Ardiyanti, Harlina and Mabarroh, Ni’matil and Wibowo, Nur Aji and Istiqomah, Nurul Imani and Tumbelaka, Rivaldo Marsel and Absor, Moh. Adhib Ulil and Suharyadi, Edi (2023) New design of a commercial chip-based GMR sensor with magnetite nanoparticles for biosensing applications. Artificial Intelligence in Agriculture.
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Abstract
The availability of rapid and low-cost instruments to detect magnetic nanoparticles (MNPs) concentrations
is vital in giant magnetoresistance (GMR)-based biosensors. This paper reports a new setup for a
simple GMR sensor using the commercial chip AAL024 as a transducer. It was combined with a basic
differential amplifier and microcontroller to acquire digital output voltages for the detection of greensynthesized
(GS)-Fe3O4 MNPs as a label and streptavidin-coated MNPs in biosensor applications. As a
characteristic feature of Fe3O4, the GS-Fe3O4 MNPs displayed a cubic inverse spinel structure. The average
GS-Fe3O4 particle size was 11 nm and they exhibited soft ferromagnetic behavior with a saturation
magnetization (MS) of 55.5 emu/g. Owing to the presence of phytochemical components in the Moringa
oleifera (MO) extract, the MS of GS-Fe3O4 was lower than that of Fe3O4. To study sensor performance, the
detection of the GS-Fe3O4 MNP labels and streptavidin-coated MNPs assay was investigated. Using the
microcontroller as the supply voltage for the AAL024 and an analog-to-digital converter simplified data
collection and made any additional measuring instruments unnecessary. The sensor showed promising
performance with the GS-Fe3O4 MNP label and streptavidin assay owing to the linear correspondence
between the signal and concentration of the MNP label. A small limit-of-detection of 4 mg/mL was
achieved for GS-Fe3O4. The sensitivity of GS-Fe3O4 and streptavidin were 2.79 and 1.80 mV/(mg/mL),
respectively. Moreover, the excellent stability and reproducibility of the sensor were confirmed by the
stable signal for over 30 s with relative signal deviation (RSD) ranges of 2e20% and 2e10% for MNPs and
streptavidin, respectively.
Item Type: | Other |
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Uncontrolled Keywords: | Classifier; Feature Combination; Multi-Layer Perceptron; Nutrient deficiency |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Mathematics and Natural Sciences > Physics Department |
Depositing User: | Masrumi Fathurrohmah |
Date Deposited: | 05 Jun 2024 07:44 |
Last Modified: | 05 Jun 2024 07:44 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/2408 |