Open Access
Micro sized interdigital capacitor for humidity detection based on agarose coating
(2021) Vitoria Pascual, Ignacio; Armas, Dayron; Coronel Camones, Carlos Manuel; Ozcariz Celaya, Aritz; Ruiz Zamarreño, Carlos; Matías Maestro, Ignacio; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
A micro sized interdigital capacitor has been proposed for the detection of relative humidity. The photolithography technique enables the fabrication of fingers with a size of 10x500 um. A thin film of agarose functionalizes the sensor for humidity sensing, which improves its performance by 155 times, obtaining a sensitivity of 32.98 pF/%RH.
Open Access
Optical sensors based on lossy-mode resonances
(Elsevier Science, 2017) Matías Maestro, Ignacio; Ascorbe Muruzabal, Joaquín; Acha Morrás, Nerea de; López Torres, Diego; Zubiate Orzanco, Pablo; Sánchez Zábal, Pedro; Urrutia Azcona, Aitor; Socorro Leránoz, Abián Bentor; Rivero Fuente, Pedro J.; Hernáez Sáenz de Zaitigui, Miguel; Elosúa Aguado, César; Goicoechea Fernández, Javier; Bariáin Aisa, Cándido; Corres Sanz, Jesús María; Ruiz Zamarreño, Carlos; Arregui San Martín, Francisco Javier; Del Villar, Ignacio; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa; Institute of Smart Cities - ISC
Open Access
Optical biosensors: a quick overview
(2021) Imas González, José Javier; Ruiz Zamarreño, Carlos; Matías Maestro, Ignacio; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
This work aims to provide a brief overview of the latest trends in the domain of optical biosensors.
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 Publikoa
This 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.
Embargo
Optimization of optical spectroscopy classification algorithms for limited data scenarios in the food industry: tomato sauce samples case
(Elsevier, 2025-01-01) Gracia Moisés, Ander; Vitoria Pascual, Ignacio; Avedillo de la Casa, Amaia; Moreno Pérez, María; Imas González, José Javier; Ruiz Zamarreño, Carlos; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertistate Publikoa
This study addresses the problem of training deep learning models with limited datasets, a significant challenge in sectors like medical imaging and food quality analysis. To tackle this issue, generative adversarial networks (GANs) will be employed to augment the available data and improve model performance. An innovative approach is introduced here, integrating semi-supervised learning and generative modeling to maximize the use of small datasets in developing robust models. The method involves reversing the conventional distribution of training and testing data to focus on model evaluation and generalization from limited samples. Wasserstein GANs (WGANs) and Semi-Supervised GANs (SGANs), are utilized to supplement datasets with synthetic but realistic examples, enhancing the training process in scenarios of data scarcity. These techniques are applied in the context of visible reflectance spectroscopy to analyze tomato sauces, demonstrating the method's effectiveness in non-invasively assessing key quality parameters such as oil content, °Brix, and pH. The results show significant improvements in model performance metrics: for %Oil content, overall accuracy increased from 0.47 to 0.66; for °Bx, it rose from 0.65 to 0.71; and for pH measurement, accuracy improved from 0.43 to 0.62. These outcomes highlight the model's improved capability to generalize and maintain accuracy with limited data.
Open Access
Low-cost optical fiber multimode interference biosensor based on a glucose sensitive Glucose-Oxidase enzyme thin-film
(Elsevier, 2024-12-04) Rodríguez Rodríguez, Wenceslao Eduardo; Rodríguez Rodríguez, Adolfo Josué; Juárez-Saldivar, Alfredo; Ruiz Zamarreño, Carlos; Matías Maestro, Ignacio; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
In this research we report a contribution for the development of low-cost fiber optical biosensors fabricated by the Single Mode-Multi Mode-Single Mode configuration applied for the glucose monitoring considering clinical concentrations ranges in aqueous analytes. Designed devices are evaluated using health standard detection ranges, such as healthy, pre-diabetic, and diabetic stages operating at the visible spectral region. The sensing regions has been prepared by the etching technique in order to improve the interaction between the evanescent wave with the surrounding medium followed by functionalization of enzyme oxidase glucose via the electrostatic self-assembly using by Poly(allylamine hydrochloride) as an immobilizer matrix. The increase of bilayers number over the sensor surface permits us to demonstrate the enhancement of sensitivity and limit of detection. Experimental results permitted the glucose characterization in the range from 0.3 to 2.4 mg mL-1 obtaining a response time of 9 s and a sensitivity of 1.8 nm/(mg mL-1) allowing to detect hypoglycemia and diabetes stages according to the World Health Organization standards.
