University of Valencia

About: University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Population & Neutrino. The organization has 27096 authors who have published 65669 publications receiving 1765689 citations. The organization is also known as: Universitat de València & UV.

Efficient Monolithic Perovskite/Perovskite Tandem Solar Cells

Abstract: Efficient monolithic perovskite/perovskite tandem solar cells are fabricated using two perovskite absorbers with complementary bandgaps. By employing doped organic semiconductors, an efficient and selective extraction of the charge carriers is ensured. This study demonstrates perovskite/perovskite tandem cells delivering a maximum efficiency of 18%, highlighting the potential of vacuum-deposited multilayer structures in overcoming the efficiency of single-junction perovskite devices.

Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison

Abstract: Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC), collected at the agricultural site of Barrax (Spain), was used to evaluate different retrieval methods on their ability to estimate leaf area index (LAI). With regard to parametric methods, all possible band combinations for several two-band and three-band index formulations and a linear regression fitting function have been evaluated. From a set of over ten thousand indices evaluated, the best performing one was an optimized three-band combination according to ( ρ 560 - ρ 1610 - ρ 2190 ) / ( ρ 560 + ρ 1610 + ρ 2190 ) with a 10-fold cross-validation R CV 2 of 0.82 ( RMSE CV : 0.62). This family of methods excel for their fast processing speed, e.g., 0.05 s to calibrate and validate the regression function, and 3.8 s to map a simulated S2 image. With regard to non-parametric methods, 11 machine learning regression algorithms (MLRAs) have been evaluated. This methodological family has the advantage of making use of the full optical spectrum as well as flexible, nonlinear fitting. Particularly kernel-based MLRAs lead to excellent results, with variational heteroscedastic (VH) Gaussian Processes regression (GPR) as the best performing method, with a R CV 2 of 0.90 ( RMSE CV : 0.44). Additionally, the model is trained and validated relatively fast (1.70 s) and the processed image (taking 73.88 s) includes associated uncertainty estimates. More challenging is the inversion of a PROSAIL based radiative transfer model (RTM). After the generation of a look-up table (LUT), a multitude of cost functions and regularization options were evaluated. The best performing cost function is Pearson’s χ -square. It led to a R 2 of 0.74 ( RMSE : 0.80) against the validation dataset. While its validation went fast (0.33 s), due to a per-pixel LUT solving using a cost function, image processing took considerably more time (01:01:47). Summarizing, when it comes to accurate and sufficiently fast processing of imagery to generate vegetation attributes, this paper concludes that the family of kernel-based MLRAs (e.g. GPR) is the most promising processing approach.

Probing Novel Scalar and Tensor Interactions from (Ultra)Cold Neutrons to the LHC

Abstract: Scalar and tensor interactions were once competitors to the now well-established V A structure of the Standard Model weak interactions. We revisit these interactions and survey constraints from low-energy probes (neutron, nuclear, and pion decays) as well as collider searches. Currently, the most stringent limit on scalar and tensor interactions arise from 0 + ! 0 + nuclear decays and the radiative pion decay ! e , respectively. For the future, we