Photocatalysis and micro-electrolysis featured floatable solar-driven interfacial purifier toward phenolic wastewater.

High tunnels are widely used by growers in the United States to extend the growing season, but their low-cost design often lacks temperature monitoring and automated ventilation. Consequently, crops can experience rapid increases in air temperature that may lead to heat stress or damage. Accurate 1-hour temperature forecasts can support timely manual ventilation and reduce crop risk. We evaluated the reuse of a machine learning (ML) artificial neural network (ANN) architecture, developed originally for solar radiation forecasting, for predicting internal high-tunnel temperatures. Three weather data sources were tested as model inputs: local weather station data, the National Oceanic and Atmospheric Administration’s High-Resolution Rapid Refresh (HRRR) forecasts, and HRRR data enhanced with solar radiation predictions from a previously developed ML-based solar radiation forecasting model. Models were trained using high-tunnel and weather data from Apr and Oct 2024 at two solar radiation thresholds (> 400 W·m –2 and > 100 W·m –2 ) tested on both the same 2024 (training-year) data and future data from Mar 2025 to assess model generalization. Results showed that for the training-year data, the enhanced HRRR feature group provided the most useful forecasts, particularly for locations without local weather data. Expanding training data to include data from > 100 W·m –2 broadened the model’s operating range, but sometimes reduced accuracy within 500 to 700 W·m –2 . When applied to future (2025) data, model performance degraded substantially; however, removing the date and time variables from the input features improved results, though they were still less than training-year accuracy. Models retrained using combined 2024 and 2025 datasets performed notably better, especially when trained with a solar radiation threshold > 100 W·m –2 , outperforming those trained solely on 2024 data. These findings demonstrate that the ANN structure can be repurposed effectively for high-tunnel temperature forecasting. They also underscore the importance of training data quality, feature (input variable) selection, and generalization strategy for reliable, real-world agricultural applications, where early temperature warnings can help growers minimize crop losses and improve management decisions. Looking forward, continuous learning approaches, such as retraining with new data or updating key input features such as solar radiation forecasts, may help sustain model performance as environmental conditions and tunnel characteristics evolve.

This dataset presents experimental data on the performance of a photovoltaic (PV) solar-powered water pumping system installed in a coffee plantation in Chiang Mai province, Thailand. The system performance was evaluated through controlled experiments using response surface methodology (RSM). Three independent variables were systematically varied: solar irradiance (300-900 W/m²), panel inclination (15-35°), and panel surface temperature (30-60°C). A total of 15 experimental runs were conducted, and the pumping efficiency (%) was recorded under each condition. Statistical analyses, including analysis of variance (ANOVA) and regression modeling, were applied to evaluate the effects of the individual variables and their interactions on system performance. The dataset includes raw and processed measurements, regression coefficients, and response surface parameters, enabling replication and further analysis. Perturbation plots, 3D surface plots, and contour plots provide detailed visualizations of the relationships between environmental factors and system efficiency. The optimal operating conditions were identified at a solar irradiance of 600 W/m², a panel inclination of 25°, and a panel surface temperature of 45°C, corresponding to a predicted maximum efficiency of 76.3-77.0%. This dataset can be reused for designing optimized solar water pumping systems, validating predictive models, and comparing system performance under different environmental conditions or geographic locations. It also serves as a reference for researchers in renewable energy system optimization and agricultural water management. The data provide high-resolution, experimentally validated information on the combined effects of solar irradiance, panel inclination, and panel surface temperature on PV water pumping efficiency. Unlike previous studies, it includes detailed quantitative analysis specific to coffee-growing regions in Northern Thailand, along with regression models and visualizations that can guide both experimental replication and predictive modeling under similar climatic and agricultural conditions.

UniEload: Electrical load dataset for energy forecasting applications at public universities in Bangladesh.

Synergistic in-situ structural modification and interfacial engineering of lignin toward multifunctional biodegradable films.

