In recent years, advancements in micromachining techniques and nanomaterials have enabled the fabrication of highly sensitive devices for the detection of odorous species. Recent efforts done in the miniaturization of gas sensors have contributed to obtain increasingly compact and portable devices. Besides, the implementation of new nanomaterials in the active layer of these devices is helping to optimize their performance and increase their sensitivity close to humans’ olfactory system. Nonetheless, a common concern of general-purpose gas sensors is their lack of selectivity towards multiple analytes. In recent years, advancements in microfabrication techniques and microfluidics have contributed to create new microanalytical tools, which represent a very good alternative to conventional analytical devices and sensor-array systems for the selective detection of odors. Hence, this paper presents a general overview of the recent advancements in microfabricated gas sensors and microanalytical devices for the sensitive and selective detection of volatile organic compounds (VOCs). The working principle of these devices, design requirements, implementation techniques, and the key parameters to optimize their performance are evaluated in this paper. The authors of this work intend to show the potential of combining both solutions in the creation of highly compact, low-cost, and easy-to-deploy platforms for odor monitoring.
Polymeric materials are widely employed for monitoring volatile organic compounds (VOCs). Compared to other sensitive materials, polymers can provide a certain degree of selectivity, based on their chemical affinity with organic solvents. The addition of conductive nanoparticles within the polymer layer is a common practice in recent years to improve the sensitivity of these materials. However, it is still unclear the effect that the nanoparticles have on the selectivity of the polymer membrane and vice versa. The current work proposes a methodology based on the Hansen solubility parameters, for assessing the selectivity of both pristine and hybrid polymer nanocomposites. The impedance response of thin polydimethylsiloxane (PDMS) films is compared to the response of hybrid polymer films, based on the addition of multi-walled carbon nanotubes (MWCNTs). With the addition of just 1 wt.% of MWCNTs, fabricated sensors showcased a significant improvement in sensitivity, faster response times, as well as enhanced classification of non-polar analytes (>22% increase) compared to single PDMS layers. The methodology proposed in this work can be employed in the future to assess and predict the selectivity of polymers in single or array-based gas sensors, microfluidic channels, and other analytical devices for the purpose of VOCs discrimination.
In an attempt to mitigate emissions and road traffic, a significant interest has been recently noted in expanding the use of shared vehicles to replace private modes of transport. However, one outstanding issue has been the hesitancy of passengers to use shared vehicles due to the substandard levels of interior cleanliness, as a result of leftover items from previous users. The current research focuses on developing a novel prediction model using computer vision capable of detecting various types of trash and valuables from a vehicle interior in a timely manner to enhance ambience and passenger comfort. The interior state is captured by a stationary wide-angled camera unit located above the seating area. The acquired images are preprocessed to remove unwanted areas and subjected to a convolutional neural network (CNN) capable of predicting the type and location of leftover items. The algorithm was validated using data collected from two research vehicles under varying conditions of light and shadow levels. The experiments yielded an accuracy of 89% over distinct classes of leftover items and an accuracy of 91% among the general classes of trash and valuables. The average execution time was 65 s from image acquisition in the vehicle to displaying the results in a remote server. A custom dataset of 1379 raw images was also made publicly available for future development work. Additionally, an indoor air quality (IAQ) unit capable of detecting specific air pollutants inside the vehicle was implemented. Based on the pilots conducted for air quality monitoring within the vehicle cabin, an IAQ index was derived which corresponded to a 6-level scale in which each level was associated with the explicit state of interior odour. Future work will focus on integrating the two systems (item detection and air quality monitoring) explicitly to produce a discrete level of cleanliness. The current dataset will also be expanded by collecting data from real shared vehicles in operation.
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