In the last decade, the use of electrolyzers in various sectors has facilitated the generation of hydrogen for multiple applications, such as alternative fuel source for vehicles, generation of green hydrogen through renewable energies, or energy storage through metal hydride tanks, among others. Regardless of their application, electrolyzers are characterised by complex operation and dependence on various operating parameters, which means that their implementation in a real system is not immediate. This paper presents sensor-based algorithms aimed at ensuring safe and stable operation of a Proton Exchange Membrane Electrolyzer (PEMEL) framed within a smart microgrid powered by renewable energy. Algorithms developed to consider factors such as operating temperature and pressure, availability of feed water or the presence of water in the phase separator are presented. The goal of these algorithms is to maintain the operation of the PEMEL within nominal ranges in order to avoid degradation and/or malfunction of the materials and equipment involved in the system. The algorithms are programmed in a programmable logic controller that is responsible for managing the complete operating cycle of the PEMEL. The sensors and actuators are described together with their relevance in the operation of the PEMEL. Finally, experimental results of their implementation and real-time operation are provided.

Additive manufacturing (AM) has been played a crucial role in the fourth industrial revolution. Sensor-based monitoring technologies are essential in detecting defects and providing feedback for process control. Acoustic emission (AE) sensors have been used for long time in a wide range of processes and fields, but they are still a challenge in AM processes. This work presents a study on the AE signals in the time-domain - raw and root mean square (RMS) values - regarding their behavior during the manufacturing of a single-layer part in the Fused Filament Fabrication process for two infill patterns. Tests were conducted in a Cartesian 3D printer using Polylactic Acid material. The AE sensor was attached to the printer table through a magnetic coupling, and the signal was collected by an oscilloscope at 1 MHz sampling frequency. It was found that the raw AE signals behaved quite differently not just for the two infill patterns, but within the same pattern. The raw and RMS AE signals contain many spikes along the whole process, but the higher ones were those generally occurring at the end and/or start of a fabrication line. The RMS values, however, was useful for finding the start and end times of each fabricated line for both patterns. The mean RMS values have shown a nearly constant but distinct averages for the only-extruder, only-table and extruder-table movements.

A McKibben artificial muscle is a typical soft actuator, and it has features of flexibility, lightweight, and low cost. It consists of a rubber tube and a sleeve which is woven with spiral fibers, and contracts axially by applying pneumatic pressure to the rubber tube. It is expected to be used in robot mechanisms that can guarantee mechanical safety because of its flexibility. For controlling the artificial muscle, a sensor system for detecting the contractile displacement is required. However, the general conventional sensors do not match the advantages of the artificial muscle mentioned above.

Therefore, we have developed the combination structure of the McKibben artificial muscle and the optical fiber which works as a contractile displacement sensor. The optical fiber can be braided into the sleeve which is the necessary component of the artificial muscle, which means that the optical fiber works as both the sensor and actuator element. In addition, the optical fiber can be combined during the fabrication process of the artificial muscle by a braider machine. As the artificial muscle contracts, the spiral curvature of the sleeve fibers, including the optical fiber, changes. Then, the light intensity propagating into the optical fiber changes due to the bending loss of the optical fiber. Therefore, the displacement of the artificial muscle can be estimated by measuring the propagating light intensity. In the previous sensor system, the light-receiving part (Photo Diode) and the light-emitting part (LED) were located at the base and tip sides of the artificial muscle, respectively. Therefore, the tip of the artificial muscle had the rigid part (LED), and electrical lines had to be wired from the base to the tip. These have a limitation in the applications and electrical line troubles. In this report, the LED and the Photo Diode are arranged at the base end of the artificial muscle. For this configuration, we established the novel process during braiding. Through the process, the optical fiber from the base can be returned to the base again via the tip, and the LED and Photo Diode can be located at the base side of the artificial muscle. Experimentally the relation between the sensor output and contractile displacement of the artificial muscle was confirmed.

Food fraud is one of the issues that may threaten the consumers’ trust and confidence in the food industry. Detecting food fraud such as rice adulteration is challenging since the adulterant looks identical to authentic rice. Moreover, the detection procedure is commonly time-consuming and requires high-cost instruments to analyze the samples in the laboratory. Therefore, this study aimed to develop a rapid method to detect rice adulteration using a low-cost and portable electronic nose (e-nose) coupled with machine learning (ML). Six types of adultered rice samples were prepared by mixing the authentic rice (i.e., premium grade rice, organic rice, aromatic rice) with the respective adulterants (i.e., regular grade rice, rice from a different origin, non-organic rice, and non-aromatic rice) from 0% to 100% with a 10% increment by weight. Artificial Neural Networks (ANN) were used to develop prediction models to estimate the adulteration levels using the e-nose sensor readings acquired from the rice samples as inputs. The ML models showed that the e-nose sensors successfully predicted the six types of adulterated rice samples at various adulteration levels from 0% to 100% with high accuracy The proposed method effectively detects various combinations of adulterated rice at different mixing ratios using rapid, contactless, portable, and low-cost digital sensing devices combined with machine learning. This may help the rice industry to fight rice fraud effectively and ensure high product compliance with food quality and safety standards.

Embedded machine learning, TinyML, is a relatively new and fast-growing field of ML, enabling on-device sensor data analytics at low power requirements. This paper presents possible improvements to GMOS, a gas sensor, using TinyML technology. GMOS is a low-cost catalytic gas sensor, fabricated with the standard CMOS-SOI process, based on a suspended thermal transistor MOS (TMOS). Exothermic combustion reactions lead to temperature increases, which modify the suspended transistor’s (used as the sensing element) current-voltage characteristics. We were able to use GMOS measurements for gas classification (both for gas types, as well as concentration), resulting in high–proficiency gas detection at a low cost. Our preliminary results show great successes in the detection of ethanol and acetone gases. Moreover, we believe the method could be generalized to more gas types, concentrations, and gas mixes in future research.