Corrigendum to “Techno-economic analysis of PV-assisted alkaline water electrolysis hydrogen production system based on dynamic matching of electricity prices and solar irradiance”. [Sol. Energy 307 (2026) 114314

Efficient photocatalytic degradation of Congo Red dye under solar irradiation using a novel ternary nanocomposite CaFe2O4/ZIF-67@ZIF-8

Characterization and in silico toxicity assessment of polypropylene photodegradation leachates in a simulated marine environment.

Geometric nonlinear deformation analysis for heterogeneous solar sail membrane with creases under solar radiation pressure

Heavier sediment pollution by per- and polyfluoroalkyl substances (PFASs) in tropical coasts compared to temperate regions: An overlooked hotspot.

Solar radiation pressure-based orbit control for small-body missions

Light-driven switchable polyacrylamide/chitosan-based hydrogel dressings for outdoor wound temperature regulation and enhanced skin regeneration.

Novel VMD-Informer-LSTM architecture for solar radiation forecasting with signal decomposition and sparse attention for smart energy applications

The selection of sustainable and effective covering systems is essential in greenhouses to ensure optimal insulation, microclimate control, energy management, and proper solar irradiance filtration for plant growth. This study evaluated representative types of greenhouse coverings: uncoated clear glass, Low-Emissivity (Low-E) coated glass, Ultraviolet (UV)-blocking coated glass, polyethylene (PE), polycarbonate (PC), anti-condensation polyethylene (PEAC), and infrared reflective plus anti-condensation polyethylene (IRAC). In our examination and comparisons, we focused on assessing their thermal and optical properties and associated energy performance through simulations across various climate zones using WINDOW, OPTICS, and EnergyPlus. In particular, this study used a full-glazing greenhouse and concluded an estimating model with interior lighting, heating and cooling energy consumption, and total energy. Low-E coated covering systems had the best U-factor among all materials and achieved an average of 15.9% reduction in total energy consumption without much suffering on interior lighting. Meanwhile, both Low-E and UV-coated glass offered desirable lighting quality in the red wavelength range, enhancing metrics such as the red-to-blue (R:B) and red-to-far-red (R:RF) ratios. Additionally, the performance of Low-E deteriorated when condensation formed, highlighting the importance of selecting materials with anti-condensation properties in certain areas where nighttime temperature significantly drops. Additionally, the findings indicated that UV coatings were not optimal for cloudy areas due to their insufficient daylight transmittance. It necessitates increased use of interior lighting to compensate for the lack of natural light. Overall, this research underscores the critical balance between energy and light performance necessary for optimal plant growth. This work aimed to guide the choice of covering systems for greenhouses. • Insights on greenhouse materials: optical for light, thermal for energy performance. • Low-E coatings save energy but are vulnerable to condensation in greenhouses. • UV coating boosts red light but needs more energy in cold climates. • Clear glass with high PAR transmittance has overheating issues in summer. • Anti-condensation additives reduce U-factor increases from condensation.

WHNet: A dual-branch Wavelet–Highlight network for solar irradiance forecasting

Stable methane dry reforming under fluctuating solar flux assisted by phase change thermal energy storage

This paper presents the results of a study of ZnO-Zn conglomerates synthesised on unpolished silicon (Si) substrates by the rapid solar evaporation method of ZnO/C precursors with mass ratios of 2:1 and 1:3. The obtained samples were analysed using X-ray structural analysis, which confirmed the formation of a wurtzite-type ZnO crystalline phase and metallic Zn. Photoluminescence spectra revealed two characteristic peaks: a near band edge emission at ~380 nm, caused by exciton recombination, and an intense green band at ~522 nm associated with oxygen vacancies. Raman spectroscopy revealed a shift in the E? (high) mode, which characterizes the oscillation of the oxygen sublattice, from 437 to 427 cm–1, indicating an increase in the lattice period because of elastic stresses in the crystallites. Energy dispersive analysis showed the distribution of grown ZnO and Zn conglomerates. The photocatalytic properties, which were studied by degrading the model dye methyl orange under UV irradiation, showed better values in the case of ZnO-Zn conglomerate synthesis at a precursor concentration of ZnO/C as 1:3. The results obtained demonstrate the promise of using concentrated solar radiation for the synthesis and creation of materials with the desired properties using environmentally friendly, energy-independent technologies.

