Photonic molecules for improving the optical response of macroporous silicon photonic crystals for gas sensing purposes

In this paper, we report the benefits of working with photonic molecules in macroporous silicon photonic crystals. In particular, we theoretically and experimentally demonstrate that the optical properties of a resonant peak produced by a single photonic atom of 2.6 µm wide can be sequentially improved if a second and a third cavity of the same length are introduced in the structure. As a consequence of that, the base of the peak is reduced from 500 nm to 100 nm, while its amplitude remains constant, increasing its Q-factor from its initial value of 25 up to 175. In addition, the bandgap is enlarged almost twice and the noise within it is mostly eliminated. In this study we also provide a way of reducing the amplitude of one or two peaks, depending whether we are in the two- or three-cavity case, by modifying the length of the involved photonic molecules so that the remainder can be used to measure gas by spectroscopic methods.Postprint (published version

Study of resonant modes in a 700 nm pitch macroporous silicon photonic crystal

In this study the modes produced by a defect inserted in a macroporous silicon (MP) photonic crystal (PC) have been studied theoretical and experimentally. In particular, the transmitted and reflected spectra have been analyzed for variations in the defect’s length and width. The performed simulations show that the resonant frequency is more easily adjusted for the fabricated samples by length tuning rather than width. The optimum resonance peak results when centered in the PC bandgap. The changes in the defect geometry result in small variations of the optical response of the PC. The resonance frequency is most sensitive to length variations, while the mode linewidth shows greater change with the defect width variation. Several MPS photonic crystals were fabricated by the electrochemical etching (EE) process with optical response in the range of 5.8 µm to 6.5 µm. Results of the characterization are in good agreement with simulations. Further samples were fabricated consisting of ordered modulated pores with a pitch of 700 nm. This allowed to reduce the vertical periodicity and therefore to have the optical response in the range of 4.4 µm to 4.8 µm. To our knowledge, modes working in this range of wavelengths have not been previously reported in 3-d MPS structures. Experimental results match with simulations, showing a linear relationship between the defect’s length and working frequency inside the bandgap. We demonstrate the possibility of tailoring the resonance peak in both ranges of wavelengths, where the principal absorption lines of different gases in the mid infrared are placed. This makes these structures very promising for their application to compact gas sensors.Postprint (author's final draft

Transmission and thermal emission in the NO2 and CO absorption lines using macroporous silicon photonic crystals with 700 Nm pitch

Postprint (author's final draft

Reassessing statistical downscaling techniques for their robust application under climate change conditions

The performance of statistical downscaling (SD) techniques is critically reassessed with respect to their robust applicability in climate change studies. To this end, in addition to standard accuracy measures and distributional similarity scores, the authors estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods. This validation framework is applied to intercompare the performances of 12 different SD methods (from the analog, weather typing, and regression families) for downscaling minimum and maximum temperatures in Spain. First, a calibration of these methods is performed in terms of both geographical domains and predictor sets; the results are highly dependent on the latter, with optimum predictor sets including near-surface temperature data (in particular 2-m temperature), which appropriately discriminate cold episodes related to temperature inversion in the lower troposphere. Although regression methods perform best in terms of correlation, analog and weather generator approaches are more appropriate for reproducing the observed distributions, especially in case of wintertime minimum temperature. However, the latter two families significantly underestimate the temperature anomalies of the warm periods considered in this work. This underestimation is found to be critical when considering the warming signal in the late twenty-first century as given by a global climate model [the ECHAM5-Max Planck Institute (MPI) model]. In this case, the different downscaling methods provide warming values with differences in the range of 1°C, in agreement with the robustness significance values. Therefore, the proposed test is a promising technique for detecting lack of robustness in statistical downscaling methods applied in climate change studies.Thiswork has been funded by the Spanish I1D1i 2008-11 Program: A strategic action for energy and climate change (ESTCENA, code 200800050084078) and the project CGL2010-21869 (EXTREMBLES). S.B. was supported by a JAE PREDOC grant (CSIC, Spain). The authors would like to especially thank the three anonymous reviewers who helped to considerably improve this manuscript

Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods

This is the second in a pair of papers in which the performance of Statistical Downscaling Methods (SDMs) is critically re-assessed with respect to their robust applicability in climate change studies. Whereas Part I focused on temperatures (Gutierrez et al., 2013), the present manuscript deals with precipitation and considers an ensemble of twelve SDMs from the analog, weather typing, and regression (GLM) families. In the first part, we assess the performance of the methods with perfect (reanalysis) predictors, screening different geographical domains and predictor sets. To this aim, standard accuracy and distributional similarity scores, and a test for extrapolation capability based on dry observed historical periods are considered. As in Part I, the results are highly dependent on the predictor sets, with optimum configurations including information of middle tropospheric humidity (in particular Q850). As a result of this analysis, deficient SDMs are discarded in order to properly assess the spread (uncertainty) of future climate projections, avoiding the noise introduced by unsuitable models. In the second part, the resulting ensemble of SDMs is applied to four Global Circulation Models (GCMs) from the ENSEMBLES (CMIP3) project to obtain historical (1961-2000, 20C3M scenario) and future (2001-2100, A1B) regional projections. The obtained results are compared with those produced by an ensemble of Regional Climate Models (RCMs) driven by almost the same GCMs in the ENSEMBLES project. In general, the mean signal is similar with both methodologies (with the exception of Summer, where the RCMs project drier conditions) but the spread is larger for the SDM results. Finally, the contribution of the GCM and SDM-derived components to the total spread is assessed using a simple analysis of variance previously applied to the ENSEMBLES RCM ensemble. Results show that the main contributor to the spread is the choice of the GCM, except for the autumn results in the Atlantic sub-region of Spain and the Autumn and Summer results in the Mediterranean sub-region, where the choice of the SDM dominates the uncertainty during the second half of the 21st century due mainly to the different projections obtained from different families of SDM techniques. The most noticeable difference with the RCMs is the magnitude of the interaction terms, which is larger in all cases in the present study.This work has been funded by the strategic action for energy and climate change by the Spanish R&D 2008–2011 program ‘‘Programa coordinado para la generación de escenarios regionalizados de cambio climático: Regionalización Estadística (esTcena),’’ code 200800050084078, and the project CGL2015-66583-R (MINECO/FEDER). The RCM simulations used in this study were obtained from the European Union–funded FP6 Integrated Project ENSEMBLES (Contract 505539)

