Ultra-wideband polarization-incident angle insensitive nano-scale metamaterial absorber for solar energy harvesting and infrared detection

Assessing vehicle solar energy harvesting using GPS trajectories and street imagery

A high-efficiency inductor-less solar energy harvesting system for IoT end nodes

Iran's electricity generation relies heavily on fossil fuels, resulting in frequent power shortages and widespread blackouts in major cities. Given the high levels of solar irradiance across the country, photovoltaic (PV) and concentrating solar power (CSP) technologies could provide a sustainable alternative. Existing studies focus on specific technologies or individual regions. Currently, there is no consistent, comprehensive mapping of the scope for political decision-making in Iran. This study aims to address this issue by providing the first nationwide assessment of solar energy potential in Iran, evaluating both PV and CSP. This GIS-based assessment uses an expanded set of environmental and technical criteria and performs sensitivity analyses to ensure robust results and identify the most effective and sustainable locations for PV and CSP plants. The model incorporates specific constraints, such as protected natural areas, to exclude unsuitable sites, and assesses suitability based on criteria such as solar irradiation levels and proximity to grid infrastructure. These factors are categorised into four suitability classes, ranging from 'high' to 'very low' for both PV and CSP installations. By synthesising the constraint and suitability maps, the model identifies feasible sites and assesses their relative desirability. A sensitivity analysis, focusing on the weighting of the suitability criteria, confirms the robustness of the results. The results highlight Iran's considerable capacity for solar power generation and suggest that the country could exceed its current electricity production by a multiple through the development of solar power plants. The model applies 14 exclusion criteria, revealing that 70% of Iran’s land is unsuitable for PV and 83% for CSP. The results show that 14.5% of Iran’s land is suitable for PV and 7.5% for CSP (medium and high suitable), with central and eastern regions offering the highest potential. Additionally, the study highlights the promising prospects of GIS modeling in renewable energy siting, emphasizing improved data integration, global scalability, environmental impact assessment, and policy harmonization. • For the first time, a nationwide GIS model identifies suitable PV and CSP sites in Iran. • Suitability results remain robust across sensitivity scenarios. • Iran's solar potential could exceed its current electricity generation by a multiple.

The modern world has led to rapid growth of Electric Vehicles. The increased dependency on EV has also increased the demand for an efficient, sustainable and smart Electric vehicle charging method for it. The current status of the EV is traditional wired charging method that encounters challenges like continuous safety concerns, grid-based reliance and cable dependency sources etc. This paper has been designed to provide a solution by developing a solar energy harvesting charging station with the incorporation of Internet of Things (IoT) technology. It enables an eco-friendly solution using inductive wireless power transfer. This method reduces the risk in failure of mechanical parts operating with the help of wire connections. The big advantage of this project is that it provides real time monitoring of the system and the operational metrics. The data’s can be accessed from anywhere and at any time in remote conditions using a cloud-based interface. It enables effective energy management of the system. A working prototype of the proposed solution has been built to test in the real-world situations. The results demonstrate the viability of combining solar energy harvesters with wireless charging module simultaneously ensuring the Realtime monitoring through IoT. This approach promotes an advancement of sustainable EV smart charging infrastructure with more future innovations.

Efficient solar energy utilization demands high-absorption materials that are low-cost, easy to fabricate, and harsh-condition-resistant. Herein, the micro-pyramid graphite array (GA) is fabricated by nanosecond laser etching followed by surface modification with the antireflection layer. The optimized GA exhibits a high solar absorbance of 99.3% and good omnidirectional broadband absorption. The superblackness is ascribed to several combined effects, including: the light-trapping effect of micro-pyramid cones, the enhancement of localized electric field around submicron/nano-wrinkles, as well as the reduced interfacial reflection via a low-refractive-index polymer coating. With its superior thermal stability, the GA can convert the high-irradiance light into heat, reaching the surface equilibrium temperature of 300°C under 10-sun irradiation. The GA-enhanced thermoelectric generator significantly increases the output power density to 20mW cm-2 under 10-sun irradiation, which outperforms the reported organic ultrablack materials. The GA may be a promising photothermal converter under harsh operation conditions.

