ABSTRACT: In this work an array of chemical sensors for gas detection has been developed, starting with a commercial sensor platform developed by Microchip (GestIC), which is normally used to detect, trace, and classify hand movements in space. The system is based on electric field changes, and in this work, it has been used as mechanism revealing the adsorption of chemical species CO2 and O2. The system is composed of five electrodes, and their responses were obtained by interfacing the sensors with an acquisition board based on an ATMEGA 328 microprocessor (Atmel MEGA AVR microcontroller). A dedicated measurement chamber was designed and prototyped in acrylonitrile butadiene styrene (ABS) using an Ultimaker3 3D printer. The measurement cell size is 120 × 85 mm. Anthocyanins (red rose) were used as a sensing material in order to functionalize the sensor surface. The sensor was calibrated using different concentrations of oxygen and carbon dioxide, ranging from 5% to 25%, mixed with water vapor in the range from 50% to 90%. The sensor exhibits good repeatability for CO2 concentrations. To better understand the sensor response characteristics, sensitivity and resolution were calculated from the response curves at different working points. The sensitivity is in the order of magnitude of tens to hundreds of µV/% for CO2, and of µV/% in the case of O2. The resolution is in the range of 10-1%-10-3% for CO2, and it is around 10-1% for O2. The system could be specialized for different fields, for environmental, medical, and food applications.
Project description:<h4>Background</h4>Hypoxia-based cell culture experiments are routine and essential components of in vitro cancer research. Most laboratories use low-cost portable modular chambers to achieve hypoxic conditions for cell cultures, where the sealed chambers are purged with a gas mixture of preset O2 concentration. Studies are conducted under the assumption that hypoxia remains unaltered throughout the 48 to 72 hour duration of such experiments. Since these chambers lack any sensor or detection system to monitor gas-phase O2, the cell-based data tend to be non-uniform due to the ad hoc nature of the experimental setup.<h4>Methodology</h4>With the availability of low-cost open-source microcontroller-based electronic project kits, it is now possible for researchers to program these with easy-to-use software, link them to sensors, and place them in basic scientific apparatus to monitor and record experimental parameters. We report here the design and construction of a small-footprint kit for continuous measurement and recording of O2 concentration in modular hypoxia chambers. The low-cost assembly (US$135) consists of an Arduino-based microcontroller, data-logging freeware, and a factory pre-calibrated miniature O2 sensor. A small, intuitive software program was written by the authors to control the data input and output. The basic nature of the kit will enable any student in biology with minimal experience in hobby-electronics to assemble the system and edit the program parameters to suit individual experimental conditions.<h4>Results/conclusions</h4>We show the kit's utility and stability of data output via a series of hypoxia experiments. The studies also demonstrated the critical need to monitor and adjust gas-phase O2 concentration during hypoxia-based experiments to prevent experimental errors or failure due to partial loss of hypoxia. Thus, incorporating the sensor-microcontroller module to a portable hypoxia chamber provides a researcher a capability that was previously available only to labs with access to sophisticated (and expensive) cell culture incubators.
Project description:Characterizing highly dynamic, transient, and vertically lofted emissions from open area sources poses unique measurement challenges. This study developed and applied a multipollutant sensor and time-integrated sampler system for use on mobile applications such as vehicles, tethered balloons (aerostats) and unmanned aerial vehicles (UAVs) to determine emission factors. The system is particularly applicable to open area sources, such as forest fires, due to its light weight (3.5 kg), compact size (6.75 L), and internal power supply. The sensor system, termed "Kolibri", consists of sensors measuring CO2 and CO, and samplers for particulate matter (PM) and volatile organic compounds (VOCs). The Kolibri is controlled by a microcontroller which can record and transfer data in real time through a radio module. Selection of the sensors was based on laboratory testing for accuracy, response delay and recovery, cross-sensitivity, and precision. The Kolibri was compared against rack-mounted continuous emissions monitoring system (CEMs) and another mobile sampling instrument (the "Flyer") that has been used in over ten open area pollutant sampling events. Our results showed that the time series of CO, CO2, and PM2.5 concentrations measured by the Kolibri agreed well with those from the CEMs and the Flyer, with a laboratory- tested percentage error of 4.9%, 3%, and 5.8%, respectively. The VOC emission factors obtained using the Kolibri were consistent with existing literature values that relate concentration to combustion efficiency. The potential effect of rotor downwash on particle sampling was investigated in an indoor laboratory and the preliminary results suggested that its influence is minimal. Field application of the Kolibri sampling open detonation plumes indicated that the CO and CO2 sensors responded dynamically and their concentrations co-varied with emission transients. The Kolibri system can be applied to various challenging open area scenarios such as fires, lagoons, flares, and landfills.