Enhanced geometries of macroporous silicon photonic crystals for optical gas sensing applications

A macroporous silicon photonic crystal is designed and optimized theoretically for its use in gas sensing applications and IR optical filters. Light impinges perpendicularly onto the sample surface (vertical propagation) so a three-dimensional (3d) structure is used. For gas sensing, a sharp resonance is desired in order to isolate an absorption line of the gas of interest. The high Q-factors needed mandate the use of a plane defect inside the PhC to give rise to a resonant mode inside the bandgap tuned to the gas absorption line. Furthermore to allow gas passage through the device, an open membrane is required. This can affect the mechanical resilience. To improve the strength of the photonic crystal the pores are extended after the “active” 3d part. The number of modulations, and the extension length have been optimized to obtain the largest Q-factor with reasonable transmitted power. These proposed structures have been experimentally performed, probing an enhancement of almost an order of magnitude in the Q-factor in respect with the basic case. Simulations considering CO2 have been performed showing that the proposed structures are promising as precise optical gas sensors.Peer ReviewedPostprint (author's final draft

Macroporous silicon filters, a versatile platform for NDIR spectroscopic gas sensing in the MIR

© The Author(s) 2019. Published by ECS. This is an open access article distributed under the terms of the Creative CommonsAttribution 4.0 License (CC BY,http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse of the work in anymedium, provided the original work is properly cited.This paper describes the spectroscopic detection of gases using macroporous silicon photonic crystals as narrow filters. The study begins by demonstrating the feasibility of photoelectrochemical etching to fabricate narrow filters along the mid infrared band. Next, we focus on the filter centered on the carbon dioxide fingerprint. The filter response is described for three different cell lengths and concentrations below 1%. Results show a concordance with the reformulated Beer-Lambert law. This can be used to predict the response of the filter for longer path lengths and higher concentrations, showing broad working ranges and compact sizes for CO2. In addition, optical robustness to external variations and long-term stability are also reported. Results are extrapolated to other macroporous silicon filters centered on the absorption spectra of N2O, OCS, NO2 and SO2. Finally, high sensitivity and selectivity is demonstrated by comparing them with some commercial filters.Postprint (published version

Do Type Ia Supernovae Explode Inside Planetary Nebulae?

The nature of Type Ia Supernova (SN Ia) explosions remains an open issue, with several contending progenitor scenarios actively being considered. One such scenario involves a SN Ia explosion inside a planetary nebula (PN) in the aftermath of a stellar merger triggered by a common envelope (CE) episode. We examine this scenario using hydrodynamic and non-equilibrium ionization simulations of the interaction between the SN ejecta and the PN cocoon into the supernova remnant (SNR) phase, focusing on the impact of the delay between the CE episode and the SN explosion. We compare the bulk dynamics and X-ray spectra of our simulated SNRs to the observed properties of known Type Ia SNRs in the Milky Way and the Magellanic Clouds. We conclude that models where the SN explosion happens in the immediate aftermath of the CE episode (with a delay $\lesssim$1,000 yr) are hard to reconcile with the observations, because the interaction with the dense PN cocoon results in ionization timescales much higher than those found in any known Type Ia SNR. Models with a longer delay between the CE episode and the SN explosion ($\sim$10,000 yr) are closer to the observations, and may be able to explain the bulk properties of some Type Ia SNRs.Comment: 9 pages, 5 figure

Prototipo brazo robótico.

La Inteligencia Artificial (IA) es la rama de las ciencias de la computación que se ocupa de construir sistemas que permitan exhibir un comportamiento cada vez más inteligente. Un brazo robótico es un tipo de brazo mecánico, normalmente programable, con funciones parecidas a las de un brazo humano; este puede ser la suma total del mecanismo o puede ser parte de un robot más complejo. Las partes de estos manipuladores o brazos son interconectadas a través de articulaciones que permiten, tanto un movimiento rotacional, como un movimiento transnacional o desplazamiento lineal. El efector final, o mano robótica, se puede diseñar para realizar cualquier tarea que se desee como puede ser soldar, sujetar, girar, etc., dependiendo de la aplicación. En algunas circunstancias, lo que se busca es una simulación de la mano humana, como en los robots usados en tareas de desactivación de explosivo