Aerosol particles that contain brown carbon (BrC) chromophores will absorb solar radiation, with important implications for their effects in the environment. Although UV/vis spectroscopy of bulk BrC samples can be used to characterize absorption spectra, these methods can be of limited use when applied to aerosol particles due to high chromophore concentrations and the formation of metastable supersaturated or supercooled states in atmospheric particles. Here, we report a method to characterize the absorption spectra of levitated aqueous particles in a linear-quadrupole electrodynamic balance using broadband light scattering, allowing the wavelength dependence of the imaginary component of the refractive index, k, to be determined from a single spectrum. We show that a non-absorbing particle is an effective reference for the illuminating light and provides a reliable indication of the intensity variation across the measured wavelength range. This allows spectra from sample particles containing light-absorbing components to be referenced and normalized in a way that decouples the processed spectrum from any artifacts introduced by the LED spectrum and optical setup. Using this approach, we explore the pH-dependent absorption spectra of 4-nitrocatechol particles under a range of relative humidity (RH) conditions. We demonstrate sensitivity to k in the range 0.0001 to 0.01 and compare our results to predictions using bulk UV/vis data. This method allows the characterization of light absorption at much higher chromophore concentrations than traditional UV/vis measurements, enabling a more direct comparison to atmospheric aerosol particles.

In this work, we extend the 1D multi-scale hybrid model of plant growth introduced by Bessonov and Volpert, which describes plant elongation driven by nutrient uptake from the soil but does not account for environmental effects such as temperature and solar radiation on growth or branching. We aim to generalize this framework to describe plant development under dynamic environmental conditions and soil nutrients while allowing the emergence of lateral branches. To this end, we build on the branching mechanism regulated by the interacting dynamics of auxin and cytokinin in the Bessonov-Volpert model, and we introduce environmental influence through an effective time variable based on Effective Day Degrees, which integrates temperature and solar radiation. This modification leads to a growth velocity that is no longer constant, as originally assumed in the Bessonov Volpert model, but depends explicitly on environmental fluctuations. The resulting model couples local hormonal signaling with nutrient-dependent growth and environmentally driven constraints. Numerical simulations illustrate how variations in soil nutrient availability and environmental conditions shape branching patterns and overall plant architecture. This extended formulation provides a mathematically consistent and biologically grounded framework for analyzing adaptive plant growth in dynamic environments.

ABSTRACT Amid global efforts to address climate change and ensure energy security, renewable energy’s large-scale development is key to energy transition. However, grid-connected photovoltaic power generation has volatile output due to dynamic weather, endangering grid stability. Thus, improving solar radiation prediction accuracy and generalization is a research focus. This paper proposes a physics-informed neural network (PINN) for solar irradiance prediction, embedding a physical loss function into traditional neural networks to boost performance. Taking Tianjin Port as the study area, it calculates solar azimuth and altitude via geometric principles, combines them with measured meteorological data as PINN inputs, and uses the physical coupling between solar irradiance and surface temperature as constraints to build the model. The results show that the PINN outperforms DNN and BPNN in terms of R2 on both the training set (0.974) and the test set (0.972) (its performance on the validation set is slightly weaker but still acceptable). On the test set, it achieves a MAPE of 4.01%, an MAE of 28.41, and an RMSE of 45.79, with a higher proportion of relative errors falling within the [−10%, 10%] range. Moreover, it maintains the highest R2 in cross-year seasonal tests, demonstrating its enhanced generalization capability.