Irradiance Fluctuations Research Articles

Ovule and seed development of crop plants in response to climate change

The ovule is a plant structure that upon fertilization, transforms into a seed. Successful fertilization is required for optimum crop productivity and is strongly affected by environmental conditions including temperature and precipitation. Climate change refers to sustained changes in global or regional climate patterns over an extended period, typically decades to millions of years. These shifts can result from natural processes like volcanic eruptions and solar radiation fluctuations, but in recent times, human activities—especially the burning of fossil fuels, deforestation, and industrial emissions—have accelerated the pace and scale of climate change. Human-induced climate change impacts the agricultural sector mainly through global warming and altering weather patterns, both of which create conditions that challenge agricultural production and food security. With food demand projected to sharply increase by 2050, urgent action is needed to prevent the worst impacts of climate change on food security and allow time for agricultural production systems to adapt and become more resilient. Gaining insights into the female reproductive part of the flower and seed development under extreme environmental conditions is important to oversee plant evolution, agricultural productivity, and food security in the face of climate change. This review summarizes the current knowledge on plant reproductive development and the effects of temperature and water stress, soil salinity, elevated carbon dioxide, and ozone pollution on the female reproductive structure and development across grain legumes, cereal, oilseed, and horticultural crops. It identifies gaps in existing studies for potential future research and suggests suitable mitigation strategies for sustaining crop productivity in a changing climate.

Influence of particle morphology on solar thermal conversion performance and sensible heat storage capacity: A case study of TiO2@Go binary nanofluid

Photothermal catalytic hydrogen production driven by the full spectrum of outdoor solar radiation, represents a highly promising and efficient approach for hydrogen generation. This method is widely anticipated by researchers due to its potential to enhance photon utilization efficiency at the reaction source. However, limited attention has been devoted to the variations in photothermal conversion performance of particle reaction suspensions caused by objective fluctuations of solar irradiation, especially when the morphology of the nanostructure changes, which is a crucial factor for practical applications in hydrogen production. Based on this bottleneck, we prepared the typical photo-responsive TiO2@Go composite with varied dimensions (i.e., nanosphere TiO2@Go, nanorod TiO2@Go, nanosheet TiO2@Go) as research models, and systematically investigated their thermal conversion and sensible heat conversion performance under different working conditions. The results showed that at low nanofluid concentration, nanosphere TiO2@Go and nanosheet TiO2@Go exhibit better sensible heat conversion. As the particle concentration increases, the sensible heat conversion efficiency of all nanofluids decreases. It can also be found that the latent heat conversion efficiency tends to increase with the increase of concentration, but the nanorod TiO2@Go show the opposite, which should be closely related to the uniformity of the particle size in different directions. In addition, for the sensible heat storage properties of nanofluids, smaller particle size and abundant porosity due to particles aggregation were found to be more beneficial for varied shapes. The underlying functional mechanisms were elucidated associated with the analysis of particle structures, interfacial properties, and optical characteristics. We contend that our research could provide valuable insights for the large-scale implementation of solar photothermal hydrogen production and a reasonable selection of photothermal material morphology for outdoor working conditions.

Resiliency-informed optimal scheduling of smart distribution network with urban distributed photovoltaic: A stochastic P-robust optimization

An increased worldwide emphasis on resilience has resulted in a greater concentration on the distribution networks' resiliency. Therefore, the conventional approach to advance scheduling for power distribution networks is no longer sufficient, thereby requiring the creation of resilient scheduling approaches. This paper examines a proactive day-ahead scheduling method for distribution networks that are linked with significant renewable energy sources. The suggested approach employs a probabilistic scenario-generating strategy to quantify the uncertainties related to wind speed, solar irradiation, catastrophe duration, and load fluctuations. This study utilizes a hybrid stochastic p-robust optimization technique to enhance the analysis and provide robust strategies against risks. The present methodology employs a regret-based metric to quantify the adverse impacts of uncertainty inside the proactive scheduling procedure. The suggested methodology is verified using an IEEE 33-bus test system that includes significant distributed photovoltaic sources. The findings indicate a significant decrease of 43% in the highest level of regret, accompanied by a relatively limited rise of 1.22% in the operational expenses of the distribution network. This framework efficiently manages natural occurrences in the day-ahead scheduling process, making it a very promising method for improving the robustness of power distribution networks.

