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AIM: The aim of this study was to describe the evolution of a regional neonatal service in Sierra Leone and changes in mortality and service use as it transitioned from a non-specialist service to a dedicated special care baby unit (SCBU). METHODS: This was a retrospective observational study. Anonymised data were taken from the ward admissions books at Bo Government Hospital, and trends in admissions and mortality within the neonatal service were examined for each stage of the department's evolution. RESULTS: Four phases of the service's development were identified between November 2015 and October 2019. Records of 2377 admissions and 333 deaths were identified. The average number of admissions per month and deaths per month varied by service development phase. There was a trend towards reduced death rates and increased numbers of admissions as the unit evolved into a dedicated neonatal unit with a reliable electricity supply. CONCLUSIONS: The development of an adequately sized SCBU with a reliable electricity supply and specially trained staff was associated with a reduction in the death rate and an increase in admissions.

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Peer-to-peer (P2P) energy trading is an innovative concept poised to transform energy demand management and utilization. EnergyShare AI is a powerful peer-to-peer energy exchange system that operates on a P2P model that integrates advanced machine learning with distributed energy sharing. This paper presents EnergyShare AI, a technology that connects consumers and prosumers through solar arrays, energy storage systems (ESS), and electric vehicles (EVs). Using Deep Reinforcement Learning (DRL) algorithms, Energy Share AI significantly improves energy management efficiency and substantially reduces costs. Our approach offers several advantages over traditional linear integer programming models, particularly in optimizing bidirectional energy transfer involving EVs and highlighting the critical role of ESS and photovoltaic (PV) systems in facilitating efficient P2P energy trading. Our research results show that successful P2P exchange can lead to significant cost savings and improved sustainability, thereby increasing the amount of energy transferred between different household profiles and stages of human development.

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Solar energy harvesting and storage are essential in the future mix of renewable energy technologies. Hierarchical coral-structured coatings have been shown to yield high solar absorptance in concentrating solar thermal (CST) systems. However, interfacial delamination and scalability challenges owing to material complexity pose significant hurdles for the widespread industrial adoption of these hierarchical CST coatings. Here, a coral-structured coating is proposed whose black pigments are strongly bonded by titania, which is a material that mitigates interfacial delamination. Importantly, this coating follows a facile deposition procedure suitable for large-scale solar receivers. The drone-deposited coating inhibits cation diffusion and maintains a stable solar absorptance of 97.39 ± 0.20 % $97.39\pm 0.20\%$ even after long-term (3000 h) high-temperature ( 800 ∘ C $800 \,^{\circ }\mathrm{C}$ ) aging. The scalability of developed coating represents a substantial advancement in the implementation of light-trapping enhancement and maintenance approaches across a wide range of CST applications.

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Concentrated solar power (CSP) is one of the few sustainable energy technologies that offers day-to-night energy storage. Recent development of the supercritical carbon dioxide (sCO2) Brayton cycle has made CSP a potentially cost-competitive energy source. However, as CSP plants are most efficient in desert regions, where there is high solar irradiance and low land cost, careful design of a dry cooling system is crucial to make CSP practical. In this work, we present a machine learning system to optimize the factory design and configuration of a dry cooling system for an sCO2 Brayton cycle CSP plant. For this, we develop a physics-based simulation of the cooling properties of an air-cooled heat exchanger. The simulator is able to construct a dry cooling system satisfying a wide variety of power cycle requirements (e.g., 10-100 MW) for any surface air temperature. Using this simulator, we leverage recent results in high-dimensional Bayesian optimization to optimize dry cooler designs that minimize lifetime cost for a given location, reducing this cost by 67% compared to recently proposed designs. Our simulation and optimization framework can increase the development pace of economically-viable sustainable energy generation systems.