In this study, hydrogen gas sensing properties of nanoporous platinum films synthesized by using the dealloying method are investigated depending on annealing, temperature and gas concentration. Platinum – copper (Pt-Cu) alloy films with approximately 50nm are coated on microscope glass slide by using magnetron co-sputtering method. In order to obtain nanoporous Pt, Pt-Cu alloy films are dealloyed in 1 M nitric acid solution for different times (15 min, 30 min, 1 hour, 2 hours, 5 hours, 10 hours and 20 hours). The structural properties of Nanoporous Pt are characterized by XRD, SEM, EDS and XPS techniques. It is observed that when dealloyed in nitric acid solution for 5 hours, Cu was completely removed from the alloy and nanoporous Pt with regular pore structure was obtained. Nanoporous Pt, which is dealloyed for 5 hours, is heat treated at different temperatures from 200 °C to 500 °C. Resistive Nanoporous Pt sensors are examined in the concentration range of 30 ppm – 5% hydrogen. The results revealed that the hydrogen sensing mechanism of the Nanoporous Pt sensors could be explained with surface scattering phenomenon. The detailed hydrogen gas sensing properties of Nanoporous Pt sensor will be discussed depending on temperature, annealing temperature and gas concentration.

This paper studies the performance of a piezoelectric-based strain sensor for Damage Detection of an asphalt Pavement Subject to Dynamic Loads. The core of the strain sensor is a metalized Polyvinylidenefluoride（PVDF) film packaged with three protection layers. The three layers offer mechanical, thermal, and corrosive protection for the PVDF film by composed of an Araldite GY-6010 epoxy resin layer, a polyurethane foam layer, and a Conathane TU-981 epoxy layer from inside to outside. The encapsulated strain sensor adopts a H-shape to optimize the overall performance. A numerical model is built for simulating the deflection of the PVDF-based strain sensor changes in real-time with the evolution of pavement crack with the sensor being embedded in the asphalt pavement. The PVDF film can convert the detected deflection change caused by the crack propagation into the amplitude change of the voltage, which can be captured and recorded using a data acquisition system. The final results of the numerical study validate the sensor performance under static and dynamic loads.

The Orinico river is in Venezuela and flows into the Carribbean sea. The chlorophyll concentration in the Ocean delta changes due to the dust deposition from the Orinoco river which affects the primary productivity. The wet and dry deposition measurements are obtained from MERRA a NASA climate reanalysis of meteorology, atmospheric chemistry, land, ocean, and aerosols data on a broad range of weather and climate time scales and places. Researchers are not sure how wet and dry deposition from the Orinoco river affects the chlorophyll concentration in the ocean. Aerosol optical depth (AOD), dry and wet deposition data are obtained from MERRA. Altimetry data of the Orinoco river and Chlorophyll concentration data are also obtained from the Giovanni database from 2016 to March, 2022. Linear regression analysis of altimetry and chlorophyll concentration show that the later does not depend on the water levels. Univariate models for each of the parameters of AOD, wet, and dry deposition are done. Bivariate models are done adding one additional variable at a time, and finally a multivariate model is built for prediction of chlorophyll concentration. From the analysis, it is seen that the multivariate models have higher correlation between chlorophyll and the independent variables. Of all the variables AOD is a better predictor of chlorophyll concentration. To improve the prediction performance, data preprocessing using a smoothing filter is performed. Also, a deep learning neural network architecture is developed for performing the predictions.

In this work, VO₂ based thermal sensing thin film synthesized on flexible muscovite substrates by direct oxidation of deposited vanadium metal, were investigated for the impact of doping and strain on their electrical properties. We investigated both undoped and Ti doped VO₂ on muscovite substrate and compared with those on Quartz substrate. Both doped and undoped VO₂ were found to undergo phase transition due to effect of heat as well as mechanical strain on muscovite substrate. On the other hand, the Ti doped VO₂, on both quartz and muscovite substrate showed significant reduction in the transition temperature compared to the undoped VO₂ thin films on these two substrates. When subjected to mechanical strain, the VO₂ thin film on muscovite substrates resulted in a decrease or an increase in resistance depending on whether the applied strain was tensile or compressive, respectively. The resistance change was also steeper around the transition temperature compared to room temperature, exhibiting high gauge factor. This metal doped VO₂ on flexible muscovite substrate has the significantly low transition temperature which causes the VO₂ film to undergo phase transition at a near-room temperature and enables it to be used as a temperature sensor with enhanced sensitivity.

The Internet of Things (IoT) offers many possibilities for digitalization in companies, among other things through sensors or wireless networks. For example, GPS data, acceleration, or speed can be recorded and evaluated. There are various ways to transmit this data. For example, they can be sent to the Internet using an Internet-capable microcontroller, in which case the information is transferred via the HTTP protocol. Another option here is the Long Range Wide Area Network (LoRaWAN).

In this paper, the LoRaWAN network coverage provided by many gateways across Germany is evaluated mainly concerning the transportation sector. For these measurements, a focus is placed on the network coverage along the German rail routes. For this purpose, related works are first analyzed. Based on this, the objective of the measurement is concretized, and a microcontroller measurement module is prototypically developed and built. It is equipped with a suitable firmware based on the Arduino framework. Along a previously defined route, the measurement module continuously attempts to send GPS data via LoRaWAN. The received data will be stored and evaluated, including metadata like RSSI. The evaluation is not only concerned with network coverage, but also with some considerations of possible use cases in the transportation sector. Problems and Limitations will also be discussed.