Research Progress of Metal Additive Manufacturing Technology and Application in Space: A Review

Metal additive manufacturing in space is a cutting-edge technology that is designed to meet the needs of space exploration and space station construction. This technology is capable of customizing and repairing key metallic parts in a space microgravity environment, providing the feasibility for long-term space tasks. It enables astronauts to perform on-site repairs and replace broken parts, significantly reducing the risk of mission failure on the International Space Station or during future deep space missions. Further, this technique opens new possibilities for constructing space bases by directly utilizing the materials from space, thereby reducing reliance on Earth’s resources. However, metal additive manufacturing in space faces challenges due to the unclear underlying mechanisms that lie in (I) the significant differences in the melting behaviors of materials in a space microgravity environment compared to those on Earth; and (II) extreme environmental factors, i.e., radiation and temperature fluctuations, that influence the metal additive manufacturing process and, consequently, the properties of the manufactured materials. This review provides a comprehensive analysis of those mechanisms underlying metal additive manufacturing in space, based on published works. Emphasis is placed on aluminum, titanium, iron, and copper-based metals. Our work may offer valuable guidance for reducing mission costs, improving safety, and enabling the on-demand production of complex components in the harsh environment of space by using metal additive manufacturing.

Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies

In 2023, solar photovoltaic energy alone accounted for 75% of the global increase in renewable capacity. Moreover, this natural energy resource is the one that requires the least investment, which makes it accessible to developing countries. Increasing return on investment in these regions requires a particular evaluation of environmental parameters influencing PV systems performance. Higher temperatures decrease PV module efficiency and, as a result, their power output. Additionally, fluctuations in solar irradiance directly impact the energy generated by these systems. Consequently, it is essential for investors to improve accurate predictive models that assess the power generation capacity of photovoltaic systems under local environmental conditions. Therefore, accurate estimation of maximum power generation is then crucial for optimizing photovoltaic (PV) system performances and selecting suitable PV modules for specific climates. In this context, this study presents an experimental comparison of three maximum power prediction methods for four PV module types (amorphous silicon, monocrystalline silicon, micromorphous silicon, and polycrystalline silicon) under real outdoor conditions. Experimental data gathered over the course of a year are analyzed and processed for the four PV technologies. Three different methods taking into account environmental parameters are presented and analyzed. The first estimation method utilizes irradiance as the primary input parameter, while two additional methods incorporate ambient temperature and PV module temperature for enhanced accuracy. The performance of each method is evaluated using standard statistical metrics, including the root mean square error (RMSE) and coefficient of determination (R2). The results demonstrate the effectiveness of all three methods, with RMSE values ranging from 1.6 W to 3.8 W and R2 values consistently above 0.95. The most appropriate method for estimating PV power output is determined by the specific type of photovoltaic module and the availability of meteorological parameters. This study provides valuable insights for selecting an appropriate maximum power prediction method and choosing the most suitable PV module for a given climate.

Short-term forecast of solar irradiance components using an alternative mathematical approach for the identification of cloud features

Solar energy technologies require precise solar forecasting to reduce power generation losses and protect equipment from irradiance fluctuations. This study introduces an alternative methodology for short-term forecasting of direct normal irradiance (DNI) and global horizontal irradiance (GHI) utilizing ground-based sky images captured by a single device. A low-cost all-sky imager (ASI) was developed, which implements an angular transformation and an optical flow technique to extract cloud features such as shape and velocity. A mathematical model calculates cloud transmittance based on pixel intensity, eliminating complex training steps. Results from a 30-day experimental campaign, incorporating diverse meteorological conditions, were compared against a secondary standard solarimetric station, a smart persistence model, and state-of-the-art approaches. The DNI forecast achieved an RMSE (relative error) of 46.79 W/m2 (11.99%) for 1-min intervals and 90.21 W/m2 (17.54%) for 10-min intervals, while GHI ranged from 31.73 W/m2 (4.68%) to 75.02 W/m2 (13.63%). Pearson correlation coefficients exceeded 0.9 overall, reaching 0.98 and 0.99 for the 1-min DNI and GHI forecasts, and 0.91 and 0.96 for the 10-min DNI and GHI forecasts, respectively, underscoring the system’s accuracy and robustness in complex meteorological scenarios.

Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data

Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar irradiance and temperature. This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. The simulations of three algorithms—Perturb and Observe (P&O), Artificial Neural Network (ANN), and the proposed LSTM-based MPPT—were conducted using MATLAB (2021b) and RT_LAB (24.3.3) with an OPAL-RT simulator for real-time analysis. The data used for this study were sourced from NASA/POWER’s Native Resolution Daily Data of solar irradiation and temperature specific to Imphal, Manipur, India. The obtained results demonstrate that the LSTM-based MPPT system achieves a superior power tracking accuracy under changing solar conditions, producing an average output of 74 W. In comparison, the ANN and P&O methods yield average outputs of 57 W and 62 W, respectively. This significant improvement, i.e., 20–30%, underscores the effectiveness of the LSTM technique in enhancing the power output of solar PV systems. By incorporating real-world data, valuable insights into solar power generation specific to the selected location are provided. Furthermore, the outputs of the model were verified through real-time simulations using the OPAL-RT simulator OP4510, showcasing the practical applicability of this approach in real-world scenarios.

Solar Dryers: Technical Insights and Bibliometric Trends in Energy Technologies

This review article provides a comprehensive analysis of the technical advancements and research trends in solar drying technologies for agricultural products. The study encompasses various innovations in energy storage systems, including phase change materials (PCMs) and the use of computational fluid dynamics (CFD) for optimizing the drying process. Through a bibliometric analysis of 126 scientific papers published between 1984 and 2024, five major research clusters were identified: energy generation, heat transfer, thermal storage, simulation modeling, and the integration of hybrid systems. The results demonstrate a marked increase in scientific output over the past decade, emphasizing a growing interest in the sustainable use of solar energy for drying applications. Key findings highlight that while PCM-based storage solutions significantly enhance the thermal stability of dryers, the high implementation costs and technical complexities limit their adoption, especially in small-scale operations. Similarly, CFD models have proven effective in optimizing air and temperature distribution within dryers; however, their performance is hindered by real-world fluctuations in solar radiation and humidity levels. To address these limitations, future research should focus on the development of cost-effective PCM materials and the improvement of CFD models for dynamic environmental conditions. The review concludes by emphasizing the importance of interdisciplinary collaboration in the design and application of these technologies, recommending the inclusion of real-world case studies to better illustrate the practical implications and economic benefits of solar drying technologies for agricultural production.

Accelerated surface brightening in China: The decisive role of reduced anthropogenic aerosol emissions

A profound comprehension of the key determinants behind the fluctuations in surface solar radiation (SSR) over decadal timescales is essential for advancing the fields of energy meteorology, climatology, and solar resources. While there is a general agreement among prior research on the significant impact of aerosols on SSR, a granular understanding of the specific influence that varying aerosol types exert on SSR remains elusive. This investigation delves into the respective impacts of aerosols and cloud radiative effects across China, utilizing data from the Clouds and the Earth's Radiant Energy System (CERES). It further dissects the distinct impacts of different aerosol types on the decadal-scale variations in SSR, employing spectral aerosol optical depths derived from the MODIS aerosol products. The findings reveal a predominantly positive trend in SSR, ranging from 1.5 to 2.5 W/m2/yr between the years 2011 and 2022 for the majority of China's regions. However, certain areas within Northeast and Western China exhibit a decline in SSR exceeding −1 W/m2/yr. Notably, in Eastern China, a downward trend in aerosol radiative effect is observed, with a pronounced reduction of approximately −2 W/m2/yr noted specifically during the summer in Northern China. The study infers that the reduction in urban industrial emissions and biomass burning aerosols is linked to the acceleration of the brightening phenomenon in China. Our results underscore that stringent regulation of anthropogenic emissions could yield dual benefits: environmental amelioration and an upsurge in solar resource. These insights merit serious contemplation in the formulation of future policy and decision-making frameworks.

