Spektra: Jurnal Fisika dan Aplikasinya

Effect of 30˚C Electrolyte Temperature on The Sensitivity Cu/Ni

Moh. Toifur, Rizka Nuzul Islamiyati — Thu, 18 Apr 2024 16:25:45 +0700

Given the necessity of cryogenic storage and monitoring cryogenic temperatures is equally important. This study aims to determine the resistance value and sensitivity of copper wire before and after electroplating at 30˚C electrolyte temperature as a low temperature sensor. The electrolyte solution consists of NiSO₄ 260 g, NiCl₂ 60 g, H₃BO₃ 40 g and Aquades 1000 mL. Electroplating was carried out with an electrolyte temperature of 30˚C, electrode distance of 4 cm, voltage of 4.5 volts and plating time of 4 minutes. The plating results were analyzed to determine the resistance and sensitivity of the sensor at temperatures from 0 to -160˚C. The results showed that the resistance value of the Cu coil obtained R_Cu = (1.44 ± 0.00) ohm and the resistance of the Cu/Ni coil R_{Cu / Ni} = (1.50 ± 0.00) ohm. The resistance value on the Cu/Ni coil (after plating) is greater than the Cu coil (before plating). While the test results of sensor sensitivity show that Cu and Cu/Ni coils have properties as low temperature sensors. Sensor sensitivity increases after plating. The sensitivity value obtained by Cu coil is S(T) = -1E-06T + 6E-05 and Cu/Ni coil S(T) = -2E-06T + 2E-05. The projection sensitivity at a temperature of -200 ˚C obtained is 0.00046 V/˚C less than the Cu/Ni coil 0.00082 V/˚C. So nickel plating on copper coil at 30˚C electrolyte temperature has successfully improved the sensitivity value of the low-temperature sensor.

Pair Correlation Influence on Superconductors Josephson Penetration Depth

Fri, 19 Apr 2024 17:41:00 +0700

The Josephson penetration depth is an essential characteristic of Josephson junctions, serving a role akin to the London penetration depth in bulk superconductors. It originates from the substantial self-magnetic field generated by a strong Josephson supercurrent, influencing the distribution of the gauge invariant phase difference across the junction. This study delves into the intricate relationship between cooper pair correlation and critical temperature in superconductors. To study relationships authors develop theoretical method and observed that critical temperature exhibits a noteworthy decrease with an increase in cooper pair correlation. Specifically, as the level of coherence among electron pairs rises, the material's capacity to maintain the superconducting state at elevated temperatures is enhanced, resulting in an elevated critical temperature. Conversely, regions characterized by lower pair correlation demonstrate a sharp reduction in critical temperature, indicating their heightened susceptibility to changes in correlation levels. This sensitivity is particularly pronounced across junction and penetration depth where cooper pair correlation is diminished. Furthermore, the study reveals an exponential decay trend in critical temperature concerning cooper pair correlation, underscoring the pivotal role played by pair correlation in the superconducting state. Even slight alterations in pair correlation have a substantial impact on the material's ability to exhibit superconductivity. These findings provide valuable insights for the tailored design and optimization of superconducting materials for specific applications. By leveraging the understanding gained from this research, it becomes possible to engineer materials with enhanced superconducting properties. This study not only advances our fundamental comprehension of superconductivity but also offers practical implications for a diverse range of technological applications.

Development of a Real-Time Gas Concentration Measurement System Using Internet of Things-Based Monitoring

Mon, 22 Apr 2024 00:00:00 +0700

Transportation and industrial activities have contributed to an increase in the concentration of pollutant gases such as CO, NO₂, and SO₂ in the air. High concentrations of these gases can adversely affect human health. One approach to addressing this issue is by measuring and monitoring gas concentrations in the air. The advancement of technology, specifically the Internet of Things (IoT), facilitates the monitoring process. Therefore, this research focuses on the development of a gas concentration measurement system, utilizing the MQ-7 sensor for CO, the MiCS-6814 sensor for NO₂, and the MQ-136 sensor for SO₂. Additionally, the system is integrated with a website as a platform for monitoring the sensor measurements. The research results indicate that the system has been successfully developed with relative errors of 0.286% for the MQ-7 sensor, 0.325% for the MiCS-6814 sensor, and 0.280% for the MQ-136 sensor. The system underwent testing at three different locations, conducting gas concentration measurements in the environment for 24 hours. The environmental testing revealed measured gas concentration ranges of 2.52-7.67 PPM for CO, 0.00450-0.103 PPM for NO₂, and 0.0100-0.0652 PPM for SO₂. The measurement data is accessible and observed in real-time through the website, presented in graphical form, indicating average concentration values of CO, NO₂, and SO₂ over a 3-hour period. Moreover, the website is equipped with indicator lights that serve as alarms if the environmental gas concentration exceeds predefined thresholds.

Rainfall Classification Analysis Using Naïve Bayes Classifier Based on Air And Wind Temperatures in Serang City

Meilani Nisa Abdilah, Yayat Ruhiat, Yudi Guntara — Fri, 26 Apr 2024 18:16:46 +0700

The city of Serang experiences relatively high annual rainfall, with an average total of more than 100 mm/year. Based on data obtained from BMKG, Serang City, in 2023, there will be a shift in the rainy season due to weather anomalies, affecting the amount of rainfall. Apart from that, Serang City is one of the cities with less rain throughout September, recorded within 15 days. In this case, a determination is needed in the form of rainfall classification. However, an exemplary method is required in order to classify rainfall so that the classification results are accurate. Several studies showed that the naïve Bayes classifier method is the best classification method compared to other analysis methods; namely, it only requires a probability. The parameters used are air temperature and wind speed. So, this research aims to determine the classification of rainfall using the Naïve Bayes classifier based on air temperature and wind in Serang City. The method used is non-experimental quantitative with naïve Bayes classifier analysis. Based on the data analysis using Microsoft Excel software, the results showed that in the Serang city area, February 2024 was classified as a humid month with rainfall of 100 - 200 mm, and March 2024 was classified as a dry month with rainfall <100 mm.