Applied Mechanics and Materials Vol. 918

Abstract: Silicon on insulator (SOI) wafer has allowed the integrated circuit (IC) industry to create superior, high-performance solutions. In addition, doping techniques are vital in the silicon sector due to the need to regulate the material electrical properties. The spin on dopant (SOD) approach is an alternative method that involves spinning a solution containing dopant onto SOI wafers. This research aims to determine the impact of thermal diffusion temperature and soaking time on sheet resistance of doped SOI wafer using SOD approach. Additionally, the homogeneity of doping was studied by utilizing mapping techniques. Three inches boron-doped SOI wafers were cut and cleaned according to Radio Corporation of America (RCA) standards. N-type dopants of Filmtronics SOD P509 were deposited on SOI wafer by using a spin coater, for 40 seconds at 4,000 revolutions per minute (rpm). The thermal diffusion temperature and soaking time were set between 700°C to 1000°C for 30 to 120 minutes. After thermal diffusion, hydrofluoric acids (HF) were diluted and used to etch samples. All materials were evaluated using a four-point probe, Hall Effect and Atomic Force Microscope (AFM). The results show that when the thermal diffusion soaking time increases, sheet resistance decreases until activated dopants are saturated. When sheet resistance decreases, dopant concentration rises. Temperature and soaking time increase carrier density and surface roughness, while decreasing Hall mobility. From mapping techniques, it shows low non-uniformity value which less than 10% suggests good thermal diffusion control.

Abstract: Lithium-ion batteries like LiFePO4 become a new choice for electrical energy sources in the world and can be used on electric vehicles. Battery packs monitoring by Battery Management System in electric vehicles require accurate monitoring. The inaccuracy of monitoring such property can lead to low safety, low efficiency and battery’s life reduction. Estimating state of charge (SoC) to prevent battery damage from overcharging and over discharging. Some of the methods used to estimate SoC such as Coulomb Counting have errors during the charge and discharge process. This research proposes a counting method for measuring SoC with the artificial neural networks (ANN) to provide more precise estimation. Feed Forward Neural Network (FFNN) is an ANN model that can give an accurate estimation of SoC by learning data of the charge-discharge process performed on sample batteries. The sample batteries are tested with a battery analyzer to get its charging-discharging data consisting of variables such as voltage, current, capacity, and time with C-Rate variations. These variables data are then learned by the modeled FFNN to predict SoC value. The FFNN model consisted of 16 neurons in the first layer, 8 neurons in the second layer, and 4 neurons in the third layer. The predicted SoC value from FFNN has a similar value with its real SoC value. The relationship between SoC and battery voltage is plotted in a curve and shows an identical characteristic with how the SoC-Voltage curve of a battery should be and have a low mae value. This FFNN model can be applied further such as in electric vehicles to maintain its safety and for longer use.

Abstract: This study aims to develop an Internet of Things (IoT) based Battery Management System that monitors battery operation and performance in real-time. They used an Agile Methodology to build a BMS Monitoring System, using the Vue js framework and Laravel. The battery monitoring system provides information on battery temperature, current, and voltage data, essential for monitoring battery conditions below or over limits. The interface is accessible via desktop and mobile devices. This research resulted in real-time temperature (temperature on sensor 1, temperature on sensor 2, temperature on sensor 3, and maximum temperature limit) , current (maximum current limit, current total, and minimum current limit) , and voltage (maximum voltage limit, total voltage, and minimum voltage limit) monitoring based on certain time intervals.

Abstract: The development of lithium batteries as an energy storage system is getting higher equal to the development of eco-friendly energy needs. However, lithium batteries have disadvantages in electrical and temperature interference. Series and parallel configuration causes voltage imbalance and leads to degradation performance of the battery. The focus of the research is the development of BMS with voltage monitoring and balancing features for the 12-series battery pack configuration. Monitoring can be done by observing electrical parameters, are cell voltage and battery temperature. The results of the simulation and modeling of BMS and Lithium-ion Battery show that the flat-zone voltage on the LFP UNS battery is in the 10-90% SoC range (generally SoC 20-80%), and the characteristics of lithium battery are current affects the battery voltage curve (high current causes a high voltage drop), while temperature affects the internal resistance (low temperature causes an increase in internal resistance). The BMS hardware monitoring test shows the accuracy and precision of the voltage sensor at 99.7064% and 99.9998%, while the temperature sensor performs the accuracy and precision of 95.4909% and 100%, respectively. The passive balancing method with Switched Shunt Resistor shows a nominal balancing current of about 170mA with a 20mV voltage drop.

Abstract: The use of rechargeable batteries to store and distribute extra energy from photovoltaics (PV) improves the efficiency of solar energy generation. This study constructs a solar power plant system that is linked to the grid network and includes battery energy storage. The efficiency of a hybrid solar power plant with integrated batteries and grid energy storage is demonstrated and evaluated in this study. This study is based on real-time testing to determine battery utilization when different electrical loads for office use are combined. The workload profile during peak hours of use, which correspond to the peak time of sunshine, is used to evaluate the system. The collected findings show that the degree of effectiveness of this hybrid power plant fulfills the power simulation. During the peak irradiation time, the maximum power is 2100 watts, whereas the needed power simulation, which is 1900 watts in this case, so the power efficiency percentage is 110.52%. It means that PV can satisfy the charged power while also supplying extra power to the battery for usage at low periods. During the 8-hour test, the calculation of cost savings revealed a savings in electricity expenditure of Rp. 14,373. The energy storage system's real operational needs were met by battery storage and PV.