Solar-enhanced DCMD: Dual-tank system for superior efficiency and viability

ABSTRACT This study investigates a novel solar-powered direct contact membrane distillation (DCMD) system designed to significantly enhance desalination efficiency and economic feasibility. The system features a dual-tank configuration integrated with a solar collector, which optimizes thermal energy storage and ensures consistent feedwater temperatures. Therefore, continuous operation is possible despite fluctuations in solar radiation. An assessment of the system's performance under various solar radiation conditions predicted an average daily permeate flux of 14.4 kg/m2 and a specific energy consumption of 1810 kJ/kg, indicating superior thermal efficiency. The economic analysis revealed a competitive levelized water cost of $25.75 per cubic meter, highlighting the system's potential as a viable alternative to conventional desalination technologies. These findings suggest that the proposed DCMD system is a leading technology in the pursuit of sustainable desalination. Considering its thermal management and cost-effectiveness, the system presents a transformative solution for sustainable freshwater production in regions with abundant solar resources.

Photophysiological and Oxidative Responses of the Symbiotic Estuarine Anemone Anthopleura hermaphroditica to the Impact of UV Radiation and Salinity: Field and Laboratory Approaches.

The estuarine anemone Anthopleura hermaphroditica and its symbiont Philozoon anthopleurum are continuously exposed to intense fluctuations in solar radiation and salinity owing to tidal changes. The aim of this study was to evaluate the effects of the tidal cycle, solar radiation, and salinity fluctuations on the photosynthetic and cellular responses (lipid peroxidation, total phenolic compounds, and antioxidant activity) of the symbiont complex over a 24 h period in the Quempillén River Estuary. Additionally, laboratory experiments were conducted to determine the specific photobiological responses to photosynthetically active radiation (PAR), ultraviolet radiation (UVR), and salinity. Our field results showed that the photosynthetic parameters of the symbiont complex decreased with increasing ambient radiation; however, no relationship was observed with changes in salinity. Increased peroxidative damage, total phenolic compound levels, and antioxidant activity were mainly related to increased UVR and, to a lesser extent, PAR. During the dark period, only PAR-exposed organisms returned to the basal levels of photosynthesis and cell damage. Laboratory exposure confirmed the deleterious effects of UVR on the photosynthetic response. The present study suggests that the ability of A. hermaphroditica to acclimate to natural radiation stress is mediated by the concerted action of various physiological mechanisms that occur at different times of the day, under varying levels of environmental stress.

Assessment of a LPG hybrid solar dryer assisted with smart air circulation system for drying basil leaves

