Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor

Biobutanol is a promising candidate as the biofuel of the future. Four isomers exist, of which 1-butanol, isobutanol, and 2-butanol are the most promising candidates for biobutanol production. However, sensing isomers are slightly challenging due to the same molecular formula and molar mass. In this study, a sensor array system was built, which consists of three microgravimetric and three amperometric sensors coated with three room temperature ionic liquids with different pairs of cation and anion combinations, i.e., 1-ethyl-3-methylimidazolium acetate, 1-butyl-3-methylimidazolium bromide, and 1-hexyl-3-methylimidazolium chloride. In the microgravimetric sensor, quartz crystal microbalance coated with different sensing films was used to gain selectivity. In addition, the interdigitated electrode was selected where a microdroplet of room temperature ionic liquid was used for the electrolytes to substitute for the conventional bulky amperometric sensor. The different mechanisms of the two sensor types are expected to enhance the separation power among butanol isomers. The reproducibility and the concentration dependency were also studied to explore the sensor characteristics. A typical statistic parameter, the Wilks’ lambda, was used to calculate the discrimination power numerically from different sensor combinations. According to the numerical result, the lowest Wilks’ lambda with the numerical value of 4.82×10 ^-8 , where the separation among butanol isomers was enhanced, was achieved from two type sensor combinations, i.e., quartz crystal microbalance with three sensing films and amperometric sensor with the electrolyte of 1-ethyl-3-methylimidazolium acetate.

Miniaturized sensors possess many advantages, such as rapid response, easy chip integration, a possible lower concentration of target compound detection, etc. However, a major issue reported is a low signal response. In this study, a catalyst, the atomic gold clusters of Au_n where n = 2, was decorated at a platinum/polyaniline (Pt/PANI) working electrode to enhance the sensitivity of butanol isomers gas measurement. Isomer quantification is challenging because this compound has the same chemical formula and molar mass. Furthermore, to create a tiny sensor, a microliter of room-temperature ionic liquid was used as an electrolyte. The combination of the Au₂ clusters decorated Pt/PANI and room temperature ionic liquid with several fixed electrochemical potentials was explored to obtain a high solubility of each analyte. According to the results, the presence of Au₂ clusters increased the current density due to electrocatalytic activity compared to the electrode without Au₂ clusters. In addition, the Au₂ clusters on the modified electrode had a more linear concentration dependency trend than the modified electrode without atomic gold clusters. Finally, the separation among butanol isomers was enhanced using different combination of room-temperature ionic liquids and fixed potentials.

The Amperometric Gas Sensor (AGS) uses an electrode as the transducer element which converts its signal into a current from the electrochemical reaction of analytes taking place at the electrode surface. Many attempts to improve AGS performance, such as modifying the working electrode, applying a particular gas-permeable membrane, and selecting the proper electrolyte, etc., have been reported in the scientific literature. On the other hand, in the materials community, atomic gold has gained much attention because its physicochemical properties dramatically differ from those of gold nanoparticles. This paper provides an overview of the use of atomic gold in AGSs, both in a bulky AGS and a miniaturized AGS. In the miniaturized AGS, the system must be redesigned; for example, the aqueous electrolyte commonly used in a bulky AGS cannot be used due to volatility and fluidity issues. A Room Temperature Ionic Liquid (RTIL) can be used to replace the aqueous electrolyte since it has negligible vapor pressure; thus, a thin film of RTIL can be realized in a miniaturized AGS. In this paper, we also explain the possibility of using RTIL for a miniaturized AGS by incorporating a quartz crystal microbalance sensor. Several RTILs coated onto modified electrodes used for isomeric gas measurement are presented. Based on the results, the bulky and miniaturized AGS with atomic gold exhibited a higher sensor response than the AGS without atomic gold.

Indonesia is the world’s largest producer of palm oil. To preserve its competitive advantages, the Indonesian oil palm sector must expand high-quality palm oil output. In oil palm quality control, the water content is a crucial parameter as it can be used as a reference to determine the right harvest time. Thus, this study proposed a near-infrared (NIR) spectroscopy as a fast and non-destructive analysis to assess oil palm water content. NIR spectra were processed using Shannon entropy to describe the characteristics at each wavelength. In this study, oil palm fruit samples at various maturity levels were collected with eight different maturity fractions. Based on the analysis, the Shannon entropy value is closely related to any changes in the water content of palm oil. The entropy value has a decreasing trend as the water content increases. The proposed technique can predict the water content of an oil palm with satisfactory performance with values of 0.9746 of coefficient of determination (R2) and 2,487 of root mean square error (RMSE). Application of this model will lead to a fast and accurate prediction system related to oil palm water content.

The cash flow analysis is essential to examine the economic flows in the financial system. In this paper, the financial dataset at Bank Rakyat Indonesia was used, it recorded the sources of cash inflow and outflow during a particular period. The univariate time series model like the autoregressive and integrated moving average is the common approach to build the prediction based on the historical dataset. However, it is not suitable to estimate the multivariate dataset and to predict the extreme cases consisting of nonlinear pairs between independent-dependent variables. In this study, the comparison of using two types of models i.e., transfer function and artificial neural network (ANN) were investigated. The transfer function model includes the coefficient of moving average (MA) and autoregressive (AR), which allows the multivariate analysis. Furthermore, the artificial neural network allows the learning paradigm to achieve optimal prediction. The financial dataset was divided into training (70%) and testing (30%) for two types of models. According to the result, the artificial neural network model provided better prediction with achieved root mean square error (RMSE) of 0.264897 and 0.2951116 for training and testing respectively.