No abstract

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.

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.

Metal oxide nanomaterials from bulk zinc oxide and tin oxide powders were synthesized via Horizontal Vapor Phase Crystal Growth deposition technique. Synthesized nanomaterials were deposited in a silica quartz tube where it acts as sensing element of the gas sensor. Volatile organic compounds (VOCs) were utilized as identifier for lung disease which served as analytes to interact with the gas sensor. The sensitivity test was measured using its response time and voltage difference. An immediate voltage response time with an average of 3 seconds was observed upon exposure of the analyte to the sensor. Selectivity test was analyzed through radar plots of tin oxide and zinc oxide which depicted that each metal oxides have distinct sensing capabilities in terms of sensed VOC gases.