Trial for simple gas sensor composed of as-grown carbon nanotubes

Metrisor: A novel diagnostic method for metritis detection in cattle based on machine learning and sensors

The Metrisor device has been developed using gas sensors for rapid, highly accurate and effective diagnosis of metritis. 513 cattle uteri were collected from abattoirs and swabs were taken for microbiological testing. The Metrisor device was used to measure intrauterine gases. The results showed a bacterial growth rate of 75.75 % in uteri with clinical metritis. In uteri positive for clinical metritis, the most commonly isolated and identified bacteria were Trueperella pyogenes, Fusobacterium necrophorum and Escherichia coli. Measurements taken with Metrisor to determine the presence of metritis in the uterus yielded the most successful results in evaluations of relevant machine learning algorithms. The ICO (Iterative Classifier Optimizer) algorithm achieved 71.22 % accuracy, 64.40 % precision and 71.20 % recall. Experiments were conducted to examine bacterial growth in the uterus and the random forest algorithm produced the most successful results with accuracy, precision and recall values of 78.16 %, 75.30 % and 78.20 % respectively. ICO also showed high performance in experiments to determine bacterial growth in metritis-positive uteri, with accuracy, precision and recall values of 78.97 %, 77.20 % and 79.00 %, respectively. In conclusion, the Metrisor device demonstrated high accuracy in detecting metritis and bacterial growth in uteri and could identify bacteria such as E. coli, S. aureus, coagulase-negative staphylococci, T. pyogenes, Bacillus spp., Clostridium spp. and F. necrophorum with rates up to 80 %. It provides a reliable, rapid and effective means of detecting metritis in animals in the field without the need for laboratory facilities.

Optoelectronic nose based on an origami paper sensor for selective detection of pesticide aerosols

This study introduces an applicable colorimetric sensor array for the detection of pesticides in the vapor phase. The array consisted of six metal nanoparticles spotted on the piece of filter paper. 3D-origami pattern was used for the fabrication of a paper-based sensor to decrease the effect of the nanoparticles leaching after exposure to analytes. Exposure to pesticide aerosols caused changes in the color of the array due to the aggregation of nanoparticles. These changes provided selective responses to thion pesticides such as malathion, parathion, chlorpyrifos, and diazinon. The sensing assay could also differentiate between aliphatic and aromatic thions and discriminate amine-containing compounds from the other studied analytes. These finding results are clearly confirmed by both visual detection and multivariate statistical methods. The proposed sensor was successfully developed for the quantitative measurement of pesticide aerosols at a very low concentration. The limit of detection of this method determined for malathion, parathion, chlorpyrifos and diazinon were 58.0, 103.0, 81.0 and 117.0, respectively. Moreover, the array could be employed to simultaneously analyze four studied pesticides. The statistcal results confirmed that the method has high performance for concurrent detection of thions as a major air pollutant without the interference of other species.

Carbon Nanotube-Based Chemical Sensors

The need to sense gases and vapors arises in numerous scenarios in industrial, environmental, security and medical applications. Traditionally, this activity has utilized bulky instruments to obtain both qualitative and quantitative information on the constituents of the gas mixture. It is ideal to use sensors for this purpose since they are smaller in size and less expensive; however, their performance in the field must match that of established analytical instruments in order to gain acceptance. In this regard, nanomaterials as sensing media offer advantages in sensitivity, preparation of chip-based sensors and construction of electronic nose for selective detection of analytes of interest. This article provides a review of the use of carbon nanotubes in gas and vapor sensing.