With technological advances occurring worldwide, wearable electronics have garnered significant attention within diverse fields, from medical to industry. Carbon Nanotube (CNT) ink has had a big impact on these applications of e-textiles. Conductive patterns can be made by printing the CNT ink directly on fabrics. However, a new process and chemical have been tested, where alternatively, a conductive pattern can be applied by laser burning of a lignin-coated layer over a piece of fabric. In this work, we evaluated the quality of patterned laminated polyester ripstop fabric with the two methods of silk screen printing of the CNT ink and the laser burning of lignin. A major focus within these two methods is their resistance values. CNT ink has succeeded with a lower four-probe resistance of about 19.69 Ω/, making it preferable for wearable applications. However, patterning can become an issue when coating CNT ink. Laser burning has its pros and cons: while it can be much simpler to pattern, it has a much higher four-probe resistance of about 49.21 Ω/. In this study, the laser power and rastering are evaluated and compared to the resistance values of CNT ink coating. With this success, further testing on different fabrics and patterns can lead to more inexpensive yet efficient applications and devices.
KEYWORDS: Sensors, Data modeling, Gas sensors, Principal component analysis, Machine learning, Gases, Detector arrays, Bioalcohols, Education and training, Nose
With the increasing demand in using electronic noses (e-noses) for various medical, industrial, and military applications, the technology of such devices is still struggling with the limitations in the gas sensors. Particularly, a limitation is in the relatively poor sensitivity and selectivity of the commercially available sensors for measuring the concentrations of various gases and volatile organic compounds (VOCs). The shortcoming has been addressed by employing machine learning (ML) methods to analyze signals from an array of gas sensors. However, with different ML models, it is required to study the effect of different models on data interpretation. In this study, we have designed a microcontroller-based system equipped with eight different gas/VOC sensors, designed for detecting CO2, O2, CO, NO, NO2, NH3, alcohol, and acetone. The sensors were tested with streams of air mixed with various VOCs including methanol, ethanol, and isopropanol at different flow rates. The collected data from the sensors were analyzed using PCA, LDA, and CNN methods for not only recognizing the signatures of different gases, but also differentiating between them and recognizing their ratio in a mixture. The results of the studies are promising for designing more effective hardware equipped with an ML modeling system to analyze the concentration of various gases and VOCs in a mixed situation.
Despite the expected high demands in the agricultural industry, the application of health monitoring systems for plants is still at the research level. While imaging methods are often used for monitoring the health status of the shoot part of a plant, there are limited parameters that can be measured for assessing the health status of a plant root. Studies show that roots need oxygen for aerobic respiration. Higher dissolved oxygen near the root zone may result in a more massive root and a healthier plant. Conventional oxygen sensors are designed to measure the oxygen level in a gaseous environment. Due to their bulky structure, their application for monitoring oxygen in the soil is challenging. In this study, we have used A10 zinc-air batteries as oxygen sensors to monitor the oxygen level at the root zone of four garden plants: sweet pepper, basil, tomato, and cherry tomato. Using a microcontroller system, the electric current from the batteries was recorded as a signal related to the oxygen level. The measurements indicate a variation of ~1% in the oxygen level every 24 hours when the plants were exposed to a controlled light for 12 hours and kept in dark for 12 hours. The simplicity of the application of Zn-air batteries allows us to monitor the oxygen level at several locations around the root of a plant to study their breathing through their roots.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.