An experimental investigation of spin-on doping optimization for enhanced electrical characteristics in silicon homojunction solar cells: Proof of concept.

The pursuit of enhancing the performance of silicon-based solar cells is pivotal for the progression of solar photovoltaics as the most potential renewable energy technologies. Despite the existence of sophisticated methods like diffusion and ion implantation for doping phosphorus into p-type silicon wafers in the semiconductor industry, there is a compelling need to research spin-on doping techniques, especially in the context of tandem devices, where fabricating the bottom cell demands meticulous control over conditions. The primary challenge with existing silicon cell fabrication methods lies in their complexity, cost, and environmental concerns. Thus, this research focuses on the optimization of parameters, such as, deposition of the spin on doping layer, emitter thickness (Xj), and dopant concentration (ND) to maximize solar cell efficiency. We utilized both fabrication and simulation techniques to delve into these factors. Employing silicon wafer thickness of 625 µm, the study explored the effects of altering the count of dopant layers through the spin-on dopant (SOD) technique in the device fabrication. Interestingly, the increase of the dopant layers from 1 to 4 enhances efficiency, whereby, further addition of 6 and 8 layers worsens both series and shunt resistances, affecting the solar cell performance. The peak efficiency of 11.75 % achieved in fabrication of 4 layers dopant. By using device simulation with wxAMPS to perform a combinatorial analysis of Xj and ND, we further identified the optimal conditions for an emitter to achieve peak performance. Altering Xj between 0.05 µm and 10 µm and adjusting ND from 1e+15 cm-3 to 9e+15 cm-3, we found that maximum efficiency of 14.18 % was attained for Xj = 1 µm and ND = 9e+15 cm-3. This research addresses a crucial knowledge gap, providing insights for creating more efficient, cost-effective, and flexible silicon solar cells, thereby enhancing their viability as a sustainable energy source.

Predictive analysis of the power spectral irradiance from blackbody radiation source using single pixel detector.

Accurate spectral irradiance measurement in the near-infrared range is significant for the design and characterization of photodetector and photovoltaic cells. Approximation method is commonly used to solve for the input power using estimated spectral irradiance, where the dependency on wavelength and temperature remains uncertain. This study aims to determine the power spectrum at different radiation temperatures using a single pixel photodetector, taking into consideration factors such as transmission spectra of alumina radiator, CaF2 collimating lens, responsivity, and measured photocurrent information of photodetectors. Utilizing predictive mathematical model, five commercial photodetectors, including Silicon, Germanium, In0.53Ga0.47As, In0.73Ga0.27As, and In0.83Ga0.17As were used to solve for the power densities as a function of wavelengths at radiation temperatures of 1000 °C and 1500 °C. The spectral irradiance of photodetectors was determined with a percentage difference of <4.9 %, presenting an accurate power density estimation for the spectrum at a wide range of radiation temperatures. Power irradiance data obtained were validated in the narrow wavelength range with 1000 nm, 1400 nm, 1500 nm, and 2000 nm bandpass filters. The reported work demonstrates a simple and efficient way which could contribute to develop a cost-effective method of measuring and determining the spectrum irradiances of objects at different radiation temperatures. This predictive analysis method hopefully intensifies the progress of efforts to reduce the reliance on complex optoelectronic instruments in accurately solving power irradiance information.

Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models.

Bacteria- or virus-infected chicken is conventionally detected by manual observation and confirmed by a laboratory test, which may lead to late detection, significant economic loss, and threaten human health. This paper reports on the development of an innovative technique to detect bacteria- or virus-infected chickens based on the optical chromaticity of the chicken comb. The chromaticity of the infected and healthy chicken comb was extracted and analyzed with International Commission on Illumination (CIE) XYZ color space. Logistic Regression, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees have been developed to detect infected chickens using the chromaticity data. Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chicken's comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95% accuracy, followed by SVM-RBF kernel, and KNN with 93% accuracy, Decision Tree with 90% accuracy, and lastly, SVM-Sigmoidal kernel with 83% accuracy. The iteration of the probability threshold parameter for Logistic Regression models has shown that the model can detect all infected chickens with 100% sensitivity and 95% accuracy at the probability threshold of 0.54. These works have shown that, despite using only the optical chromaticity of the chicken comb as the input data, the developed models (95% accuracy) have performed exceptionally well, compared to other reported results (99.469% accuracy) which utilize more sophisticated input data such as morphological and mobility features. This work has demonstrated a new feature for bacteria- or virus-infected chicken detection and contributes to the development of modern technology in agriculture applications.

Quantifying Microalgae Growth by the Optical Detection of Glucose in the NIR Waveband.

