Indonesian Journal of Applied Physics最新文献

Graphene is a promising material for supercapacitors due to its unique properties, which influence the device's supercapacitor. This study aims to investigate the key factor of graphene properties in supercapacitors (, with the goal of improving their performance. Also, we observe the machine learning models for predicting capacitance of supercapacitor including four algorithms of machine learning: Linear Regression (LR), lazy IBK, Decision Table (DT), and Random Forest (RF). Machine learning model showed that the RF model demonstrated the highest correlation value of 0.745, surpassing other models. Also, the study revealed that graphene has a high specific surface area and highly porous structure, which enhanced the high capacitance values. Finally, these machine learning models are suitable to apply in materials sciences field for understanding the materials properties in supercapacitor.

We conducted first-principles Density Functional Theory (DFT) calculations using the CASTEP software package to investigate the crystal structure and mechanical properties of Fe³⁺-doped La_0.7Ba_0.3MnO₃ material at the Mn³⁺ site, with doping concentrations ranging up to 50%. Through geometry optimization, we simulated the X-ray diffraction (XRD) pattern. We observed that the doping of Fe did not result in a shift in the peak positions of the diffraction pattern. However, it led to an increase in intensity at the [012] peak and the splitting of peaks [104] and [110]. Regarding the mechanical properties, we examined the elastic constants and observed a reduction in the Bulk, Shear, and Young's modulus values. The Shear and Bulk modulus and Poisson's ratio indicated that La_0.7Ba_0.3Mn_(1-x)Fe_xO₃ becomes less ductile with increased Fe³⁺ doping content. Furthermore, we performed calculations for the Debye temperature, which revealed a decrease in the thermal conductivity of the La_0.7Ba_0.3Mn_(1-x)Fe_xO₃ material.

Liquid crystals are a type of substance that has solid and liquid properties. One of the types of it is cholesteric. Cholesteric liquid crystals have a characteristic which is called pitch. Pitch is very sensitive to changes in temperature. The pitch will reflect different colors depending on the wavelength at a particular temperature. Thermochromic Liquid Crystals (TLC) is the cholesteric liquid crystals material sold commercially. At the transitional temperature, the texture of TLC changes, so the reflected color will also change. Second-order feature extraction was chosen to determine the change in texture with the transition temperature. The TLC layer was made by thickness of 100 µm. This layer was heated and observed using a polarizing microscope with an angle between the polarizer and analyzer of 90^o. The obtained result are cross patterns emerged at anisotropic transition temperature and higher temperature on TLC will lead to an isotropic phase.

Research has been carried out to analyze the thickness of the sediment layer based on the value of the ground profile Vs (S-wave velocity) from microtremor measurement data sources in Solok City. The thickness of the sediment layer is one of the parameters that affect the amplification or amplification of incoming waves when an earthquake occurs. This study aimed to determine the sediment thickness level in Solok City based on the Vs value in the ground profile model from microtremor data sources. So that the analysis of sediment layers can be used as a form of disaster mitigation caused by tectonic activities such as earthquakes. Single station and array microtremor data were collected, then processed using a combination of HVSR and SPAC methods. The data processing results indicate that the value of S-wave velocity (Vs) derived from microtremor data analysis can be used to determine the thickness of the sediment layer (h), and vs values in Solok City ranged from 126.15-193.35 m/s with depths between 7.23-19.06 m. For areas with unseparated volcanic rock (QTau) lithology, the Vs value is 182.41 m/s. Meanwhile, for areas with geological conditions like alluvium (Qal), the Vs value is 161.66 m/s. The area with a thick layer of sediment, which is 62.74 m, is in the center of the northeastern part of Solok City, covering most of Vi Suku, Nan Balimo, and Javanese Village with alluvium (Qal) lithology and low topography through which rivers flow. Meanwhile, the thin layer of sediment, which is 23.12 m, is located in the western part of Solok City, precisely in Tanah Garam, with undivided volcanic rock lithology (QTau) and high topography in hilly areas.