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The Royal Society and UK Department for International Development supported a consortium of three universities across sub-Saharan Africa and Imperial College London with the aim of developing new knowledge on direct-steam-generation concentrated solar power (CSP) plants and supporting relevant capacity building across the Universities of Lagos, Mauritius and Pretoria. Key research findings from the programme include an improved flow-classification scheme for two-phase, liquid-liquid flows; testing of advanced surfaces with much-improved steady-state heat transfer performance-the commercial nanoFLUX surface showed up to 200% higher heat-transfer coefficients (HTCs) in pool boiling compared with other surfaces with R-134a/R-245fa; first-of-a-kind measurements of transient flow boiling HTCs, which were up to 30% lower in step perturbations than quasi-steady-state expectations in horizontal pipes with R-245fa; error estimation and corrections for laser-induced fluorescence (LIF) measurements, leading to the development of an adapted planar LIF technique with uncertainty <10% for local, instantaneous film thickness measurements in annular flows, and the application of such diagnostic methods to pool, falling-film and flow boiling in pipes; and predictions of an ~80% increase in the net present value of a case-study CSP plant when integrated with solid storage media.

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The need to move to more sustainable energy generation has become a major concern among world leaders due to the debilitating effect of greenhouse gases on the environment. Africa has the greatest potential to transition to more sustainable energy sources due to its enormous renewable energy resource potential, particularly solar. This study thus assessed the potential of generating power using a concentrated solar tower power plant (CSTP) at three different locations in Algeria. The study evaluated the system's technical, environmental, economic, and employment creation potential and analyzed the hydrogen and ammonia creation potential using the electricity produced by the CSTP system. Naama, Laghouat, and Ghardaia recorded annual energies of 507 GWh, 502 GWh, and 547 GWh, with capacity factors of 57.6%, 57.6%, and 62%, respectively. A real levelized cost of energy ranging between 7.72 and 8.47 cent$/kWh was obtained. A total of 8530 tons of nitrogen and 1844 tons of hydrogen will be theoretically needed to produce ammonia (fertilizer) for 500,000 hectares of arable land for agricultural activities. In addition, using hydrogen from the CSTP system to produce the estimated ammonia will save 6124.56 tons of CO2 emissions from polluting the environment annually. The creation of thousands of direct and indirect jobs will significantly benefit Algerians. The study concluded with some policy recommendations based on its findings.

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The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

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The discrepancy between the operating and design capacities of solar plants in eastern Uganda is alarming; about 35 % underperformance in solar power generation is observed. The goal of the current study is to minimize this disparity by improving the design models. Considering only cell temperature in the power generation model is responsible for the observed difference in design and operational solar power generated, the present study used a thermocouple to directly measure cell temperature, an anemometer to measure wind speed, and a solar power meter to measure irradiance. These extrinsic factors were used to modify the power generation model based only on cell temperature through the direct correlation of cell temperature, wind speed, and irradiance with solar power generation. Thus, the absence of extrinsic factors (wind speed and irradiance) in the design models is responsible for the colossal drop in solar power generated. Empirically, the missing extrinsic factors were used to transform the implicit solar power model into an explicit model. The development of a solar power generation model, multiple differential models, simulation and experimentation with a pilot solar rig served as alternate model for the prediction of solar power generation. The second-order differential model validated well with empirical solar power generated in Busitema, Mayuge, Soroti, and Tororo study areas based on RMSEs (0.6437, 0.6692, 0.2008, 0.1804, respectively), thus, narrowing the gap between the designed and operational solar power generated. Mayuge and Soroti recorded the highest solar power generation of 9.028 MW compared to Busitema (8.622 MW) and Tororo (8.345 MW), suggesting that it has a conducive site for installing future solar plants. The above results support the use of empirical explicit (triple) and second-order differential models for the design and operation of power plants.

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The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer's manual labor.