Microalgae have become a popular area of research over the past few decades due to their enormous benefits to various sectors, such as pharmaceuticals, biofuels, and food and feed. Nevertheless, the benefits of microalgae cannot be fully exploited without the optimization of their upstream production. The growth of microalgae is commonly measured based on the optical density of the sample. However, the presence of debris in the culture and the optical absorption of the intercellular components affect the accuracy of this measurement. As a solution, this paper introduces the direct optical detection of glucose molecules at 940-960 nm to accurately measure the growth of microalgae. In addition, this paper also discusses the effects of the presence of glucose on the absorption of free water molecules in the culture. The potential of the optical detection of glucose as a complement to the commonly used optical density measurement at 680 nm is discussed in this paper. Lastly, a few recommendations for future works are presented to further verify the credibility of glucose detection for the accurate determination of microalgae's growth.

Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate.

Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.

Chemical Composition, Antioxidant, Antibacterial, and Antibiofilm Activities of Backhousia citriodora Essential Oil.

The essential oil of Backhousia citriodora, commonly known as lemon myrtle oil, possesses various beneficial properties due to its richness in bioactive compounds. This study aimed to characterize the chemical profile of the essential oil isolated from leaves of Backhousia citriodora (BCEO) and its biological properties, including antioxidant, antibacterial, and antibiofilm activities. Using gas chromatography-mass spectrometry, 21 compounds were identified in BCEO, representing 98.50% of the total oil content. The isomers of citral, geranial (52.13%), and neral (37.65%) were detected as the main constituents. The evaluation of DPPH radical scavenging activity and ferric reducing antioxidant power showed that BCEO exhibited strong antioxidant activity at IC50 of 42.57 µg/mL and EC50 of 20.03 µg/mL, respectively. The antibacterial activity results showed that BCEO exhibited stronger antibacterial activity against Gram-positive bacteria (Staphylococcus aureus and Staphylococcus epidermidis) than against Gram-negative bacteria (Escherichia coli and Klebsiella pneumoniae). For the agar disk diffusion method, S. epidermidis was the most sensitive to BCEO with an inhibition zone diameter of 50.17 mm, followed by S. aureus (31.13 mm), E. coli (20.33 mm), and K. pneumoniae (12.67 mm). The results from the microdilution method showed that BCEO exhibited the highest activity against S. epidermidis and S. aureus, with the minimal inhibitory concentration (MIC) value of 6.25 µL/mL. BCEO acts as a potent antibiofilm agent with dual actions, inhibiting (85.10% to 96.44%) and eradicating (70.92% to 90.73%) of the biofilms formed by the four tested bacteria strains, compared with streptomycin (biofilm inhibition, 67.65% to 94.29% and biofilm eradication, 49.97% to 89.73%). This study highlights that BCEO can potentially be a natural antioxidant agent, antibacterial agent, and antibiofilm agent that could be applied in the pharmaceutical and food industries. To the best of the authors' knowledge, this is the first report, on the antibiofilm activity of BCEO against four common nosocomial pathogens.

Color Index of Transformer Oil: A Low-Cost Measurement Approach Using Ultraviolet-Blue Laser.

The color of transformer oil can be one of the first indicators determining the quality of the transformer oil and the condition of the power transformer. The current method of determining the color index (CI) of transformer oil utilizes a color comparator based on the American Society for Testing and Materials (ASTM) D1500 standard, which requires a human observer, leading to human error and a limited number of samples tested per day. This paper reports on the utilization of ultra violet-blue laser at 405- and 450-nm wavelengths to measure the CI of transformer oil. In total, 20 transformer oil samples with CI ranging from 0.5 to 7.5 were measured at optical pathlengths of 10 and 1 mm. A linear regression model was developed to determine the color index of the transformer oil. The equation was validated and verified by measuring the output power of a new batch of transformer oil samples. Data obtained from the measurements were able to quantify the CI accurately with root-mean-square errors (RMSEs) of 0.2229 for 405 nm and 0.4129 for 450 nm. This approach shows the commercialization potential of a low-cost portable device that can be used on-site for the monitoring of power transformers.

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

A Feasible Alternative to FDSOI and FinFET: Optimization of W/La2O3/Si Planar PMOS with 14 nm Gate-Length.