Open Access
Fiber-optic pH sensors fabrication based on selective deposition of neutral red
(IEEE, 2009-01-22) Hernáez Sáenz de Zaitigui, Miguel; Arregui San Martín, Francisco Javier; Matías Maestro, Ignacio; Ruiz Zamarreño, Carlos; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
In this work, a novel application of the electric field directed layer-by-layer self assembly (EFDLA) selective deposition method for the fabrication of optical fiber pH sensors is presented. Here, indium tin oxide (ITO) coated optical fibers have been fabricated via a dip-coating deposition method. These fibers are used as electrodes in the EFDLA protocol in order to deposit selectively the sensitive layer. Neutral Red (NR) colorimetric pH sensitive indicator and the polymers poly(acrylic acid) (PAA) and poly(allylamine hydrochloride) (PAH) are used in order to obtain a pH sensitive nanostructured coating onto ITO coated optical fibers. The results obtained in this work revealed that the LbL material adsorption on the electrodes can be enhanced or even inhibited when applying a specific direct current voltage between them under some other specific fabrication parameters. Particularly, the response of these sensors to variations of the pH in the surrounding medium was studied when the pH of the solutions used for the fabrication of the films was adjusted to 7.0 and the potential applied between electrodes was set to 2.5 V. These sensors showed fast response time and high repeatability.
Open Access
Ultra-low detection limit lossy mode resonance-based fibre-optic biosensor
(Optica Publishing Group, 2018) Chiavaioli, Francesco; Zubiate Orzanco, Pablo; Del Villar, Ignacio; Ruiz Zamarreño, Carlos; Giannetti, Ambra; Tombelli, Sara; Trono, Cosimo; Matías Maestro, Ignacio; Arregui San Martín, Francisco Javier; Baldini, Francesco; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
We report on ultra-low detection limit lossy mode resonance-based fibre-optic biosensor coated with nm-thick tin dioxide film, which allows measuring fibre-surrounding medium changes with very high sensitivity for label-free and real-time biomolecular interaction analysis
Open Access
Air bubble detection in water flow by means of ai-assisted infrared reflection system
(IEEE, 2024-06-26) Gracia Moisés, Ander; Vitoria Pascual, Ignacio; Imas González, José Javier; Ruiz Zamarreño, Carlos; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
This letter introduces an innovative, cost-effective solution for detecting air bubbles in water flow systems using an AI-assisted infrared reflection system. In industries, such as chemical, mechanical, oil, and nuclear, the presence of air bubbles in fluids can compromise both product quality and process efficiency. Our research develops a system that combines infrared optical sensors with machine learning algorithms to detect and quantify bubble presence effectively. The system’s design utilizes infrared emitters and photodetectors arranged around a pipe to capture detailed data on bubble characteristics, which is then analyzed using a support vector machine (SVM) model to predict bubble concentrations. Experimental results demonstrate the system’s ability to accurately identify different levels of bubble presence, offering significant improvements over existing methods. Key performance metrics include a mean squared error of 0.0694, a root mean squared error of 0.2634, and a coefficient of determination of 0.9765, indicating high accuracy and reliability. This approach not only enhances operational reliability and safety but also provides a scalable solution adaptable to various industrial settings.
Open Access
D-shape optical fiber refractometer based on TM and TE lossy mode resonances
(SPIE, 2014) Zubiate Orzanco, Pablo; Ruiz Zamarreño, Carlos; Del Villar, Ignacio; Matías Maestro, Ignacio; Arregui San Martín, Francisco Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza
The fabrication and characterization of an optical fiber refractometer based on Lossy Mode Resonances (LMR) is presented. TiO2/ poly (sodium 4-styrenesulfonate) coatings deposited on side-polished D-shaped optical fibers are used as LMR supporting coatings. LMRs are sensitive to the external medium refractive index and D-shaped optical fibers enable the observation of TE and TM LMR polarizations. These refractometers based on TE and TM LMR showed an average sensitivity of 2737 nm/RIU and 2893 nm/RIU respectively for a surrounding medium refractive index (SMRI) range from 1.35 to 1.41.