https://hdl.handle.net/10481/47947 2026-04-06T21:35:52Z https://hdl.handle.net/10481/111408 Thread-based colorimetric biosensor for urea determination in serum Lewinska, Izabela; Bącal, Paweł; Orbe Payá, Ignacio De; Capitán Vallvey, Luis Fermín; Erenas Rodríguez, Miguel María Urea is a crucial biomarker in clinical analysis, mainly used to diagnose and monitor kidney condition. However, its reliable determination in serum in point-of-care testing format still remains a vital challenge. Here, we tackled this issue by developing a microfluidic thread-based optical biosensor for serum urea determination. The working principle of the presented device was combining enzymatic urea hydrolysis using urease with the resulting ammonium ions detection using ionophore-chromoionophore chemistry. Urease was immobilized on thread with the aid of synthesized urease-calcium phosphate nanoflowers while components for ammonium ions detection were embedded in PVC-based membrane located on the same thread. In the first step, ammonium ions determination in a thread-based sensor was optimized. Then, urease-calcium posphate nanoflowers (U-CaNFs) were included on the thread to ensure selectivity for urea. U-CaNFs synthesis was optimized and the resulting nanoflowers were characterized using various analytical techniqes (e.g. SEM, EDS, TGA, XRD). The calculated limit of detection for urea was 37 µmol L−1 and the total analysis time was only 8 min. The developed thread-based devices were validated with control sera samples, proving their high accuracy. https://hdl.handle.net/10481/109411 Flexible Strain and Temperature Sensing NFC Tag for Smart Food Packaging Applications Escobedo Araque, Pablo; Bhattacharjee, Mitradip; Nikbakhtnasrabadi, Fatemeh; Dahiya, Ravinder This paper presents a smart sensor patch with flexible strain sensor and a printed temperature sensor integrated with a Near Field Communication (NFC) tag to detect strain or temperature in a semi-quantitative way. The strain sensor is fabricated using conductive polymer poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) in a polymer Polydimethylsiloxane microchannel. The temperature sensor is fabricated by printing silver electrodes and PEDOT:PSS on a flexible polyvinyl chloride (PVC) substrate. A custom-developed battery-less NFC tag with an LED indicator is used to visually detect the strain or temperature by modulating the LED light intensity. The LED shows maximum brightness for relaxed or no strain condition, and also in the case of maximum temperature. In contrast, the LED is virtually off for the maximum strain condition and for room temperature. Both these could be related to food spoilage. Swollen food packages can be detected with the strain sensor, serving as beacons of microbial contamination. Temperature deviations can result in the growth or survival of food-spoilage bacteria. Based on this, the potential application of the sensor system for smart food packaging is presented. https://hdl.handle.net/10481/109409 Energy Generating Electronic Skin With Intrinsic Tactile Sensing Without Touch Sensors Escobedo Araque, Pablo; Ntagios, Markellos; Shakthivel, Dhayalan; Navaraj, William T.; Dahiya, Ravinder Electronic skin (eSkin) with various types of sensors over large conformable substrates has received considerable interest in robotics. The continuous operation of large number of sensors and the readout electronics make it challenging to meet the energy requirements of eSkin. In this article, we present the first energy generating eSkin with intrinsic tactile sensing without any touch sensor. The eSkin comprises a distributed array of miniaturized solar cells and infrared light emitting diodes (IRLEDs) on soft elastomeric substrate. By innovatively reading the variations in the energy output of the solar cells and IRLEDs, the eSkin could sense multiple parameters (proximity, object location, edge detection, etc.). As a proof of concept, the eSkin has been attached to a 3-D-printed hand. With an energy surplus of 383.6 mW from the palm area alone, the eSkin could generate more than 100 W if present over the whole body (area ~1.5 m2). Further, with an industrial robot arm, the presented eSkin is shown to enable safe human-robot interaction. The novel paradigm presented in this article for the development of a flexible eSkin extends the application of solar cell from energy generation alone to simultaneously acting as touch sensors. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through engineering fellowship for growth under Grant EP/R029644/1 and Grant EP/M002527/1. https://hdl.handle.net/10481/109408 Smart Bandage with Wireless Strain and Temperature Sensors and Battery-less NFC Tag Escobedo Araque, Pablo; Bhattacharjee, Mitradip; Nikbakhtnasrabadi, Fatemeh; Dahiya, Ravinder This article presents a smart bandage with wireless strain and temperature sensors and a batteryless near-field communication (NFC) tag. Both sensors are based on conductive poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) polymer. The highly sensitive strain sensor consists of a microfluidic channel filled with PEDOT:PSS in Polydimethylsiloxane (PDMS) substrate. The strain sensor shows 3 order (~1250) increase in the resistance for 10% strain and considerably high gauge factor (GF) of ~12 500. The sensor was tested for ~30% strain, which is more than typical stretching of human skin or body parts such as chest expansion during respiration. The strain sensor was also tested for different bending and the electrical resolution was ~150% per degree of free bending and ~12k% per percentage of stretching. The resistive temperature sensor, fabricated on a Polyvinyl Chloride (PVC) substrate, showed a ~60% decrease in resistance when the temperature changed from 25 °C to 85 °C and a sensitivity of ~1% per °C. As a proof of concept, the sensors and NFC tag were integrated on wound dressing to obtain wearable systems with smart bandage form factor. The sensors can be operated and read from distance of 25 mm with a user-friendly smartphone application developed for powering the system as well as real-time acquisition of sensors data. Finally, we demonstrate the potential use of smart bandage in healthcare applications such as assessment of wound status or respiratory diseases, such as asthma and COVID-19, where monitoring via wearable strain (e.g., respiratory volume) and temperature sensors is critical. https://hdl.handle.net/10481/107005 Multimodal sensing approach using remote and onsite data for estimating the pre-harvest ripening stage of Hass Avocado with machine learning algorithms López Ruiz, Nuria; Pérez Ávila, Antonio Javier; Pérez de Vargas Sansalvador, Isabel María; Palma López, Alberto José; Capitán Vallvey, Luis Fermín; Martínez Olmos, Antonio; Erenas Rodríguez, Miguel María In recent years, avocado has gained significant global importance due to its nutritional benefits, and rising consumer demand, becoming a staple in health-conscious diets. This growing interest has also raised concerns about environmental sustainability, and as a result, efforts are being made to promote more sustainable farming practices while meeting the rising demand. In this study, we present a tool designed to enhance the efficiency of the pre-harvest process and improve avocado quality. We propose a multimodal sensing scheme integrating three different data sources: a portable multispectral system for in situ measurements, satellite imagery and, onsite environmental sensors to estimate the fruit ripening stage. This combined remote and onsite yielded a high correlation with dry matter content, considered here as the reference indicator of avocado ripening, across three consecutive harvest seasons. The performance of various machine learning techniques was evaluated using different combinations of these datasets. Notably, the artificial neural network (ANN) model achieved the highest accuracy (0.74) and recall (0.96) for predicting the overripe avocado class. Therefore, ANN model was extended to regression models, where all of them have demonstrated high predictive accuracy, with R2 coefficient ranges from 0.81 to 0.91. The online data achieved the highest coefficient (0.91), providing a slightly better performance compared to the offline model. Nonetheless, predictions based solely on multispectral data remain valuable, particularly when online data are unavailable.