At the 90-nm node, the rate of transistor miniaturization slows down due to challenges in overcoming the increased leakage current (Ioff). The invention of high-k/metal gate technology at the 45-nm technology node was an enormous step forward in extending Moore's Law. The need to satisfy performance requirements and to overcome the limitations of planar bulk transistor to scales below 22 nm led to the development of fully depleted silicon-on-insulator (FDSOI) and fin field-effect transistor (FinFET) technologies. The 28-nm wafer planar process is the most cost-effective, and scaling towards the sub-10 nm technology node involves the complex integration of new materials (Ge, III-V, graphene) and new device architectures. To date, planar transistors still command >50% of the transistor market and applications. This work aims to downscale a planar PMOS to a 14-nm gate length using La2O3 as the high-k dielectric material. The device was virtually fabricated and electrically characterized using SILVACO. Taguchi L9 and L27 were employed to study the process parameters' variability and interaction effects to optimize the process parameters to achieve the required output. The results obtained from simulation using the SILVACO tool show good agreement with the nominal values of PMOS threshold voltage (Vth) of -0.289 V ± 12.7% and Ioff of less than 10-7 A/µm, as projected by the International Technology Roadmap for Semiconductors (ITRS). Careful control of SiO2 formation at the Si interface and rapid annealing processing are required to achieve La2O3 thermal stability at the target equivalent oxide thickness (EOT). The effects of process variations on Vth, Ion and Ioff were investigated. The improved voltage scaling resulting from the lower Vth value is associated with the increased Ioff due to the improved drain-induced barrier lowering as the gate length decreases. The performance of the 14-nm planar bulk PMOS is comparable to the performance of the FDSOI and FinFET technologies at the same gate length. The comparisons made with ITRS, the International Roadmap for Devices and Systems (IRDS), and the simulated and experimental data show good agreement and thus prove the validity of the developed model for PMOSs. Based on the results demonstrated, planar PMOSs could be a feasible alternative to FDSOI and FinFET in balancing the trade-off between performance and cost in the 14-nm process.

The Amber-Colored Liquid: A Review on the Color Standards, Methods of Detection, Issues and Recommendations.

For most natural or naturally-derived liquid products, their color reflects on their quality and occasionally affects customer preferences. To date, there are a few subjective and objective methods for color measurement which are currently utilized by various industries. Researchers are also improving these methods and inventing new methods, as color is proven to have the ability to provide various information on the condition and quality of the liquid. However, a review on the methods, especially for amber-colored liquid, has not been conducted yet. This paper presents a comprehensive review on the subjective and objective methods for color measurement of amber-colored liquids. The pros and cons of the measurement methods, the effects of the color on customer preferences, and the international industry standards on color measurements are reviewed and discussed. In addition, this study elaborates on the issues and challenges related to the color measurement techniques as well as recommendations for future research. This review demonstrates that the existing color measurement technique can determine the color according to the standards and color scales. However, the efforts toward minimizing the complexity of the hardware while maximizing the signal processing through advanced computation are still lacking. Therefore, through this critical review, this review can hopefully intensify the efforts toward finding an optimized method or technique for color measurement of liquids and thus expedite the development of a portable device that can measure color accurately.

A Review on Thermophotovoltaic Cell and Its Applications in Energy Conversion: Issues and Recommendations.

Generally, waste heat is redundantly released into the surrounding by anthropogenic activities without strategized planning. Consequently, urban heat islands and global warming chronically increases over time. Thermophotovoltaic (TPV) systems can be potentially deployed to harvest waste heat and recuperate energy to tackle this global issue with supplementary generation of electrical energy. This paper presents a critical review on two dominant types of semiconductor materials, namely gallium antimonide (GaSb) and indium gallium arsenide (InGaAs), as the potential candidates for TPV cells. The advantages and drawbacks of non-epitaxy and epitaxy growth methods are well-discussed based on different semiconductor materials. In addition, this paper critically examines and summarizes the electrical cell performance of TPV cells made of GaSb, InGaAs and other narrow bandgap semiconductor materials. The cell conversion efficiency improvement in terms of structural design and architectural optimization are also comprehensively analyzed and discussed. Lastly, the practical applications, current issues and challenges of TPV cells are critically reviewed and concluded with recommendations for future research. The highlighted insights of this review will contribute to the increase in effort towards development of future TPV systems with improved cell conversion efficiency.

Corrigendum to "Compatibility of household appliances with DC microgrid for PV systems" [Heliyon 6 (12), (December 2020) Article e05699].

[This corrects the article DOI: 10.1016/j.heliyon.2020.e05699.].

MoS2/h-BN/Graphene Heterostructure and Plasmonic Effect for Self-Powering Photodetector: A Review.