Abstract: Infectious disease such as COVID-19 is one of the major concerns in Malaysia as it becomes the second killing disease and causes huge number of death and spread to other regions of the world if left unchecked. In developed countries, infectious diseases are often preventable, but lack of medical devices in detecting it makes the death cases increase. The growth of different COVID-19 mutation has given so much challenges in detecting, preventing and curing. This gives motivation to researchers in order to solve this global problem by creating and advancing the detection tools and methods. Time, equipment availability, and the biological nature of COVID-19 influence the selection of appropriate detection techniques. This paper summarizes the comprehensive review on the type of diagnostic tests and biosensors available in detecting COVID-19 disease.

Abstract: Heavy metal elements are elements with comparatively high density and are dangerous even in a minimal quantity as they can persist in the environment. The electrochemical sensor can play an essential role in detecting heavy metals. However, the electrochemical sensor has drawbacks, such as low sensitivity and a high detection limit. Bismuth nanoparticles (BiNPs) can improve the sensitivity and lower the detection limit of an electrochemical sensor by modifying the working electrode. In this study, BiNPs produced by the hydrothermal method were drop-casted on the indium-tin-oxide (ITO) coated with polyethene terephthalate (PET) film (BiNPs/ITO-PET). The effect of the hydrothermal reaction was studied by varying the hydrothermal reaction period (5, 6, 7, and 8 h). X-ray Diffraction (XRD) was used to characterize the phase presence, and the morphology of BiNPs was characterized using a transmission electron microscope (TEM). The BiNPs/ITO-PET electrode was subjected to electrochemical characterization using cyclic voltammetry (CV) and the detection of Pb(II) using differential pulse anodic stripping voltammetry (DPASV). The BiNPs/ITO-PET electrode showed good electrochemical performance in detecting Pb(II).

Abstract: The extensive use of pesticides can result in overexposure and soil, water, and produce residues. For instance, residues of malathion were found on some vegetables. Molecularly-imprinted polymers (MIP) have been recently developed for sensing of pesticide residues. This study prepared malathion-imprinted polymers via precipitation polymerization and deposited on quartz crystal microbalance (QCM) electrodes. FTIR spectroscopy proved the incorporation and removal of malathion in the matrix of MIP. SEM images revealed that MIP particles are larger than the non-imprinted polymer (NIP) particles due to the incorporation of malathion. Binding experiments were done using standard malathion solutions of 10 to 60 ppm. The MIP-QCM sensor had a greater response than the NIP-QCM sensor. This is due to the specific binding sites in the MIP matrix. On the other hand, the response of NIP-QCM sensor is attributed to the non-specific adsorption sites in its matrix. A sensitivity and detection limit of 1.62 Hz·L/mg and 5.67 ppm, respectively were determined for the MIP-QCM sensor. Lastly, the MIP-QCM sensor is stable and reusable up to three (3) cycles.

Abstract: An acetabular implant is a cup-shaped implant that wraps around the head of the femur at the hip joint. Severe damage to the acetabular implant results in bone turnover. To meet the needs of implants, appropriate implant manufacturing techniques are needed. Investment casting is the most widely used casting method because it has the best dimensional accuracy. To help reduce production costs in the industry, this research was carried out using the ProCast 2018 Software. In this study using the 2018 ProCast Software with investment casting techniques with CoCrMo material and variations in the orientation of the mold pattern, namely 0^o, 45^o, 90^o and variations in the shape of the sprue including straight sprue , tapper sprue, and reverse tapper sprue. From these variations, the most optimal result is the tapper sppue variation with 0^o print pattern orientation. With the results of the analysis related to the temperature distribution that occurs, fluidity, solidification process and the most optimal shrinkage porosity.

Abstract: Damage or a loss of strength in this hip joint can occur as a result of calcification, aging, the development of illnesses such as osteoporosis, arthritis, and bone cancer, and it can also be permanently destroyed by accidents. As a result, an artificial hip prosthesis can be implanted to prevent undesirable outcomes. Throughout the jumping, running, and walking cycles, the hip joint is the most essential load-bearing and shock-absorbing component in the lower half body. As a result, using a finite element analysis technique, this work simulates the design of an Artificial Hip Joint with holes and thickness as variables, using CoCrMo acetabular implant material. ANSYS 19.1 software with transient structural characteristics will be used to simulate providing the load with the activity of climbing stairs. According to the findings of this study, the acetabular design with a thickness of 3 mm and 5 holes is the most optimal. This is due to the design's distribution of stress, strain, and total deformation being the most ideal and having a relatively low weight with appropriate usage period and safety factor forecasts.