Nor Azizah Parmin
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Nor Azizah Parmin
Official Name
Nor Azizah, Parmin
Alternative Name
Parmin, N. A.
Parmin, Nor Azizah
Parmin, Nor A.
Main Affiliation
Scopus Author ID
57195835481
Researcher ID
S-6303-2019

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  • Publication
    Novelty Studies on Amorphous Silica Nanoparticle Production from Rice Straw Ash
    ( 2020-07-09)
    Muhammad Nur Aiman Uda
    ;
    Subash Chandra Bose Gopinath
    ;
    Uda Hashim
    ;
    Muhammad Nur Afnan Uda
    ;
    Nor Azizah Parmin
    ;
    Halim N.H.
    ;
    Anbu P.
    Turning waste product into the valuable resources is the best alternative way to overcome the waste management issue. Generally, rice is grown and planted twice a year where a lot of rice by-products have been produced after harvesting the matured paddy. Rice straw is one of turning waste products into the valuable resources and to manage the environmental issues. Generally, rice is grown and planted twice a year where a lot of rice by-products are produced. Rices straw is one of the rice by-products, generated roughly 0.7-1.4 kg per kilograms of harvested milled rice. With the nanotechnological approach, silica particles at nano-size can be produced using the incinerated rice straw. In addition to that, this research will report the synthesis, characterization and adsorption analysis towards the heavy metal removal.
  • Publication
    Production and characterization of graphene from carbonaceous rice straw by cost-effect extraction
    ( 2021-05-01)
    Muhammad Nur Aiman Uda
    ;
    Subash Chandra Bose Gopinath
    ;
    Uda Hashim
    ;
    Halim N.H.
    ;
    Nor Azizah Parmin
    ;
    Muhammad Nur Afnan Uda
    ;
    Anbu P.
    This paper describes the synthesis of graphene-based activated carbon from carbonaceous rice straw fly ash in an electrical furnace and the subsequent potassium hydroxide extraction. The produced graphene has a proper morphological structure; flakes and a rough surface can be observed. The average size of the graphene was defined as up to 2000 nm and clarification was provided by high-resolution microscopes (FESEM and FETEM). Crystallinity was confirmed by surface area electron diffraction. The chemical bonding from the graphene was clearly observed, with –C=C– and O–H stretching at peaks of 1644 cm−1 and 3435 cm−1, respectively. Impurities in the graphene were found using X-ray photoelectron spectroscopy and energy dispersive X-ray spectroscopy. The measured size, according to zeta-potential analysis, was 8722.2 ± 25 nm, and the average polydispersity index was 0.576. The stability of the mass reduction was analyzed by a thermogravimetric at 100 Â°C, with a final reduction of ~ 11%.
  • Publication
    Silica and graphene mediate arsenic detection in mature rice grain by a newly patterned current–volt aptasensor
    ( 2021-12-01)
    Muhammad Nur Aiman Uda
    ;
    Subash Chandra Bose Gopinath
    ;
    Uda Hashim
    ;
    Halim N.H.
    ;
    Nor Azizah Parmin
    ;
    Muhammad Nur Afnan Uda
    ;
    Anbu P.
    ;
    Tijjani Adam
    Arsenic is a major global threat to the ecosystem. Here we describe a highly accurate sensing platform using silica nanoparticles/graphene at the surface of aluminum interdigitated electrodes (Al IDE), able to detect trace amounts of arsenic(III) in rice grain samples. The morphology and electrical properties of fabricated Al IDEs were characterized and standardized using AFM, and SEM with EDX analyses. Micrometer scale Al IDEs were fabricated with silicon, aluminum, and oxygen as primary elements. Validation of the bare Al IDE with electrolyte fouling was performed at different pH levels. The sensing surface was stable with no electrolyte fouling at pH 7. Each chemical modification step was monitored with current–volt measurement. The surface chemical bonds were characterized by fourier transform infrared spectroscopy (FTIR) and revealed different peaks when interacting with arsenic (1600–1000 cm−1). Both silica nanoparticles and graphene presented a sensitive limit of detection as measured by slope calibration curves at 0.0000001 pg/ml, respectively. Further, linear regression was established using ΔI (A) = 3.86 E−09 log (Arsenic concentration) [g/ml] + 8.67 E−08 [A] for silica nanoparticles, whereas for graphene Y = 3.73 E−09 (Arsenic concentration) [g/ml] + 8.52 E−08 on the linear range of 0.0000001 pg/ml to 0.01 pg/ml. The R2 for silica (0.96) and that of graphene (0.94) was close to the maximum (1). Modification with silica nanoparticles was highly stable. The potential use of silica nanoparticles in the detection of arsenic in rice grain extract can be attributed to their size and stability.
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