A photodetector converts optical signals to detectable electrical signals. Lately, self-powered photodetectors have been widely studied because of their advantages in device miniaturization and low power consumption, which make them preferable in various applications, especially those related to green technology and flexible electronics. Since self-powered photodetectors do not have an external power supply at zero bias, it is important to ensure that the built-in potential in the device produces a sufficiently thick depletion region that efficiently sweeps the carriers across the junction, resulting in detectable electrical signals even at very low-optical power signals. Therefore, two-dimensional (2D) materials are explored as an alternative to silicon-based active regions in the photodetector. In addition, plasmonic effects coupled with self-powered photodetectors will further enhance light absorption and scattering, which contribute to the improvement of the device's photocurrent generation. Hence, this review focuses on the employment of 2D materials such as graphene and molybdenum disulfide (MoS2) with the insertion of hexagonal boron nitride (h-BN) and plasmonic nanoparticles. All these approaches have shown performance improvement of photodetectors for self-powering applications. A comprehensive analysis encompassing 2D material characterization, theoretical and numerical modelling, device physics, fabrication and characterization of photodetectors with graphene/MoS2 and graphene/h-BN/MoS2 heterostructures with plasmonic effect is presented with potential leads to new research opportunities.

Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm.

The optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In0.53Ga0.47As TPV cell using the real coded genetic algorithm (RCGA) at various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 to 2000 K, the optimized In0.53Ga0.47As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5 to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.

Compatibility of household appliances with DC microgrid for PV systems.

DC distribution of PV systems has spread back especially in the residential sector as a variety of electronic appliances became locally available in the market. The compatibility of household appliances with the best voltage-level in a DC environment is the field that still in the research phase and has not yet made a practically extensive appearance. This paper mainly discusses this issue by providing a review of the concerning research efforts, identifying the gaps in the existing knowledge. The work explains the electrical diagrams of the recently produced appliances, classifying them to get an understanding of how each one consumes energy. It includes exploiting the recent dependence of the commercial appliances on power electronics to improve the efficiency of the existing DC distribution systems by extrapolating new architectures. The proposed topology has a DC distribution environment with two levels of voltage for all appliances. Appliances performances have been evaluated by calculating the energy transfer efficiency. The outcomes of this work can help in designing more efficient DC power distribution networks with minimal energy converters and establishing standardizations for DC microgrids.

Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement.

Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques.

State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.

UV-Vis Spectroscopy: A New Approach for Assessing the Color Index of Transformer Insulating Oil.

Monitoring the condition of transformer oil is considered to be one of the preventive maintenance measures and it is very critical in ensuring the safety as well as optimal performance of the equipment. Various oil properties and contents in oil can be monitored such as acidity, furanic compounds and color. The current method is used to determine the color index (CI) of transformer oil produces an error of 0.5 in measurement, has high risk of human handling error, additional expense such as sampling and transportations, and limited samples can be measured per day due to safety and health reasons. Therefore, this work proposes the determination of CI of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. Results show a good correlation between the CI of transformer oil and the absorbance spectral responses of oils from 300 nm to 700 nm. Modeled equations were developed to relate the CI of the oil with the cutoff wavelength and absorbance, and with the area under the curve from 360 nm to 600 nm. These equations were verified with another set of oil samples. The equation that describes the relationship between cutoff wavelength, absorbance and CI of the oil shows higher accuracy with root mean square error (RMSE) of 0.1961.

Temperature dependence of impact ionization in InAs.

An Analytical Band Monte Carlo model was used to investigate the temperature dependence of impact ionization in InAs. The model produced an excellent agreement with experimental data for both avalanche gain and excess noise factors at all temperatures modeled. The gain exhibits a positive temperature dependence whilst the excess noise shows a very weak negative dependence. These dependencies were investigated by tracking the location of electrons initiating the ionization events, the distribution of ionization energy and the effect of threshold energy. We concluded that at low electric fields, the positive temperature dependence of avalanche gain can be explained by the negative temperature dependence of the ionization threshold energy. At low temperature most electrons initiating ionization events occupy L valleys due to the increased ionization threshold. As the scattering rates in L valleys are higher than those in Γ valley, a broader distribution of ionization energy was produced leading to a higher fluctuation in the ionization chain and hence the marginally higher excess noise at low temperature.

Temperature dependence of gain and excess noise in InAs electron avalanche photodiodes.

Measurement and analysis of the temperature dependence of avalanche gain and excess noise in InAs electron avalanche photodiodes (eAPDs) at 77 to 250 K are reported. The avalanche gain, initiated by pure electron injection, was found to reduce with decreasing temperature. However no significant change in the excess noise was measured as the temperature was varied. For avalanche gain > 3, the InAs APDs with 3.5 µm i-region show consistently low excess noise factors between 1.45 and 1.6 at temperatures of 77 to 250 K, confirming that the eAPD characteristics are exhibited in the measured range of electric field. As the dark current drops much more rapidly than the avalanche gain and the excess noise remains very low, our results confirmed that improved signal to noise ratio can be obtained in InAs eAPDs by reducing the operating temperature. The lack of hole impact ionization, as confirmed by the very low excess noise and the exponentially rising avalanche gain, suggests that hole impact ionization enhancement due to band "resonance" does not occur in InAs APDs at the reported temperatures.