Nina Suhaity Azmi

Carbonyl Functionalized Single Walled Carbon Nanotube-Hb Crosslinked Network: A Novel Platform for Studying Bio- Electrochemistry and Electrocatalysis of Hemoglobin

In this work, the direct electrochemistry of hemoglobin (Hb), which was immobilized on carbonyl functionalized single walled carbon nanotube (SWCNT) and deposited onto a gold (Au) electrode has been described. The synthesis of the network of crosslinked SWCNT/Hb was done with the help of crosslinking agent EDC (1-ethyl-3-(3-dimethylaminopropyl) carbodiimide). The UV-Vis and FTIR spectroscopy of SWCNT/Hb networks showed that Hb maintained its natural structure and kept good stability. In addition with this, scanning electron microscopy (SEM) illustrated that SWCNT/Hb networks had a featured layered structure and Hb being strongly liked with SWCNT surface. Cyclic voltammetry (CV) was used to study and to optimize the performance of the resulting modified electrode. The cyclic voltammetric (CV) responses of SWCNT/Hb networks in pH 7.0 exhibit prominent redox couple for the FeIII/II redox process with a midpoint potential of -0.46 V and -0.34, cathodic and anodic respectively. Furthermore, SWCNT/Hb networks are utilized for the detection of hydrogen peroxide (H2O2). Electrochemical measurements reveal that the resulting SWCNT/Hb electrodes display high electrocatalytic activity to H2O2 with high sensitivity, wide linear range, and low detection limit. Overall, the electrochemical results are due to excellent biocompatibility and excellent electron transport efficiency of CNT as well as high Hb loading and synergistic catalytic effect of the modified electrode toward H2O2.

Impact of incubation period on biodegradation of petroleum hydrocarbons from refinery wastewater in Kuantan, Malaysia by indigenous bacteria

ABSTRACT This work studied the biodegradation of petroleum hydrocarbons (PHCs) extracted from refinery wastewater to produce industrially important by-products at different incubation periods. Two out of 13 bacterial isolates, KRD2 and KRA4 were isolated. Dichloromethane was used to extract the PHC, and gas chromatography-mass spectrometry (GC-MS) analysis revealed that the refinery wastewater PHC was successfully biodegraded using the selected bacterial isolates within 15 days of incubation. Both KRD2 and KRA4 isolates degraded all 13 initially extracted PHC compounds within 5 days, except C13BD and C9BD, which produced 6 and 4 compounds as secondary metabolites with peak area percentages of 1.58, 1.38, 0.85, 29.94, 7.59, and 11.16% and 3.55, 2.88, 52.31, and 6.14%, respectively. These metabolites have been reported in industrial and medical applications. After 10 days, only 6 and 8 compounds were degraded by both isolates, respectively, and C11PAD compound was produced, as well as C5PAD, C7PAD, and C13PAD. After 15 days, it was clear that all the initial PHC compounds have been completely degraded by both isolates. Metabolites C5PAD, C6PAD, C8PAD, and C13PAD were produced by KRD2, and metabolites C5PAD, C6PAD, C8PAD, and C9PAD were produced by KRA4 at different peak areas. The alignment revealed that the KRA4 isolate was included in the genus Chryseobacterium gambrini, while KRD2 isolate was successfully identified as Mycobacterium confluentis using the Biolog microbial identification system. The incubation period evidently affected biodegradation process by indigenous degraders. These effective bacteria were shown to be of great potential for further application in biodegradation technology of PHC contaminated refinery wastewater to produce industrially important by-products.

Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics

A stochastic model is introduced to describe the growth of cancer affected by anti-cancer therapeutics of Chondroitin Sulfate (CS). The parameters values of the stochastic model are estimated via maximum likelihood function. The numerical method of Euler-Maruyama will be employed to solve the model numerically. The efficiency of the stochastic model is measured by comparing the simulated result with the experimental data.