The fluctuation of solar radiation throughout the day presents a significant obstacle to the widespread adoption of solar dryers for the dehydration of agricultural products, particularly those that are sensitive to high temperatures, such as basil leaf drying during the winter season. Consequently, this recent study sought to address the limitations of solar-powered dryers by implementing a hybrid drying system that harnesses both solar energy and liquid petroleum gas (LPG). Furthermore, an innovative automatic electronic unit was integrated to facilitate the circulation of air between the drying chamber and the ambient environment. Considering the solar radiation status in Egypt, an LPG hybrid solar dryer has been developed to be suitable for both sunny and cloudy weather conditions. This hybrid solar dryer (HSD) uses indirect forced convection and a controlled auxiliary heating system (LPG) to regulate both temperature and relative humidity, resulting in increased drying rates, reduced energy consumption, and the production of high-quality dried products. The HSD was tested and evaluated for drying basil leaves at three different temperatures of50, 55, and 60 °C and three air changing rates of 70, 80, and 90%, during both summer and winter sessions. The obtained results showed that drying basil at a temperature of 60 °C and an air changing rate of 90% led to a decrease in the drying time by about 35.71% and 35.56% in summer and winter, respectively, where summer drying took 135–210 min and winter drying took 145–225 min to reach equilibrium moisture content (MC). Additionally, the effective moisture diffusivity ranged from 5.25 to 9.06 × 10− 9 m2/s, where higher values of effective moisture diffusivity (EMD) were increased with increasing both drying temperatures and air change rates. Furthermore, the activation energy decreased from 16.557 to 25.182 kJ/mol to 1.945–15.366 kJ/mol for the winter and summer sessions, respectively. On the other hand, the analysis of thin-layer kinetic showed that the Modified Midilli II model has a higher coefficient of determination R2, the lowest χ2, and the lowest root mean square error (RMSE) compared to the other models of both winter and summer sessions. Finally, the LPG hybrid solar dryer can be used for drying a wide range of agricultural products, and it is more efficient for drying medicinal plants. This innovative dryer utilizes a combination of LPG and solar energy, making it efficient and environmentally friendly.

Mathematical Analysis of Novel Method to Solve Protection Issues Pertaining To Solar PV Integration

The integration of Solar Photovoltaic (PV) systems into modern power grids presents protection challenges, such as voltage fluctuations, fault detection complexities, and bidirectional power flow issues. These challenges compromise grid reliability, requiring advanced methods to address them. This study introduces a novel mathematical analysis approach to solve protection issues in solar PV integration, focusing on developing an adaptive protection scheme using advanced mathematical models. The proposed approach utilizes differential equations, optimization techniques, and machine learning algorithms to create dynamic protection settings responsive to varying grid conditions. The method integrates fault detection and classification algorithms based on wavelet transform and support vector machines (SVM), allowing for rapid and accurate identification of fault types and locations. The findings demonstrate that the proposed adaptive protection scheme significantly enhances fault detection accuracy, reducing false trip rates by over 30% compared to conventional protection systems. Moreover, the method efficiently distinguishes between transient and permanent faults, ensuring swift isolation and minimizing disruption to solar PV operations. The developed model also exhibits robustness in handling variations in solar irradiance and load fluctuations, making it suitable for real-world grid applications. This research provides a substantial contribution to the field of solar PV integration by offering a mathematically grounded, adaptive protection solution that ensures improved reliability and resilience in power systems. The proposed method paves the way for the development of more advanced protection schemes, ensuring the seamless integration of renewable energy sources into modern grids while maintaining system stability and security.

Experimental validation of a novel hybrid Equilibrium Slime Mould Optimization for solar photovoltaic system

Maximizing Power Point Tracking (MPPT) is an essential technique in photovoltaic (PV) systems that guarantees the highest potential conversion of sunlight energy under any irradiance changes. Efficient and reliable MPPT technique is a challenge faced by researchers due to factors such as fluctuations in irradiance and the presence of partial shading. This paper introduced a novel hybrid Equilibrium Slime Mould Optimization (ESMO) MPPT-based algorithm combining the advantages of two recent algorithms, Slime Mould Optimization (SMO) and Equilibrium Optimizer (EO). The ESMO algorithm is compared with highly efficient MPPT-based techniques such as SMO, EO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA), both under a Simulink environment and a real-time experimental laboratory setup using a Dspace1104 controller and PV emulator. The comparison focuses on performance under several irradiance cases, including instant irradiance change, partial shading, complex partial shading, and dynamic partial shading. The key advantage of ESMO is the fact that it has a single tunable parameter, which makes implementation much easier and, at the same time, reduces the computational resources that are required by the control system. Extensive testing proves the superiority of ESMO over all other techniques, the average efficiency of which is 99.98% under all conditions. Additionally, ESMO provides fast average tracking times of 244 ms under simulation experiments and 200 ms for real-time experiments. These results show that ESMO can be very important for future implementation in large-scale solar PV systems.

