Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Arif KSubject: search.filters.subject.0301 Analytical Chemistry

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    Methamphetamine detection using nanoparticle-based biosensors: A comprehensive review
    (Elsevier BV, 2022-12) Lal K; Noble F; Arif K
    Drug abuse is a global issue, requiring diverse techniques for recognition of drug of interest. One such illicit drug that is abused worldwide is Methamphetamine (METH). It is an addictive and illicit substance that severely affects the central nervous system. Similar to many other illicit substances, recognition of METH in biological fluids and in more diverse matrices such as wastewater, is a topic of great interest to the government and law enforcement agencies. With the rise of nanotechnology that relies on exploiting the properties of certain materials at a scale down to their nanometer range in conjunction with aptamers, molecularly imprinted polymers as well as antibodies have gained much attention over the last decade. The scope and appositeness of nanomaterials have significant characteristics that are highly suitable for recognition of illicit chemical compounds such as METH. This comprehensive review focuses on the detection of METH using nanoparticles in real world samples such as biological fluids and wastewater, while discussing varieties of materials used as nanoparticles and that aid in its recognition. It also offers insights into future opportunities and challenges that come with the use of nanotechnology in sensing applications.
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    Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network
    (MDPI AG, 11/01/2023) Ali S; Alam F; Arif K; Potgieter J-G
    The advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.
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    Low-Cost Sensor for Continuous Measurement of Brix in Liquids
    (MDPI AG, 25/11/2022) Jaywant SA; Singh H; Arif K
    This paper presents a Brix sensor based on the differential pressure measurement principle. Two piezoresistive silicon pressure sensors were applied to measure the specific gravity of the liquid, which was used to calculate the Brix level. The pressure sensors were mounted inside custom-built water-tight housings connected together by fixed length metallic tubes containing the power and signal cables. Two designs of the sensor were prepared; one for the basic laboratory testing and validation of the proposed system and the other for a fermentation experiment. For lab tests, a sugar solution with different Brix levels was used and readings from the proposed sensor were compared with a commercially available hydrometer called Tilt. During the fermentation experiments, fermentation was carried out in a 1000 L tank over 7 days and data was recorded and analysed. In the lab experiments, a good linear relationship between the sugar content and the corresponding Brix levels was observed. In the fermentation experiment, the sensor performed as expected but some problems such as residue build up were encountered. Overall, the proposed sensing solution carries a great potential for continuous monitoring of the Brix level in liquids. Due to the usage of low-cost pressure sensors and the interface electronics, the cost of the system is considered suitable for large scale deployment at wineries or juice processing industries.