Catalysts in fuel cells are normally platinum based because platinum exhibits high electrocatalytic activity towards ethanol oxidation in acidic medium. However, bulk Pt is expensive and rare in nature. To reduce the consumption of Pt, a support material or matrix is needed to disperse Pt on its surface as micro- or nanoparticles with potential application as anode material in direct ethanol fuel cells (DEFCs). In this study, a composite material consisting of platinum particles dispersed on reduced graphene oxide/poly(3,4-ethylenedioxythiophene) (RGO/PEDOT) support was electrochemically prepared for ethanol oxidation in sulfuric acid electrolyte. PEDOT, a conductive polymer, was potentiodynamically polymerized from the corresponding monomer, 0.10 M EDOT in 0.10 M HClO4electrolyte. The PEDOT-modified electrode was used as a substrate for exfoliated graphene oxide (EGO) which was prepared by electrochemical exfoliation of graphite from carbon rod of spent batteries and subsequently reduced to form RGO. The Pt/RGO/PEDOT composite gave the highest electrocatalytic activity with an anodic current density of 2688.7 mA·cm−2at E = 0.70 V (versus Ag/AgCl) towards ethanol oxidation compared to bare Pt electrode and other composites. Scanning electron microscopy (SEM) revealed the surface morphology of the hybrid composites while energy dispersive X-ray (EDX) confirmed the presence of all the elements for the Pt/RGO/PEDOT composite.
Detection of aldehydes such as pentanal, hexanal, octanal, and nonanal are studied with the use of nanostructured zinc oxide (ZnO) as sensing element. ZnO nanowires synthesized at optimized growth parameters using horizontal vapor phase growth (HVPG) technique was used due to its unique properties in gas sensing applications. Scanning Electron Microscope (SEM) and Energy Dispersive X-Ray (EDX) were used to verify the growth of ZnO nanowire structures. Further characterization using Source Meter was used to measure its resistance and resistivity based on the I-V graph. The sensor substrate wire set-up is connected to the Source Meter for resistance measurements as exposed to the different gas concentration of aldehydes. Gas sensing measurements were done at the static headspace gas concentration of the identified aldehydes. The sensor response of nanostructured ZnO-based gas sensor towards different gas concentrations ranges from 5.84% to 38.08%. Response time varies but it was observed that octanal gas has the longest response while pentanal has the fastest response.
A support vector machine classification algorithm was formulated to differentiate rubber cup coagulum according to the type of acid coagulant used. Two classification models were established, a binary classification algorithm and a model that can identify if formic, acetic, sulfuric acid, or no acid was used to induce coagulation. The models were based on the properties of the rubber cup coagulum that are easy to measure, such as tensile strength, water contact angle, and density. The binary classification model, which differentiates the industry-accepted formic acid-coagulated rubber cup coagulum from those which are not, exhibited satisfactory reliability, as evidenced by a 92% overall prediction accuracy and 71.4% cross-validation accuracy. Moreover, it was also determined that the rubber properties density, and water contact angle were important contributors for the classification. Acid-induced rubber coagulation is an important post-harvest process that influences the resulting rubber quality. Thus, the accurate differentiation of the rubber samples is useful for quality assurance purposes, as well as in policy enforcement.
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