Enhanced frequency control of a hybrid microgrid using RANFIS for partially shaded photovoltaic systems under uncertainties

Nowadays environmental concerns and fossil fuel limitations, as well as economic benefits and technical issues such as reliability lead conventional distribution networks to smart microgrids, where the renewable energy resources are merged with the grids. Nevertheless, the systems face new challenges due to the intermittent nature and uncertainties in the distributed generations, which cause frequency fluctuations in the microgrid. The photovoltaic cells are the main part of the contemporary microgrids. Although the photovoltaic (PV) systems depend on solar irradiance, and temperature and are affected by the partial shading phenomenon they could contribute to improving the microgrid frequency stability with a proper control scheme. In this paper, the frequency control strategy is designed for a hybrid stand-alone microgrid, which is robust against load disturbances, variations in weather conditions, and uncertainties in the microgrid parameters. The proposed intelligent control scheme relies on the Recurrent Adaptive Neuro Fuzzy Inference System (RANFIS). The Whale Optimization Algorithm (WOA) is employed to optimize the RANFIS controller structure and generate the parameters of membership functions. In this multi-objective optimization, the objectives are settling time (ST), overshoot (Osh), and Integral Square Error (ISE). The simulation results verify the high robustness and performance of the proposed RANFIS controller, compared to other controllers, during various operational circumstances, as well as the sporadic behavior of renewable energy resources (RES) such as fluctuations in solar radiation and certain uncertainties in the microgrid parameters.

Investigating thermal and UV ageing effects on crumb rubber modified bitumen enhanced with emission reduction agents and carbon black

Over time, bitumen experiences deterioration due to factors like the exposure to oxygen, solar irradiation, and temperature fluctuations. While laboratory techniques like rolling thin film oven (RTFO) and pressure ageing vessel (PAV) were developed to simulate field ageing, they do not accurately replicate environmental factors normally occurring in the field, particularly solar irradiation. Limited research has focused on understanding the ageing processes of crumb rubber modified bitumen (CRMB). This study investigated how different ageing conditions affect the chemo-rheological properties of CRMB and CRMB with geopolymer-based fly ash (GFA), Portland cement paste (PCP), and carbon black (CB). GFA and PCP were added to reduce noxious fumes and VOC emissions from CRMB whereas CB is commonly employed as an antioxidant agent. RTFO and PAV were used to simulate short- and long-term ageing, while a SUNTEST XLS+ weatherometer was used for thermal and UV ageing simulations. A dynamic shear rheometer and attenuated total reflectance Fourier transform infrared spectroscopy were employed for rheological and chemical analysis. Results indicated that extended exposure to thermal conditioning and UV irradiation had the most impact on the chemo-rheological properties of the binders. The incorporation of CR into bitumen enhanced ageing resistance and mitigated the potential for stiffening and embrittlement. The addition of GFA, PCP, and CB to CRMB had a negligible impact on the chemo-rheological properties when subjected to RTFO, PAV, and thermal conditioning. However, following UV ageing, these additives led to a slight increase in carbonyl bonds and greater stiffness compared to pure CRMB

Joint Analysis of Solar Radiation and Wind Speed: Approach With Sliding Windows

Objective: This paper aims to characterise the daily fluctuations of the wind speed and solar radiation time series of some cities in the State of Bahia, Brazil, from January 2009 to December 2018, using the sliding window approach. Theoretical Framework: Due to its complexity and importance for life on planet Earth, climate change and its socio-environmental impacts are subject to studies by the scientific community and governmental and non-governmental institutions. Wind speed and solar radiation are among the elements that are relevant to climate change. Method: The DCCA Cross-Correlation Coefficient (ρDCCA) was applied to meet the objective. Five cities in Bahia, with different biomes, were selected for the modelling. Results and Discussion: The descriptive one with sliding windows identified a predominance of greater relative variation around the mean in the solar radiation time series and divergent signs in the asymmetry of wind speed and solar radiation. It was found in the cross-correlation estimation, via ρDCCA, positive and negative correlations varying according to the city, the size of the window (w) and the evaluated temporal scale (n). Conclusion: From the results and chosen methodology, one more proposal to jointly and dynamically characterise the climatic variables wind speed and solar radiation fluctuations is presented.