A graphene-integrated refractory metasurface absorber is proposed for broadband solar thermal energy conversion. Near-unity broadband absorptance across 300-2500nm is achieved through three concurrent mechanisms: free-space impedance matching, ground-plane-mediated transmission suppression, and multimodal electromagnetic energy dissipation distributed across spectrally coupled plasmonic and dielectric resonant channels. Spectral tunability without structural reconfiguration is demonstrated through electrostatic modulation of the graphene Fermi level, which enables reversible control of the optical sheet conductivity. Alternative material configurations incorporating caesium, gallium arsenide, copper, and strontium are evaluated through full-wave COMSOL Multiphysics simulations under periodic boundary conditions, with assessment of spectral bandwidth, resonance behaviour, and thermal robustness at elevated temperatures. A dielectric substrate thickness of approximately 4.1μm satisfies the quarter-wavelength Fabry-Perot cavity resonance condition, suppressing mid-infrared radiative emission and reducing parasitic thermal losses. A random forest regression surrogate model trained on 1,200 finite-element simulation samples, with five geometric and material input parameters and 500 estimators, maps the design space with R2 > 0.90 across most parameter configurations. Accuracy decreases to R2 approximately 0.63 near normal incidence, where overlapping resonances increase spectral complexity. The optimised configuration achieves a peak absorptance of 99.99% and a broadband solar-weighted average of 98.6%.

Abstract The increasing energy demand necessitates efficient renewable energy solutions. This necessity highlights solar energy as the most reliable and abundant source. Since material design is very important for obtaining efficient energy from the sun, metamaterial absorbers are of interest. Metamaterials, which are characterized by their negative refractive index and electromagnetic manipulation capabilities, are preferred in solar absorber designs due to their high efficiency, low cost, and ease of fabrication. The characteristic properties of metamaterial absorbers can be obtained by optimizing the parameters in their geometric structures. However, optimizing these structures requires electromagnetic simulations involving a large parameter space. This situation results in high computational costs that require high-performance computing (HPC) resources and parallel processing. To overcome this challenge, computational steps can be significantly reduced by integrating artificial intelligence tools into optimization processes. In this study, a metamaterial absorber operating at ultraviolet, visible light, and infrared wavelengths is designed, and its solar energy collection efficiency is investigated. Then, a deep learning-based optimization approach is presented for the proposed solar energy absorber. The approach proposed in the study is particularly suitable for HPC-supported environments where large-scale simulations can be generated and processed using parallel computing. With the proposed deep neural network optimization model, geometric parameters are suggested for the maximum absorption value to be obtained from the solar absorber. Thanks to this state-of-the-art deep learning-based optimization model, the number of simulations has been reduced by approximately 90%, decreasing from 1256 to 126. Therefore, the approach proposed in the study significantly reduces the computational workload and reveals the potential of combining deep learning methods with HPC infrastructures in accelerating electromagnetic optimization problems.

ABSTRACT The transition to sustainable agriculture requires technologies that simultaneously enhance crop yields and reduce environmental impacts. Solar‐driven nitrate valorization, when coupled with CO 2 capture from industrial flue gas, presents a promising dual strategy for producing high‐value fertilizers while mitigating carbon emissions. However, its practical implementation is hindered by two interrelated challenges: (i) the intermittent nature of solar irradiation and (ii) the competitive hydrogen evolution reaction (HER), which severely compromises Faradaic efficiency (FE) of desired nitrogenous products. Here, we address these challenges by designing a heterogeneous CuPd electrocatalyst featuring an amorphous/crystalline heterojunction. This catalyst suppresses HER across a broad potential window (−0.4 to −1.4 V), maintaining >80% FE(ammonia) for >100 h. The catalytic robustness enables stable solar‐powered electrolysis even under low irradiation (0.4 sun), achieving >70% FE(ammonia) and 6% solar‐to‐fuel conversion efficiency, while catholyte simultaneously captures CO 2 at a rate of 6–20 mg h −1 . Techno‐economic analysis demonstrates cost competitiveness against biological counterparts. When applied to plant cultivation, this artificial photosynthesis system boosts solar‐to‐biomass conversion efficiency by 3.5‐fold compared to natural photosynthesis. By unifying solar energy harvesting, waste nitrate reduction, and carbon sequestration, our work provides a scalable blueprint for a closed‐loop agrochemical ecosystem and advanced catalyst design for intermittent renewable‐powered electrosynthesis.

ABSTRACT The transition to sustainable agriculture requires technologies that simultaneously enhance crop yields and reduce environmental impacts. Solar‐driven nitrate valorization, when coupled with CO 2 capture from industrial flue gas, presents a promising dual strategy for producing high‐value fertilizers while mitigating carbon emissions. However, its practical implementation is hindered by two interrelated challenges: (i) the intermittent nature of solar irradiation and (ii) the competitive hydrogen evolution reaction (HER), which severely compromises Faradaic efficiency (FE) of desired nitrogenous products. Here, we address these challenges by designing a heterogeneous CuPd electrocatalyst featuring an amorphous/crystalline heterojunction. This catalyst suppresses HER across a broad potential window (−0.4 to −1.4 V), maintaining >80% FE(ammonia) for >100 h. The catalytic robustness enables stable solar‐powered electrolysis even under low irradiation (0.4 sun), achieving >70% FE(ammonia) and 6% solar‐to‐fuel conversion efficiency, while catholyte simultaneously captures CO 2 at a rate of 6–20 mg h −1 . Techno‐economic analysis demonstrates cost competitiveness against biological counterparts. When applied to plant cultivation, this artificial photosynthesis system boosts solar‐to‐biomass conversion efficiency by 3.5‐fold compared to natural photosynthesis. By unifying solar energy harvesting, waste nitrate reduction, and carbon sequestration, our work provides a scalable blueprint for a closed‐loop agrochemical ecosystem and advanced catalyst design for intermittent renewable‐powered electrosynthesis.

