Gallego Martínez, Elieser Ernesto
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Gallego Martínez
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Elieser Ernesto
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Ingeniería Eléctrica, Electrónica y de Comunicación
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Publication Embargo Hyperbolic mode resonance-based acetone optical sensors powered by ensemble learning(Elsevier, 2024-11-01) Gallego Martínez, Elieser Ernesto; Ruiz Zamarreño, Carlos; Meurs, Joris; Cristescu, Simona M.; Matías Maestro, Ignacio; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaThe current work describes and compares the performance of hyperbolic mode resonance (HMR)-based sensors for the detection of acetone at parts per billion (ppb) concentrations using ensemble machine learning (EML) techniques. A pair of HMR based-sensors with resonances located in the visible (VIS) and mid infrared (MIR) regions were obtained in order to train a set of ensemble machine learning models. The response of the detection system formed by both devices in the VIS and MIR regions, with the help of the EML system, allowed the limit of detection (LoD) of the sensors to be reduced by an order of magnitude. It is the first time that HMR-based sensors are shown in practical applications, at the same time that their performance is improved using EML techniques. This opens new avenues for the use of this type of HMR-based sensors for the detection of other substances, in addition to improving the performance of any optoelectronic sensor using EML techniques.Publication Open Access Photonic chip breath analyzer(SpringerOpen, 2025-06-03) Gallego Martínez, Elieser Ernesto; Matías Maestro, Ignacio; Ruiz Zamarreño, Carlos; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaThis work introduces a novel single-package optical sensing device for multiple gas sensing, which is suitable for breath analysis applications. It is fabricated on a coverslip substrate via a sputtering technique and uses a planar waveguide configuration with lateral incidence of light. It features three sequentially ordered strips of different materials, which serve to increase the multivariate nature of the response of the device to different gases. For the proof-of-concept, the selected materials are indium tin oxide (ITO), tin oxide (SnO2), and chromium oxide III (Cr2O3), while the selected gases are nitric oxide (NO), acetylene (C2H2), and ammonia (NH3). The sensing mechanism is based on the hyperbolic mode resonance (HMR) effect, with the first-order resonance obtained for each strip located in the near infrared region. The multivariate response of the resonances and the correlation with the concentration of each gas allow training a machine learning (ML) model based on a nonlinear autoregressive neural network, enabling the accurate prediction of the concentration of each gas. The obtained limit of detection for all the gases was in the order of a few parts per billion. This innovative approach coined as the multivariate optical resonances spectroscopy demonstrates the potential of HMR-based optical sensors in combination with ML techniques for ultra-sensitive multi-gas detection applications using a single device.