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Photo-galvanic cells are liquid electrolyte-based dye-sensitized solar cells. Chemically, the dye/pigment photo-sensitizer, reductant, surfactant, and alkali materials are the main fabrication components of these cells. Most dye/pigment materials are more soluble and stable at high pH. The pH of Potassium hydroxide (an alkali of plant nutrient 'potassium' element) is very high. Therefore, Potassium hydroxide is supposed to be the best eco-friendly and effective alkali medium for photogalvanics. As far as alkali is concerned, NaOH has been exploited extensively in photo-galvanics. Although, the NaOH-based photo-galvanics show good electrical output, it is plagued with some drawbacks like shorter shelf life, high cost, unsafe for skin, low conductivity, low water solubility, etc. Therefore, in the present research, the KOH has been exploited as an alkali material for harvesting solar energy using the Sunset Yellow FCF dye sensitizer-Ascorbic acid reductant-CTAB surfactant cylindrical cell designed photo-galvanic system. In the present study, the observed optimum cell performance is as follows-open-circuit potential 777 mV, maximum current 25000 µA, short-circuit current 5600 µA, power 733.6 µW, fill factor 0.16, and efficiency is 19.77 % at pH 14.30. The Sunset Yellow FCF dye shows very high photostability and photo-absorption with KOH alkali. The power storage capacity is sufficiently robust, as the cell is capable of supplying power at its ∼36.16 % capacity after a very long time of 24 h. The KOH-Sunset Yellow FCF dye sensitizer-Ascorbic acid reductant-CTAB surfactant photo-galvanics in the present study show improved results over the reported results for the NaOH-Sunset Yellow FCF dye sensitizer-Ascorbic acid reductant-CTAB surfactant photo-galvanics. The reasons for the good photo-galvanics with KOH alkali may be attributed to some peculiar chemical and physical properties of KOH vis-à-vis the chemical and physical properties of NaOH.

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Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6-7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

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In this study, the thermal and drying characteristics of a thin layer food sample were investigated. An indirect type, simple, efficient, and economically feasible solar dryer was fabricated and used for food preservation. However, a dynamic model of a fabricated solar dryer was also presented to gain a better insight into the drying and thermal actions. This model consists of thermal modeling of the drying chamber, solar collector, and solar-dried food sample. The law of conservation of energy was applied to evaluate the temperature at different sections of the solar dryer with respect to drying time. All listed model equations were solved in the MATLAB environment. This study helps to examine the influence of solar radiation on the collector plate temperature, drying chamber temperature, food sample temperature, and performance parameters such as thermal efficiency with respect to drying time. Model data was found in good agreement with experimental data within a 4% error. It is concluded that the drying of food material is affected by air temperature, the collector temperature, mode of heat transfer, and material characteristics such as dimension and mass of the food sample.

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The global need for energy is increasing at a high rate and is expected to double or increase by 50%, according to some studies, in 30 years. As a result, it is essential to look into alternative methods of producing power. Solar photovoltaic (PV) power plants utilize the sun's clean energy, but they're not always dependable since they depend on weather patterns and requires vast amount of land. Space-based solar power (SBSP) has emerged as the potential solution to this issue. SBSP can provide 24/7 baseload carbon-free electricity with power density over 10 times greater than terrestrial alternatives while requiring far less land. Solar power is collected and converted in space to be sent back to Earth via Microwave or laser wirelessly and used as electricity. However, harnessing its full potential necessitates tackling substantial technological obstacles in wireless power transmission across extensive distances in order to efficiently send power to receivers on the ground. When it comes to achieving a net-zero goal, the SBSP is becoming more viable option. This paper presents a review of wireless power transmission systems and an overview of SBSP as a comprehensive system. To introduce the state-of-the-art information, the properties of the system and modern SBSP models along with application and spillover effects with regard to different sectors was examined. The challenges and risks are discussed to address the key barriers for successful project implementation. The technological obstacles stem from the fact that although most of the technology is already available none are actually efficient enough for deployment so with, private enterprises entering space race and more efficient system, the cost of the entire system that prevented this notion from happening is also decreasing. With incremental advances in key areas and sustained investment, SBSP integrated with other renewable could contribute significantly to cross-sector decarbonization.

