Precise photovoltaic (PV) performance modeling is essential for optimizing system design, operational monitoring, and reliable power forecasting—yet spectral correction is often overlooked, despite its significant impact on energy yield uncertainty. This study employs the FARMS-NIT model to assess the impact of spectral irradiance on eight PV technologies across 79 European sites, grouped by Köppen–Geiger climate classification. Unlike previous studies limited to clear-sky or single-site analysis, this work integrates satellite-derived spectral data for both all-sky and clear-sky scenarios, enabling hourly, tilt-optimized simulations that reflect real-world operating conditions. Spectral analyses reveal European climates exhibit blue-shifted spectra versus AM1.5 reference, only 2–5% resembling standard conditions. Thin-film technologies demonstrate superior spectral gains under all-sky conditions, though the underlying drivers vary significantly across climatic regions—a distinction that becomes particularly evident in the clear-sky analysis. Crystalline silicon exhibits minimal spectral sensitivity (<1.6% variations), with PERC/PERT providing highest stability. CZTSSe shows latitude-dependent performance with ≤0.7% variation: small gains at high latitudes and losses at low latitudes. Atmospheric parameters were analyzed in detail, revealing that air mass (AM), clearness index (K_t), precipitable water (W), and aerosol optical depth (AOD) play key roles in shaping spectral effects, with different parameters dominating in distinct climate groups.

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

We present a study on amorphous SiO/SiO₂ superlattice formation on Si substrate held at room temperature and annealed in the temperature range 600–1100 °C. Grazing-incidence small-angle X-ray scattering (GISAXS) and X-ray reflectivity were used to study such samples. Amorphous SiO/SiO₂ superlattices were prepared by high vacuum physical vapor deposition of 4 nm thin films of SiO and SiO₂ (10 layers each) from corresponding targets on silicon substrate. Rotation of the Si substrate during evaporation ensured homogeneity of the films over the whole substrate. We observed that the inhomogeneities introduced into the SiO and SiO₂ layers during the deposition (evaporation) give rise to small angle scattering at lower annealing temperatures. After an initial SiO layer thickness reduction for 600 °C annealing, these thicknesses remain virtually unchanged up to 1000 °C, where they start to decrease again which leads to particle formation. Nevertheless, this compacting at low temperatures may lead to the seed formation in SiO layers that will facilitate later Si nanoparticles growth.