Project description:Mobile health monitoring via non-invasive wearable sensors is poised to advance telehealth for older adults and other vulnerable populations. Extreme heat and other environmental conditions raise serious health challenges that warrant monitoring of real-time physiological data as people go about their normal activities. Mobile systems could be beneficial for many communities, including elite athletes, military special forces, and at-home geriatric monitoring. While some commercial monitors exist, they are bulky, require reconfiguration, and do not fit seamlessly as a simple wearable device. We designed, prototyped and tested an integrated sensor platform that records heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response. The device uses a small microcontroller to integrate the measurements and store data directly on the device for up to 48+ h. continuously. The device was compared to clinical standards for calibration and performance benchmarking. We found that our system compared favorably with clinical measures, such as fingertip pulse oximetry and infrared thermometry, with high accuracy and correlation. Our novel platform would facilitate an individualized approach to care, particularly those whose access to healthcare facilities is limited. The platform also can be used as a research tool to study physiological responses to a variety of environmental conditions, such as extreme heat, and can be customized to incorporate new sensors to explore other lines of inquiry.
Project description:Over the last decade, smart sensors have grown in complexity and can now handle multiple measurement sources. This work establishes a methodology to achieve better estimates of physical values by processing raw measurements within a sensor using multi-physical models and Kalman filters for data fusion. A driving constraint being production cost and power consumption, this methodology focuses on algorithmic complexity while meeting real-time constraints and improving both precision and reliability despite low power processors limitations. Consequently, processing time available for other tasks is maximized. The known problem of estimating a 2D orientation using an inertial measurement unit with automatic gyroscope bias compensation will be used to illustrate the proposed methodology applied to a low power STM32L053 microcontroller. This application shows promising results with a processing time of 1.18 ms at 32 MHz with a 3.8% CPU usage due to the computation at a 26 Hz measurement and estimation rate.
Project description:A new luminescent indicator is presented that enables simultaneous measurement of oxygen and temperature at a single wavelength. The indicator, an alkylsulfone-substituted Zn(II)-meso-tetraphenyltetrabenzoporphyrin, emits prompt and thermally activated delayed fluorescence (TADF). TADF is sensitive toward oxygen and temperature and is referenced against prompt fluorescence (PF) that is not affected by oxygen. The information on both parameters is accessed from the decay time of TADF and the temperature-dependent ratio of TADF and PF. Sensor foils, made from poly(styrene-co-acrylonitrile) and the indicator dye, enable temperature-compensated trace oxygen sensing (0.002-6 hPa pO2) at ambient conditions. Compared to the previously reported dual sensors based on two emitters, the new sensor significantly simplifies the experimental setup and eliminates risks of different leaching or photobleaching rates by utilizing only one indicator dye and operating at a single wavelength.
Project description:In this work, we present a complete hardware development and current consumption study of a portable electronic nose designed for the Internet-of-Things (IoT). Thanks to the technique of measuring in the initial action period, it can be reliably powered with a moderate-sized battery. The system is built around the well-known SoC (System on Chip) ESP8266EX, using low-cost electronics and standard sensors from Figaro's TGS26xx series. This SoC, in addition to a powerful microcontroller, provides Wi-Fi connectivity, making it very suitable for IoT applications. The system also includes a precision analog-to-digital converter for the measurements and a charging module for the lithium battery. During its operation, the designed software takes measurements periodically, and keeps the microcontroller in deep-sleep state most of the time, storing several measurements before uploading them to the cloud. In the experiments and tests carried out, we have focused our work on the measurement and optimization of current consumption, with the aim of extending the battery life. The results show that taking measurements every 4 min and uploading data every five measurements, the battery of 750 mAh needs to be charged approximately once a month. Despite the fact that we have used a specific model of gas sensor, this methodology is quite generic and could be extended to other sensors with lower consumption, increasing very significantly the duration of the battery.
