battery (ZAIB) by coupling with iodide oxidation half reactions. By developing a Pt/Co3O4 electrocatalyst in two steps and with under 2% Pt loading, we achieve an unprecedented low full cell potential for hydrogen generation at 0.574 V, exhibiting an ultra-high reduction of energy consumption of 64.7%. The Pt/Co3O4 electrode also enables ZAIB to record a power density of 154 mW cm−2 at an ultra-low charging potential of 1.68 V. Mechanistic studies and DFT calculations of the novel electrode confirm an electron rich Pt-Co interface and favorable Pt-I interactions, facilitating both HER and IOR reactions. Our design provides critical technology for potential large-scale renewable energy projects.