Polyaniline Composites Containing Eco-Friendly Biomass Carbon from Agricultural-Waste Coconut Husk for Enhancing Gas Sensor Performance in Hydrogen Sulfide Detection.

Hydrogen sulfide, a colorless, flammable gas with a distinct rotten egg odor, poses severe health risks in industrial settings. Sensing hydrogen sulfide is crucial for safeguarding worker safety and preventing potential accidents. This study investigated the gas-sensing performance of an electroactive polymer (i.e., polyaniline, PANI) and its composites with active carbon (AC) (i.e., PANI-AC1 and PANI-AC3) toward H2S at room temperature. PANI-AC composites-coated IDE gas sensors were fabricated and their capability of detecting H2S at concentrations ranging from 1 ppm to 30 ppm was tested. The superior gas-sensing performance of the PANI-AC composites can be attributed to the increased surface area of the materials, which provided increased active sites for doping processes and enhanced the sensing capability of the composites. Specifically, the incorporation of AC in the PANI matrix resulted in a substantial improvement in the doping process, which led to stronger gas-sensing responses with higher repeatability and higher stability toward H2S compared to the neat PANI-coated IDE sensor. Furthermore, the as-prepared IDE gas sensor exhibited the best sensing response toward H2S at 60% RH. The use of agricultural-waste coconut husk for the synthesis of these high-performance gas-sensing materials promotes sustainable and eco-friendly practices while improving the detection and monitoring of H2S gas in industrial settings.

On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data.

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert-Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time-frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.