Project description:We develop a laser-assisted sensor embedding process to embed all-glass optical fiber sensors into bulk ceramics for high-temperature applications. A specially designed two-step microchannel was fabricated on an Al2O3 substrate for sensor embedment using a picosecond (ps) laser. An optical fiber Intrinsic Fabry-Perot Interferometer (IFPI) sensor was embedded at the bottom of the microchannel and covered by Al2O3 slurry which was subsequently sintered by a CO2 laser. The sensor spectrum was in-situ monitored during the laser sintering process to ensure the survival of the sensor and optimize the laser sintering parameters. By testing in furnace through high temperature, the embedded optical fiber shows improved stability after CO2 laser sealing, resulting in the linear temperature response of the embedded optical fiber IFPI sensor. To improve the embedded IFPI sensor for thermal strain measurement, a dummy fiber was co-embedded with the sensing fiber to improve the mechanical bonding between the sensing fiber and the ceramic substrate so that the thermal strain of the ceramic substrate can apply on the sensing fiber. The response sensitivity, measurement repeatability and high-temperature long-term stability of the embedded optical fiber IFPI sensor were evaluated in this work.
Project description:Motivated by the recent realization of cluster-assembled nanomaterials as gas sensors, first-principles calculations are carried out to explore the stability and electronic properties of Zn12O12 cluster-assembled nanowires and the adsorption behaviors of environmental gases on the Zn12O12-based nanowires, including CO, NO, NO2, SO2, NH3, CH4, CO2, O2 and H2. Our results indicate that the ultrathin Zn12O12 cluster-assembled nanowires are particularly thermodynamic stable at room temperature. The CO, NO, NO2, SO2, and NH3 molecules are all chemisorbed on the Zn12O12-based nanowires with reasonable adsorption energies, but CH4, CO2, O2 and H2 molecules are only physically adsorbed on the nanowire. The electronic properties of the Zn12O12-based nanowire present dramatic changes after the adsorption of the NO and NO2 molecules, especially their electric conductivity and magnetic properties, however, the other molecules adsorption hardly change the electric conductivity of the nanowire. Meanwhile, the recovery time of the nanowire sensor at T?=?300?K is estimated at 1.5 ?s and 16.7 ?s for NO and NO2 molecules, respectively. Furthermore, the sensitivities of NO and NO2 are much larger than that of the other molecules. Our results thus conclude that the Zn12O12-based nanowire is a potential candidate for gas sensors with highly sensitivity for NO and NO2.
Project description:While temporary streams account for more than half of the global discharge, high spatiotemporal resolution data on the three main hydrological states (dry streambed, standing water, and flowing water) of temporary stream remains sparse. This study presents a low-cost, multi-sensor system to monitor the hydrological state of temporary streams in mountainous headwaters. The monitoring system consists of an Arduino microcontroller board combined with an SD-card data logger shield, and four sensors: an electrical resistance (ER) sensor, temperature sensor, float switch sensor, and flow sensor. The monitoring system was tested in a small mountainous headwater catchment, where it was installed on multiple locations in the stream network, during two field seasons (2016 and 2017). Time-lapse cameras were installed at all monitoring system locations to evaluate the sensor performance. The field tests showed that the monitoring system was power efficient (running for nine months on four AA batteries at a five-minute logging interval) and able to reliably log data (<1% failed data logs). Of the sensors, the ER sensor (99.9% correct state data and 90.9% correctly timed state changes) and flow sensor (99.9% correct state data and 90.5% correctly timed state changes) performed best (2017 performance results). A setup of the monitoring system with these sensors can provide long-term, high spatiotemporal resolution data on the hydrological state of temporary streams, which will help to improve our understanding of the hydrological functioning of these important systems.
Project description:Scaling up robot swarms to collectives of hundreds or even thousands without sacrificing sensing, processing, and locomotion capabilities is a challenging problem. Low-cost robots are potentially scalable, but the majority of existing systems have limited capabilities, and these limitations substantially constrain the type of experiments that could be performed by robotics researchers. Instead of adding functionality by adding more components and therefore increasing the cost, we demonstrate how low-cost hardware can be used beyond its standard functionality. We systematically review 15 swarm robotic systems and analyse their sensing capabilities by applying a general sensor model from the sensing and measurement community. This work is based on the HoverBot system. A HoverBot is a levitating circuit board that manoeuvres by pulling itself towards magnetic anchors that are embedded into the robot arena. We show that HoverBot's magnetic field readouts from its Hall-effect sensor can be associated to successful movement, robot rotation and collision measurands. We build a time series classifier based on these magnetic field readouts. We modify and apply signal processing techniques to enable the online classification of the time-variant magnetic field measurements on HoverBot's low-cost microcontroller. We enabled HoverBot with successful movement, rotation, and collision sensing capabilities by utilising its single Hall-effect sensor. We discuss how our classification method could be applied to other sensors to increase a robot's functionality while retaining its cost.