QCM or quartz crystal microbalance is a well-known sensor technology that generates cycles of oscillation related to mass change on the crystal’s surface. This crystal works well when it has a frequency counter and an oscillator to drive the crystal and count the oscillation, and a good airflow regulator. This study developed a measurement system for aerosol concentrations with a diameter of less than 2.5 micrometers. The system consists of QCM sensors, an oscillator, a frequency counter, and an airflow regulator. The system was tested inside an exposure chamber with a constant emission source for the different velocity speeds, namely v 1 , v 2 , v 3 , v 4 , and v 5 . The test was conducted every10seconds due to the saturated time of the QCM related to the mass loading effect of aerosol. The results show that the system can drive the QCM sensor with a frequency of 5MHz. The measurement system works well to measure aerosol concentrationafter the preload duration often seconds and every sixty seconds in which the durations are related to the optimum QCM’s response at v 1 and v 2 . The optimum performance was found to be in the laminar regime, with the sample rate of 0.6 m/s to 1.0 m/s.

Particulate matters with the diameter less than 2.5 µm or PM2.5, have been known to the health adverse. The developing of a measurement system of PM2.5 with a high precision has become a challenge in the last decade. We design the system using a NOVA SDS011 sensor to measure PM2.5 concentration. The problem is that the sensor has a capacity to measure particulate matter in the range of 0.3 to 10 µm, meanwhile we would like to design the measurement system of PM2.5 with the high precision. Another this that we address is how to optimize the sensor. A factor influenced the sensor optimization is a sample compartment. In this paper, we present the PM2.5 measurement system with the different compartments. The PM2.5 measurement system was calibrated using the 3443 Kanomax dust monitor. The result shows that the system works well with the compartment is important factor to increase the precision.

Bioaerosols are the biological constituent of PM (particulate matter). Bioaerosols are produced during many activities in landfills, agricultural sectors, food preservation, and many others in daily life [1,2]. Bioaerosols can be generated from biomass burning activity, resulting in bioaerosols with a diameter <2.5 µm (fine bioaerosols) [2]. Bioaerosols commonly have many forms like fungal spores, pollen, bacteria, and even viruses. As confirmed before, bioaerosols consist of Aspergillus, Alternaria, and Cladosporium species [4,5]. Another previous study investigated bioaerosols from bacteria species in landfill areas, such as Staphylococcus aureus, Staphylococcus gordonii, Alloiococcus otitis, Kocuria rosea, Pediococcus pentosaceus, and many others [1].

Power quality becomes a severe problem due to the increasing use of nonlinear electrical loads, complex electric power systems on smart grids, inverters in renewable power plants, and electronic control equipment. Power quality problems include variations in voltage or current such as sag, swell, flicker, spike, overvoltage, undervoltage, interruption, transient, harmonics, and frequency fluctuations. Research on power quality disturbances mostly applies signal processing and transformation methods such as Fast Fourier transform, S-transform, and Wavelet. In this paper, we use empirical mode decomposition methods and statistical parameters to analyze power quality disturbance signals. It gives more detailed characteristics of power quality disturbances. We conducted a six-step analysis to get a percentage of each power quality disturbance signal. The developed method provides a preliminary description of the power quality characteristics from the percentage values of RMS, range, and energy levels at the first IMF. The positive percentage indicates the existence of power quality disturbances contains flicker and swell, while negative indicates contain harmonics, sag, and transient.

TiO2 synthesis using sonochemical methods and their coating on the surface of the Quartz Crystal Microbalance (QCM) sensor utilizing the spin coating method has been carried out. The synthesis started by mixing 7 ml of titanium (IV) isopropoxide (TTIP) (97%) in 70 ml of ethanol (96%). The mixture of TTIP solution was stirred for 30 minutes. After ward, the sonication was carried out for 4 hours at a frequency of 40 kHz and heated for 16 hours at 80 °C. To produce TiO2 powder, the precursor was calcined at 500°C for 3 hours. The TiO2 nanoparticle was analyzed using XRD, and the particle size was observed using SEM/Eds. Based on the XRD results, the crystal structure of Anatases was obtained at the 2θ (deg) of 25.503°(101), 29.56° (103), 36.20° (004), 37.977° (112), 48. 25.9° (200), 54.100° (105), 55.272° (211), 62.89°. The particle size from the SEM results showed the average particle size in the range of ±7μm-±54 nm with the majority in ±96 nm. The obtained TiO2 was dissolved with ethanol then the solvent was deposited on the QCM surface using the spin coating method. A homogeneous and porous surface structure is obtained from the SEM observation. The coated QCM was tested for its ability to adsorb CO2 gas. QCM was exposed to CO2 gas for 100 seconds with a constant exposure discharge. A positive response was obtained for the adsorbability of the coated QCM to adsorb CO2 gas with time variations. TiO2’s response against CO2 gas adsorption indicates that TiO2 can be further investigated as a CO2 gas sensor. The measuring response of the TiO2 layer on the QCM sensor was conducted to adsorb CO2 gas.