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Researchers from all around the world have been paying close attention to particle-based power tower technologies. On the King Saud University campus in the Kingdom of Saudi Arabia, the first integrated gas turbine-solar particle heating hybrid system has been realized. In this study, two different types of experiments were carried out to examine how susceptible prospective liner materials for thermal energy storage tanks were to erosion. An accelerated direct-impact test with high particulate temperature was the first experiment. A low-velocity mass-flow test was the second experiment, and it closely mimicked the flow circumstances in a real thermal energy storage tank. The tests were conducted on bare insulating fire bricks (IFBs) and IFBs coated with Tuffcrete 47, Matrigun 25 ACX, and Tuffcrete 60 M. The latter three lining materials were high-temperature-resilient materials made by Allied Mineral Products Inc. (AMP) (Columbus, OH, USA). The results showed that although IFBs coated with AMP materials worked well in this test, the accelerated direct-impact test significantly reduced the bulk of the bare IFB. As a result, lining substances must be added to the surface of IFBs to increase their strength and protection because they cannot be used in situations where particles directly impact their surface. On the other hand, the findings of the 60 h cold-particle mass-flow test revealed that the IFBs were not significantly eroded. Additionally, it was discovered that the degree of erosion on the samples of bare IFB was unaffected by the height of the particle bed.

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The development of a new generation of solid particle solar receivers (SPSRs) with high solar absorptivity (0.28-2.5 µm) and high infrared emissivity (1-22 µm) is crucial and has attracted much attention for the attainment of the goals of "peak carbon" and "carbon neutrality". To achieve the modulation of infrared emission and solar absorptivity, two types of medium- and high-entropy rare-earth hexaboride (ME/HEREB6) ceramics, (La0.25Sm0.25Ce0.25Eu0.25)B6 (MEREB6) and (La0.2Sm0.2Ce0.2Eu0.2Ba0.2)B6 (HEREB6), with severe lattice distortions were synthesized using a high-temperature solid-phase method. Compared to single-phase lanthanum hexaboride (LaB6), HEREB6 ceramics show an increase in solar absorptivity from 54.06% to 87.75% in the range of 0.28-2.5 µm and an increase in infrared emissivity from 76.19% to 89.96% in the 1-22 µm wavelength range. On the one hand, decreasing the free electron concentration and the plasma frequency reduces the reflection and ultimately increases the solar absorptivity. On the other hand, the lattice distortion induces changes in the B-B bond length, leading to significant changes in the Raman scattering spectrum, which affects the damping constant and ultimately increases the infrared emissivity. In conclusion, the multicomponent design can effectively improve the solar energy absorption and heat transfer capacity of ME/HEREB6, thus providing a new avenue for the development of solid particles.

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Wastewater treatment plants (WWTPs) consume significant amount of energy to sustain their operation. From this point, the current study aims to enhance the capacity of these facilities to meet their energy needs by integrating renewable energy sources. The study focused on the investigation of two primary solar energy systems in As Samra WWTP in Jordan. The first system combines parabolic trough collectors (PTCs) with thermal energy storage (TES). This system primarily serves to fulfill the thermal energy demands of the plant by reducing the demands from boiler units, which allows more biogas for electricity generation. The second system is a photovoltaic (PV) system with Lithium-Ion batteries, which directly produces electricity that will be used to cover part of the electrical energy demands of plant. To assess the optimal configuration, two distinct scenarios have been formulated and compared to the current case scenario (SC#1). The first scenario focuses on maximizing the net present value (NPV) and minimizing the levelized cost of electricity (LCOE). The second scenario is centred on minimizing the levelized cost of heat (LCOH). The findings indicate that both scenarios succeeded in reducing the reliance on the grid to a value that reach 1 %. Moreover, they both reduced biogas percentage in energy production from 88 % to approximately 65 % through the integration of the PV system. In terms of thermal demand, SC#2 reduced the reliance on biogas boiler units from 100 % to 25 %, while SC#3 achieved an even more impressive reduction to just 8 %. The best LCOE value was attained in SC#2, at 0.0895 USD/kWh, with an NPV of 10.54 million USD. Conversely, SC# 3 yielded an LCOH value of 0.0432 USD/kWhth compared to 0.0534 USD/kWhth USD for SC#2. Despite their relatively high capital and operating costs, SC#2 and SC#3 managed to substantially decrease the annual electricity expenditure from approximately 2 million USD to 86,000 USD and 0 USD, respectively.