Pneumonia has been detected using Machine learning. The stages in this study began with preprocessing in 4 stages: resizing, cropping, filtering using a high pass filter, and Adaptive Histogram Equalization. The feature extraction process continued with 22 Gray Level Co-occurrence Matrix (GLCM) features and classification using K-Nearest Neighbor (KNN). The image used was 150 data sets for training on the classification of 3 classes with a ratio of 50:50:50 while training on two classes was 50 bacterial pneumonia and 50 viral pneumonia. The most optimal training data accuracy results were obtained using the angle direction on the GLCM, namely 135o with the KNN classification (k = 3). For the classification of two classes Using 40 data sets, an accuracy of 91% was obtained, while testing for three classes with 60 data sets was 83.3%.

The geothermal phenomenon in Banyu Biru hot springs in Gondangwetan Village, Jatikalen District, Nganjuk Regency, has the potential to be developed into a tourist spot and an alternative renewable energy source that is environmentally friendly; for example, a geothermal power plant. So it is necessary to know the distribution of geothermal reservoirs and how much potential energy is contained. This research aims to determine the distribution of geothermal energy in the research area and its geological structure. This study used the Magnetic Method for secondary data obtained from NOAA satellite data. Data acquisition with an area of 2000 meters x 2000 meters obtained 100 data with a spacing of 200 meters. Based on research results, geothermal bursts have a low anomaly value of -50 nT to 25 nT. The low anomaly distribution can be used to determine the geothermal distribution in the area, assuming that areas with the same anomaly value indicate the presence of geothermal energy. The geology of the study area has five layers, namely: Topsoil (soil) has a susceptibility value of 0.0000377 SI, Alluvium has a susceptibility value of 0.00144513 SI, Tufan Clay has a susceptibility value of 0.00692407 SI, Limestone Tuff has a susceptibility value of 0.125713399 SI and Breccia (Andesite and Basalt) has a susceptibility value 0.0126292 SI. The depth of the geothermal source in the study area is ± 250 meters below the surface.

Acoustic energy harvester is a device used to convert environmental noise into electrical energy. Many researches on acoustic energy harvesting have been carried out, but most of them have not yet reached the stage of storing the electrical energy produced. This paper presents an experimental study of storing electrical energy generated by an acoustic energy harvester into a supercapacitor. The acoustic energy harvester in this study used a 4-inch woofer loudspeaker as a noise converter into electricity, equipped with a straight cylindrical resonator, a cylindrical housing, and an electric current rectifier unit. The supercapacitor used has a specification of 100F/2.7V. Experiments were carried out by using several variations of the sound frequency with three variations of sound pressure level (SPL) namely 90 dB, 95 dB, and 100 dB, and by measuring the supercapacitor voltage in a charging time of 60 minutes. It was found that the supercapacitor voltage reached 368 mV which was obtained from noise sound with an SPL of 100 dB and a frequency of 54 Hz which gave an initial charging electric current of about 12 mA. In the last five minutes of charging, the increase in supercapacitor voltage was still linear with time at a rate of about 5.2 mV/min. Therefore, the supercapacitor voltage can still significantly increase if the charging continues.

In this study, TiO₂, ZnO, and TiO₂/ZnO films were prepared under low calcination temperature and characterized to observe their properties related to photocatalytic performance. The samples were prepared by mixing the gel phase of ZnO precursor, TiO₂ anatase powder, triton-x 100, and acetylacetone to produce a paste form for the deposition process. The resulting paste was then deposited by screen printing onto a glass substrate and subjected to calcination at 250C to facilitate the ZnO crystallization and remove other additive materials. XRD analysis confirms that the formation of ZnO and TiO₂ crystals was assisted, although their crystallinity was lower than corresponding particulate forms. The lower crystallinity seems to be related by additive materials remains. The surface morphology of each sample was observed by scanning electron microscopy (SEM) imaging, Brunauer–Emmett–Teller (BET), and contact angle examination. Interestingly, both TiO₂ and ZnO layers tend to have a hydrophobic surface meanwhile TiO₂/ZnO has a hydrophilic surface. BET analysis revealed that ZnO has the highest specific surface area due to a nanosized. FTIR spectra confirmed the presence of appropriate chemical bonds in the ZnO and TiO₂ and other additive materials, such as alkyl groups. The photoluminescence (PL) spectrum shows a blue emission associated with intrinsic defects such as vacancies and interstitials of Zn and Ti in all samples. Differences in the photocatalytic performance of film and particulate form for each material were observed and analyzed. All samples' structures, morphology, and PL characteristics were then correlated to their photocatalyst behavior for methylene blue degradation.