Paper-Based Carbon Dioxide Sensors: Past, Present, and Future Perspectives

Paper-based sensors are a new possible technology for fabricating easy, economical, portable, and expendable analytical devices for various application fields counting, diagnosis clinically, control of the quality of the food, and environmental monitoring. The distinctive characteristics of paper that enable the transport of the passive liquid and the affinity with chemicals/biochemical is the principal lead of employing paper as a sensing platform. Even if paper-based sensors are extremely favorable, they are quite abided due to undeniable constraints, namely, accuracy and sensitivity. Nevertheless, it is forecasted that in the coming times, with improvisation in the fabrication and analytical techniques, that there will be adding new and novel evolution in paper-based sensors. These sensors can meet the present-day intentions of being a cost-efficient and portable device besides contributing high sensitivity and selectivity and multiple analytes biasing. The present work is a review of paper-based sensors for sensing carbon dioxide.

Development of Volcano Early Warning System for Kelud Volcano

Kelud is one of Indonesian volcano lies between Kediri and Blitar districts of East Java province. This volcano has erupted since 1000 where casualties of 200000 people emerged until the last eruption in 2014. Therefore, it is needed a volcano early warning system to detect the eruption earlier for minimizing the casualties. We have developed an early warning system based on sensor nodes consist of vibration, temperature and gasses (sulfur and carbon dioxide) sensors to monitor the physical parameter of the volcano, drone surveillance, mapping and temperature measurement, and mobile robot consists of the same sensor as in the node for both normal and emergency situations. The system has been tested in Kelud volcano in August 2019. In a normal condition, the system has detected 1 Hz of seismicity, under 1 ppm of sulfur and carbon dioxide, 23-55.3oC of the lake temperature, 32oC of the ground temperature and 23-25oC of the air temperature. The system could be used for 37 hours of full operation for 1 charging cycles of solar cell’s charging process where suitable for dangerous environment application.

Investigating the Plastic Decomposing Ability of Tenebriomolitor Using Carbon Dioxide Sensors

According to a group of Stanford scientists, mealworms, the larva of darkling beetles, can digest Styrofoam, a type of polystyrene, and break it down into carbon dioxide. This paper investigates the plastic-digesting property of mealworms on different types of plastic, namely polystyrene (PS), low- density polyethylene (LDPE) and polyvinyl chloride (PVC). Since mealworms breathe in oxygen and exhale carbon dioxide. To measure the extent of plastic intake, we explored an alternative method to measure the extent of plastic digestion - using Arduino carbon dioxide sensors instead of calculating the difference in weight of plastic. As the increase in the exhalation of carbon dioxide is indicative of the amount of plastic consumed, the authors measured the change in carbon dioxide concentration of the mealworms' environment as they consume plastic. This is put into comparison with when the mealworms respire without food. The results show that mealworms consume Styrofoam to a certain extent. However, they are mostly unable to digest polyethylene and polyvinyl chloride, likely due to their elasticity and high density respectively.

Development of CNN-based visual recognition air conditioner for smart buildings

Demand-driven heating, ventilation, and air conditioning (HVAC) operations have become very attractive in energy-efficient smart buildings. Demand-oriented HVAC control largely relies on accurate detection of building occupancy levels and locations. So far, existing building occupancy detection methods have their disadvantages, and cannot fully meet the expected performance. To address this challenge, this paper proposes a visual recognition method based on convolutional neural networks (CNN), which can intelligently interpret visual contents of surveillance cameras to identify the number of occupants and their locations in buildings. The proposed study can detect the quantity, distance, and angle of indoor human users, which is essential for controlling air-conditioners to adjust the direction and speed of air blow. Compared with the state of the art, the proposed method successfully fulfills the function of building occupant counting, which cannot be realized when using PIR, sound, and carbon dioxide sensors. Our method also achieves higher accuracy in detecting moving or stationary human bodies and can filter out false detections (such as animal pets or moving curtains) that are existed in previous solutions. The proposed idea has been implemented and collaboratively tested with air conditioners in an office environment. The experimental results verify the validity and benefits of our proposed idea.