Triplet-triplet annihilation upconversion (TTA-UC) is an anti-Stokes process that converts low-energy photons into high-energy photons, holding significant promise for applications in solar energy harvesting, photocatalysis, and bioimaging. However, the main operating environment for efficient TTA-UC is an oxygen-free solution, which severely limits its practical applications. Recently, porous framework materials, such as metal-organic frameworks (MOFs) and porous aromatic frameworks (PAFs), have emerged as promising platforms due to their diverse functional and structural characteristics. These materials provide promising platforms for developing efficient, oxygen-resistant, solid-state TTA-UC systems. In this review, we first outline the key parameters and status of porous framework-based TTA-UC materials, providing a detailed analysis of the intrinsic relationship between structure and performance. We then summarize recent advances in this class of materials and highlight their applications in bioimaging, sensing, and photocatalysis. Finally, we discuss the prevailing challenges and propose prospective solutions.

Study of a road thermal collector prototype: mathematical model, numerical simulations and experimental validation

Comparative study of solar radiation estimation models based on sunshine duration and irradiance measurements in the Tigray Region, Ethiopia

Shape-stabilized phase change materials prepared via mercerization of self-entangled cellulose nanofibrils.

Halogen-induced electronic and structural modulation in Cs₂NaTlBr₃Cl₃ double perovskites for high-efficiency solar energy harvesting

Photocatalytic CO2 reduction (CO2RR) is particularly attractive due to its ability to directly harvest solar energy, representing a promising and sustainable route toward carbon neutrality. All-inorganic halide perovskite nanocrystals, such as CsPbBr3, have emerged as highly promising photocatalysts owing to their exceptional electronic and optical properties. In this work, Cu-atom-doped CsPbBr3 perovskite nanocrystals (Cu-CsPbBr3 PNCs) were successfully developed, and the role of the Cu single atom in CsPbBr3 PNCs for modulating their photocatalytic CO2RR was elucidated. Comprehensive structural characterizations, including X-ray diffraction (XRD), high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), and X-ray absorption spectroscopy (XAS), collectively confirm that the Cu element is atomically distributed as single atoms uniformly within the nanocrystal lattice rather than undergoing phase segregation into clusters or long-range-ordered Cu phases. A slight red shift of the Cu K-edge XANES under illumination was found, indicating a partial reduction of Cu2+ in Cu-CsPbBr3 PNCs. The results represented the occurrence of photoexcited electron transfer from CsPbBr3 to the doped Cu sites during the CO2RR. Moreover, photocatalytic CO2RR revealed that an optimal Cu-doping concentration in Cu-CsPbBr3 PNCs achieves a significantly enhanced CO production rate of 2.80 μmol g-1 h-1, outperforming pristine CsPbBr3 (1.47 μmol g-1 h-1). Time-resolved photoluminescence (TRPL) measurements show a substantial decrease in carrier lifetime from 3.17 ns (pristine CsPbBr3 PNCs) to 0.72 ns (optimal Cu-CsPbBr3 PNCs), evidencing efficient electron trapping by single Cu single atoms. Transient absorption (TA) spectra further reveal modified hot-carrier dynamics and pronounced carrier-trapping behavior. Overall, the enhanced photocatalytic activity of Cu-CsPbBr3 PNCs toward CO2 reduction is attributed to efficient nonradiative charge transfer from photoexcited CsPbBr3 PNCs to the dopant Cu atoms. This design strategy opens a general avenue for the development of metal-atom-doped perovskite-based photocatalysts with improved efficiency for the CO2 reduction reaction.

