ORCID Profile
0000-0001-7010-8285
Current Organisation
Manchester Metropolitan University
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Publisher: IOP Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 03-02-2023
DOI: 10.3390/EN16031537
Abstract: Sluggish oxygen reduction reaction (ORR) of electrodes is one of the main challenges in fuel cell systems. This study explored the kinetics of the ORR reaction mechanism, which enables us to understand clearly the electrochemical activity of the electrode. In this research, electrocatalysts were synthesized from platinum (Pt) catalyst with multi-walled carbon nanotubes (MWCNTs) coated by three polymers (polybenzimidazole (PBI), sulfonated tetrafluoroethylene (Nafion), and polytetrafluoroethylene (PTFE)) as the supporting materials by the polyol method while hexachloroplatinic acid (H2PtCl6) was used as a catalyst precursor. The oxygen reduction current of the synthesized electrocatalysts increased that endorsed by linear sweep voltammetry (LSV) curves while increasing the rotation rates of the disk electrode. Additionally, MWCNT-PBI-Pt was attributed to the maximum oxygen reduction current densities at −1.45 mA/cm2 while the minimum oxygen reduction current densities of MWCNT-Pt were obtained at −0.96 mAcm2. However, the ring current densities increased steadily from potential 0.6 V to 0.0 V due to their encounter with the hydrogen peroxide species generated by the oxygen reduction reactions. The kinetic limiting current densities (JK) increased gradually with the applied potential from 1.0 V to 0.0 V. It recommends that the ORR consists of a single step that refers to the first-order reaction. In addition, modified MWCNT-supported Pt electrocatalysts exhibited high electrochemically active surface areas (ECSA) at 24.31 m2/g of MWCNT-PBI-Pt, 22.48 m2/g of MWCNT-Nafion-Pt, and 20.85 m2/g of MWCNT-PTFE-Pt, compared to pristine MWCNT-Pt (17.66 m2/g). Therefore, it can be concluded that the additional ionomer phase conducting the ionic species to oxygen reduction in the catalyst layer could be favorable for the ORR reaction.
Publisher: MDPI AG
Date: 08-06-2022
DOI: 10.3390/S22124358
Abstract: Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim’s blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim’s blood s les, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.
Publisher: Elsevier BV
Date: 05-2022
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Julfikar Haider.