A review on free space optical communication links: 5G applications

Abstract Free space optical (FSO) communications has garnered significant attention as a potential substitute for radio frequency (RF) wireless communication in short and long range transmissions. Free-space optical (FSO) communication is a method where an optical beam is sent across an unguided channel, gaining favour in high-speed wireless networks due to its large optical bandwidth, fast data transmission, lack of licencing requirements, secure nature, and easy setup. Despite several inherent advantages that FSO has over existing radio frequency (RF) communication technologies, its link performance is severely hampered by atmospheric fog and turbulence, which not only reduce optical irradiance but also cause the modulated optical beam to deviate from its intended path, impacting the quality of the received signal. This review paper discusses FSO technology, channel impairments under varied atmospheric attenuation conditions, advantages, and applications. Various channel models in terms of probability density functions are described, which characterise the statistical nature of the irradiance fluctuations of optical transmission through scattering and atmospheric turbulence. Key objectives that need to be addressed in the near future, particularly in fifth-generation (5G) and beyond, includes enhancing capacity, boosting data rates, minimising latency, and enhancing service quality. Last but not the least, several uses of FSO that facilitates 5G and future technologies have also been covered in this study.

Performance of Low-Dimensional Solid Room-Temperature Photodetectors-Critical View.

In the last twenty years, nanofabrication progress has allowed for the emergence of a new photodetector family, generally called low-dimensional solids (LDSs), among which the most important are two-dimensional (2D) materials, perovskites, and nanowires/quantum dots. They operate in a wide wavelength range from ultraviolet to far-infrared. Current research indicates remarkable advances in increasing the performance of this new generation of photodetectors. The published performance at room temperature is even better than reported for typical photodetectors. Several articles demonstrate detectivity outperforming physical boundaries driven by background radiation and signal fluctuations. This study attempts to explain these peculiarities. In order to achieve this goal, we first clarify the fundamental differences in the photoelectric effects of the new generation of photodetectors compared to the standard designs dominating the commercial market. Photodetectors made of 2D transition metal dichalcogenides (TMDs), quantum dots, topological insulators, and perovskites are mainly considered. Their performance is compared with the fundamental limits estimated by the signal fluctuation limit (in the ultraviolet region) and the background radiation limit (in the infrared region). In the latter case, Law 19 dedicated to HgCdTe photodiodes is used as a standard reference benchmark. The causes for the performance overestimate of the different types of LDS detectors are also explained. Finally, an attempt is made to determine their place in the global market in the long term.

Technical Evaluation of a Stand-Alone Photovoltaic Heat Pump Dryer without Batteries

This paper presents the results of the technical validation of an innovative prototype for drying alfalfa bales. It is based on a 4.1 kW Heat Pump (HP) that uses an advanced technology (optimized for extracting the humidity from the air) and is directly powered by a 6.6 kWp PV generator without grid or batteries support. The main technical challenges of this work were managing solar irradiance fluctuations due to cloud-passing and achieving good drying efficiency. The prototype has been validated for two consecutive drying campaigns in La Rioja (Spain). There were no abrupt stops generated by cloud-passing. The PRPV, which evaluates the performance of the PV system only during the periods when the PV energy can be used by the HP unit, presented values of 0.82 and 0.85, comparable to a well-performing grid-connected PV system. Although the bales’ initial relative humidity (RH) ranged from 18 to 30%, all but one of them presented a final RH below 16%, which is the limit point to avoid fermentation. The drying times ranged from 1 to 5 h, and the specific energy consumption per liter of water extracted, from 0.7 to 1.46 kWh/L. These values are comparable to traditional diesel and grid-powered systems. It is worth noting that the agricultural drying market represented USD 1.7 billion in 2023.