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This study proposes a novel honey bee dancing (HBD) maximum power point tracking (MPPT) algorithm inspired by the foraging behavior of honey bees. When a bee finds nectar, it returns to the honeycomb and dances to inform others about the location of the nectar. Other bees then fly towards the location and gather the nectar. The proposed HBD algorithm uses five bees searching for the nectar who communicate with each other about the location and the quantity of nectar by dancing. Finally, the five bees found the location of the most nectar which is represented by the maximum power point. The proposed HBD algorithm applies to uniform irradiance condition (UIC) and partial shaded condition (PSC). It is then compared with the PV panel output power and load relationship (OPLR) algorithm and perturb and observe (P&O) algorithm. Experimental verification has been performed under both the UIC and PSC. At the UIC's irradiance level of 200 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 120 W, 118 W, and 94.5 W, respectively, with efficiencies of 99 %, 97 %, and 78 %. Additionally, under the PSC with an irradiance level of 600 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 218 W, 189.2 W, and 74.8 W, respectively, with efficiencies of 99 %, 86 %, and 34 %, and convergence times of 4.7 ms, 6.5 ms, and 6 ms, respectively. It is evident from the results that the solar MPPT performance of the proposed HBD algorithm is better than the OPLR algorithm and P&O algorithm. This method ingeniously combines the foraging behavior of bees with solar power generation to produce the maximum natural power. This approach does not require the development of photovoltaic (PV) panel specification data, complex calculations, and additional temperature meters and heliographs. It is highly efficient and has significant economic benefits.

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Electric vehicles (EVs) have become an attractive alternative to IC engine cars due to the increased interest in lowering the consumption of fossil fuels and pollution. This paper presents the design and simulation of a 4 kW solar power-based hybrid EV charging station. With the increasing demand for electric vehicles and the strain they pose on the electrical grid, particularly at fast and superfast charging stations, the development of sustainable and efficient charging infrastructure is crucial. The proposed hybrid charging station integrates solar power and battery energy storage to provide uninterrupted power for EVs, reducing reliance on fossil fuels and minimizing grid overload. The system operates using a three-stage charging strategy, with the PV array, battery bank, and grid electricity ensuring continuous power supply for EVs. Additionally, the system can export surplus solar energy to the grid, reducing the load demand. The paper also discusses the use of MPPT techniques, PV cell modeling, and charge controller algorithms to optimize the performance of the hybrid charging station.

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Solar power tower technology has strong potential among the other concentration solar power techniques for large power generation. Therefore, it is necessary to make a new and efficient power conversion system for utilizing the solar power tower system. In present research, a novel combined cycle is proposed to generate power for the application of the solar power tower. The pre-compression configuration of the Brayton cycle is used as a topping cycle in which helium is taken as the working fluid. The transcritical CO2 cycle is used as bottoming cycle for using the waste heat. The proposed system is investigated based on exergy, energy, and exergoenvironmental point of view using computational technique engineering equation solver. Also, the parametric analysis is carried out to check the impact of the different variables on the system performance. It is concluded that the overall plant's optimized thermal and exergy efficiencies are obtained as 31.59% and 33.12%, respectively, at 800 °C optimum temperature of combined cycle and 850 W m-2 of direct normal irradiation and 2.278 of compressor pressure ratio. However, exergetic stability factor and exergoenvironmental impact index are observed as 0.5952 and 0.6801 respectively. The present proposed system performs better than the previous studies with fewer components.

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This study critically reviews the key aspects of nanoparticles and their impact on molten salts (MSs) for thermal energy storage (TES) in concentrated solar power (CSP). It then conducts a comprehensive analysis of MS nanofluids, focusing on identifying the best combinations of salts and nanoparticles to increase the specific heat capacity (SHC) efficiently. Various methods and approaches for the synthesis of these nanofluids are explained. The article presents different experimental techniques used to characterize nanofluids, including measuring the SHC and thermal conductivity and analyzing particle dispersion. It also discusses the challenges associated with characterizing these nanofluids. The study aims to investigate the underlying mechanisms behind the observed increase in SHC in MS nanofluids. Finally, it summarizes potential areas for future research, highlighting crucial domains for further investigation and advancement.