Solar energy harvesters can experience intermittent output due to rainfall, cloud cover, and rapid temperature variations. In this work, we present an experimental demonstration of a hybrid energy harvester that integrates single-crystal lithium niobate (LiNbO3) piezoelectric/pyroelectric devices with a silicon solar cell to enable multisource energy capture under variable weather conditions. Interdigital transducers (IDTs) patterned on LiNbO3 convert raindrop impacts and temperature swings into electrical signals through the piezoelectric effect, while the silicon cell produces photovoltaic power. Under controlled testing, the hybrid configuration increased photovoltaic output by 22.6% compared with the standalone solar cell. It improved the raindrop-induced peak-to-peak voltage by 8.3% relative to the standalone piezoelectric component. The LiNbO3 device generated 0.8 Vpp from a single raindrop and 7.54 Vpp at approximately 70 °C through pyroelectric conversion. Device durability was supported by extended cycling with ≤1% drift in Vpp and by post-test microscopy, which confirmed intact electrodes and a clean LiNbO3-metal interface. Overall, these findings demonstrate a robust and straightforward pathway toward weather-resilient hybrid energy modules that mitigate solar intermittency by harvesting light, mechanical rain energy, and thermal fluctuations within a single integrated stack. Overall, the demonstrated hybrid device delivers stable multisource energy harvesting with high repeatability and strong structural integrity after extended raindrop and thermal cycling.

Metal nanoclusters (MNCs) are an emerging class of atomically precise nanomaterials with sizes comparable to the Fermi wavelength of free electrons, exhibiting discrete energy levels, molecular-like behaviors, and tunable physicochemical properties. Among these properties, photothermal conversion-the process of transforming absorbed light into thermal energy-has garnered considerable interest due to its vital importance in applications such as solar energy harvesting, photothermal therapy, and catalysis. This review begins by summarizing recent progress in synthetic strategies for MNCs, including kinetic control, seeded growth, in situ two-phase ligand exchange, and metal exchange, which help overcome challenges such as polydispersity, low yield, restricted surface functionality, and lengthy synthesis times. Subsequently, a comprehensive analysis is provided on the photothermal conversion behaviors of various MNC systems (e.g., coinage metal nanoclusters, Ti NCs, and Mo NCs) reported in the past five years, with in-depth discussion of their structural characteristics, absorption properties, photothermal conversion efficiencies, and underlying conversion mechanisms. Finally, the review addresses current challenges and prospects for advancing MNC-based photothermal technologies via atomic-level engineering and interdisciplinary approaches. Through this in-depth and systematic review, we endeavor to provide scholars dedicated to metal nanocluster research-as well as experts engaged in photothermal conversion and its diverse applications-with valuable scientific insights. We are confident that this contribution will not only catalyze innovative breakthroughs but also unlock exciting new frontiers within this vibrant and rapidly evolving field of study.

Singlet fission (SF) provides a promising strategy for surpassing the Shockley-Queisser limit in photovoltaics, thereby enabling high-efficiency, sustainable solar energy harvesting. However, the identification of efficient SF materials is hindered by the limited availability of suitable molecular candidates and the high computational costs associated with conventional quantum-chemical methods for excited states. In this study, we introduce a high-throughput screening framework that integrates a graph neural network (GNN) with multi-level validation to accelerate the discovery of promising SF candidates. Trained on a previously reported FORMED database, the GNN yields highly accurate predictions for SF-relevant excited-state properties, demonstrating a mean absolute error of about 0.1eV for S1, T1, and T2 excitation energies. This capability facilitates the efficient screening of over 20 million molecular structures from both OE62 and QO2Mol databases. Our framework significantly reduces the computational demand associated with time-dependent density functional theory validation and identifies 180 potential SF molecules along with more than 1000 conformers. Subsequent assessments regarding synthetic accessibility, GW approximation and Bethe-Salpeter equation calculations further highlight a subset of experimentally feasible candidates among these SF candidates. The present approach exemplifies an effective, AI-driven strategy for accelerating the discovery of functional materials for sustainable optoelectronic application.

The ability to convert low-energy photons to those of higher energy through triplet-triplet annihilation upconversion (TTA-UC) is of significant interest for diverse applications including solar energy harvesting, sensing, and anticounterfeiting. However, the efficiency of TTA-UC under ambient conditions is often severely limited due to the quenching of excited triplet states by molecular oxygen. Therefore, when designing TTA-UC systems, it is crucial to effectively characterize the extent of oxygen quenching and to use these insights to drive material design that effectively prevents the permeation of oxygen. In this work, we investigate the oxygen barrier properties of three organic-inorganic hybrid polymers known as ureasils, previously determined to be effective TTA-UC hosts under ambient conditions. Through both direct oxygen permeation measurements and kinetic analysis of the phosphorescence quenching of palladium-(II) octaethylporphyrin (PdOEP), we investigate how the ureasil structure, particularly with respect to silica content and molecular weight and branching of the polymer chains, affects the bulk and local oxygen permeabilities. We also demonstrate the interplay of oxygen quenching with the wider TTA-UC process, confirming that the variation in oxygen permeability is the primary factor affecting ambient TTA-UC efficiency between different ureasil structures. This emphasizes the importance of considering oxygen barrier properties as a key metric in the design of future host materials for more efficient ambient TTA-UC systems.