Fauzan Khairi Che Harun

Analytical modelling of monolayer graphene-based ion-sensitive FET to pH changes

Graphene has attracted great interest because of unique properties such as high sensitivity, high mobility, and biocompatibility. It is also known as a superior candidate for pH sensing. Graphene-based ion-sensitive field-effect transistor (ISFET) is currently getting much attention as a novel material with organic nature and ionic liquid gate that is intrinsically sensitive to pH changes. pH is an important factor in enzyme stabilities which can affect the enzymatic reaction and broaden the number of enzyme applications. More accurate and consistent results of enzymes must be optimized to realize their full potential as catalysts accordingly. In this paper, a monolayer graphene-based ISFET pH sensor is studied by simulating its electrical measurement of buffer solutions for different pH values. Electrical detection model of each pH value is suggested by conductance modelling of monolayer graphene. Hydrogen ion (H+) concentration as a function of carrier concentration is proposed, and the control parameter (Ƥ) is defined based on the electro-active ions absorbed by the surface of the graphene with different pH values. Finally, the proposed new analytical model is compared with experimental data and shows good overall agreement.

A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation

Accurately differentiating between ventricular fibrillation (VF) and ventricular tachycardia (VT) episodes is crucial in preventing potentially fatal misinterpretations. If VT is misinterpreted as VF, the patient will receive an unnecessary shock that could damage the heart; conversely, if VF is incorrectly interpreted as VT, the result will be life-threatening. In this study, a new method called semantic mining is used to characterize VT and VF episodes by extracting their significant characteristics (the frequency, damping coefficient and input signal). This newly proposed method was tested using a widely recognized database provided by the Massachusetts Institute of Technology (MIT) and achieved high detection accuracy of 96.7%. The semantic mining technique was capable of completely discriminating between normal rhythms and VT and VF episodes without any false detections and also distinguished VT and VF episodes from one another with a recognition sensitivity of 94.1% and 95.2% for VT and VF, respectively.

Conductance modulation of charged lipid bilayer using electrolyte-gated graphene-field effect transistor

Graphene is an attention-grabbing material in electronics, physics, chemistry, and even biology because of its unique properties such as high surface-area-to-volume ratio. Also, the ability of graphene-based materials to continuously tune charge carriers from holes to electrons makes them promising for biological applications, especially in lipid bilayer-based sensors. Furthermore, changes in charged lipid membrane properties can be electrically detected by a graphene-based electrolyte-gated graphene field effect transistor (GFET). In this paper, a monolayer graphene-based GFET with a focus on the conductance variation caused by membrane electric charges and thickness is studied. Monolayer graphene conductance as an electrical detection platform is suggested for neutral, negative, and positive electric-charged membrane. The electric charge and thickness of the lipid bilayer (QLP and LLP) as a function of carrier density are proposed, and the control parameters are defined. Finally, the proposed analytical model is compared with experimental data which indicates good overall agreement.

Characterization of Ventricular Tachycardia and Fibrillation Using Semantic Mining

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are potentially life-threatening forms of cardiac arrhythmia. Fast and accurate detection of these conditions can save lives. We used semantic mining to characterize VT and VF episodes by extracting three significant parameters (frequency, damping coefficient and input signal) from electrocardiogram (ECG) signal. This method was used to analyze four-second ECG signals from a widely recognized database at the Massachusetts Institute of Technology (MIT). The method achieved a high sensitivity and specificity of 96.7% and 98.3%, respectively, and was capable of detecting normal sinus rhythm (N) from VT and VF signals without false detection, with a sensitivity of 100%. VT and VF signals were recognized from each other, with a recognition sensitivity of 96% and 94%, respectively. This newly proposed method using semantic mining shows strong potential for clinical applications because it is able to recognize VT and VF signals with higher accuracy and faster recognition times compare to existing methods.

Integration of electrochemical detection into micropumps for continuous monitoring system

This paper proposes the integration of electrochemical detection to micropumps for continuous monitoring of ferrocyanide ions. A homemade device is fabricated using 3D printing and casting techniques with a top and bottom layer of 0.5 mm and 3.0 mm, respectively. The volumetric flow and cyclic voltammetry performances of the device are evaluated at the actuator frequency of 10-50 Hz, in 10 Hz increments at concentrations of 2-10 mM, in 2 mM increments. The fabricated device exhibits maximum flow rate of 0.30405 ml/min in the reverse direction with resonance frequency of 20 Hz. The oxidation peak current at 16.02018 μA of 10 mM potassium ferrocyanide in 1 M potassium nitrate is noted. Among the various concentrations of potassium ferrocyanide, 10 mM of potassium ferrocyanide in 1 M potassium nitrate at the maximum flow rate shows the highest oxidation peak current at 16.02018 μA. The presented device offers the advantage of an affordable miniaturized bedside patient continuous monitoring system, due to its low fabrication cost. Additionally, the disposability nature of the device assures collected samples are unpolluted.

Silver nanoparticles stamping for the production of fabrics-based bio-MEMS

This paper describes the developments of Bio-MEMS using fabric as the structural components. The use of fabrics for Bio-MEMS construction significantly simplifies the conventional fabrication process. In this paper we proposed a new fabrication process for fabrics-based Bio-MEMS. In addition, to the simple and low-cost fabrication process, stamping method offers a number of advantages which is less complicated and less of procedure steps during the fabrication process as a result reducing fabrication time and less amount of nanomaterial used. We demonstrate the gauge factor for fabrics-based Bio-MEMS is 7.424 which are results in higher sensitivity. By utilizing the concept of MEMS technology, fabrics-based Bio-MEMS will be recommended to become employed for activity monitoring and control especially in rehabilitations therapy.