Santa Ana winds are extreme meteorological events that strongly affect the U.S.–Mexico border region, often associated with droughts, high fire risk, and hydrological imbalance. Understanding the temporal behavior of key atmospheric variables during these events is crucial for integrated water resource management in semi-arid regions such as the Guadalupe Basin in northern Baja California. In this study, we explored the predictive capability of several hybrid deep learning architectures—Long Short-Term Memory (LSTM), Convolutional Neural Network combined with LSTM (CNN–LSTM), and Bidirectional LSTM with Attention (BiLSTM–Attention)—to model the temporal evolution of wind speed, wind direction, temperature, relative humidity, and atmospheric pressure using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data from 1980 to 2020. Model performance was evaluated using RMSE, MAE, and R² metrics and compared against persistence and climatology baselines. The BiLSTM–Attention model achieved the best overall performance, showing particularly high accuracy for temperature (R² = 0.95) and relative humidity (R² = 0.76), while maintaining angular errors below 35° for wind direction. The results demonstrate the potential of hybrid deep learning models to capture nonlinear temporal dependencies in meteorological time series and provide a methodological framework to enhance hydrometeorological understanding and water resource management in the Guadalupe Basin under Santa Ana wind conditions.

Zinc oxide (ZnO) is a semiconductor with photocatalytic activity, although it presents limitations due to its band gap and the rapid recombination of the electron–hole pair; therefore, strategies such as doping have been explored. In this work, ZnO nanoparticles doped with 3% and 5% silver (Ag) were synthesized using a Cylindropuntia cholla root extract as a reducing and stabilizing agent. The structural, chemical, and optical properties of the synthesized nanoparticles were investigated using Fourier Transform Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD), Cathodoluminescence (CL), X-ray Photoelectron Spectroscopy (XPS), and Energy-Dispersive X-ray Spectroscopy (EDS). FT-IR shows that the nanoparticles have peaks between 400 cm⁻¹ and 406 cm⁻¹, attributed to the Zn–O bond. XRD characterization confirmed the formation of the wurtzite crystalline phase of ZnO, as well as the cubic phase of Ag. CL reveals two peaks: one attributed to the ultraviolet (UV) region and another in the visible region, which is associated with defects in the lattice. XPS and EDS confirm the presence of Zn, O, and Ag in the samples. The degradation of methylene blue was 90.9%, 96.4%, and 97.0% for ZnO, 3AgZnO, and 5AgZnO, respectively, demonstrating an improvement in dye degradation efficiency when doping ZnO nanoparticles with Ag.

This study systematically investigates the non-monotonic influence of the electrospinning temperature (TE) on the properties of high-quality carbon nanofibers (CNFs) derived from a polyacrylonitrile (PAN)/dimethyl sulfoxide (DMSO) system. This work addresses a gap in understanding how initial processing conditions with a high-boiling-point green solvent (DMSO) dictate final CNF structure. PAN precursor fibers were fabricated via electrospinning at TE of 90, 95, 100, and 105 °C, followed by standard two-step thermal treatment (stabilization at 250 °C, carbonization at 1100 °C). A comprehensive analysis was performed using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Raman spectroscopy, and X-ray Photoelectron Spectroscopy (XPS). These studies identified a critical processing window at 100°C that yielded superior CNFs. These optimal CNFs were uniform and fracture-free (mean diameter: 302 nm), exhibiting an optimal balance of properties, including a highly ordered graphitic structure (Lc = 1.22 nm) and a low defect ratio (I _D /I _G = 0.92), representing the best compromise between structural order and morphological integrity. In contrast, lower TE (90 °C) caused fiber fracture during carbonization due to the presence of trapped residual DMSO. In contrast, higher TE (105 °C) promoted premature solvent evaporation and ‘skinning,’ resulting in significant structural defects (I _D /I _G = 1.20). A novel finding from XPS analysis confirms that TE≥100 °C initiates surface oxidation on the precursor fibers. These findings establish a clear, scalable protocol and demonstrate the key role of electrospinning temperature in modulating solvent evaporation dynamics and influencing early-stage chemical transformations. This control is crucial when using high-boiling-point solvents such as DMSO, enabling the rational design of CNFs for tailored applications (e.g., structural versus high-surface-reactivity materials).