Perspectives on cultivation and harvesting technologies of microalgae, towards environmental sustainability and life cycle analysis.

The development of algae is seen as a potential and ecologically sound approach to address the increasing demands in multiple sectors. However, successful implementation of processes is highly dependent on effective growing and harvesting methods. The present study provides a complete examination of contemporary techniques employed in the production and harvesting of algae, with a particular emphasis on their sustainability. The review begins by examining several culture strategies, encompassing open ponds, closed photobioreactors, and raceway ponds. The analysis of each method is conducted in a systematic manner, with a particular focus on highlighting their advantages, limitations, and potential for expansion. This approach ensures that the conversation is in line with the objectives of sustainability. Moreover, this study explores essential elements of algae harvesting, including the processes of cell separation, dewatering, and biomass extraction. Traditional methods such as centrifugation, filtration, and sedimentation are examined in conjunction with novel, environmentally concerned strategies including flocculation, electro-coagulation, and membrane filtration. It evaluates the impacts on the environment that are caused by the cultivation process, including the usage of water and land, the use of energy, the production of carbon dioxide, and the runoff of nutrients. Furthermore, this study presents a thorough examination of the current body of research pertaining to Life Cycle Analysis (LCA) studies, presenting a perspective that emphasizes sustainability in the context of algae harvesting systems. In conclusion, the analysis ends up with an examination ahead at potential areas for future study in the cultivation and harvesting of algae. This review is an essential guide for scientists, policymakers, and industry experts associated with the advancement and implementation of algae-based technologies.

Exploring the impact of defect energy levels in CdTe/Si dual-junction solar cells using wxAMPS.

A numerical analysis of a CdTe/Si dual-junction solar cell in terms of defect density introduced at various defect energy levels in the absorber layer is provided. The impact of defect concentration is analyzed against the thickness of the CdTe layer, and variation of the top and bottom cell bandgaps is studied. The results show that CdTe thin film with defects density between 1014 and 1015 cm-3 is acceptable for the top cell of the designed dual-junction solar cell. The variations of the defect concentrations against the thickness of the CdTe layer indicate that the open circuit voltage, short circuit current density, and efficiency (ƞ) are more affected by the defect density at higher CdTe thickness. In contrast, the Fill factor is mainly affected by the defect density, regardless of the thin film's thickness. An acceptable defect density of up to 1015 cm-3 at a CdTe thickness of 300 nm was obtained from this work. The bandgap variation shows optimal results for a CdTe with bandgaps ranging from 1.45 to 1.7 eV in tandem with a Si bandgap of about 1.1 eV. This study highlights the significance of tailoring defect density at different energy levels to realize viable CdTe/Si dual junction tandem solar cells. It also demonstrates how the impact of defect concentration changes with the thickness of the solar cell absorber layer.

Advancing renewable fuel integration: A comprehensive response surface methodology approach for internal combustion engine performance and emissions optimization.

In the realm of internal combustion engines, there is a growing utilization of alternative renewable fuels as substitutes for traditional diesel and gasoline. This surge in demand is driven by the imperative to diminish fuel consumption and adhere to stringent regulations concerning engine emissions. Sole reliance on experimental analysis is inadequate to effectively address combustion, performance, and emission issues in engines. Consequently, the integration of engine modelling, grounded in machine learning methodologies and statistical data through the response surface method (RSM), has become increasingly significant for enhanced analytical outcomes. This study aims to explore the contemporary applications of RSM in assessing the feasibility of a wide range of renewable alternative fuels for internal combustion engines. Initially, the study outlines the fundamental principles and procedural steps of RSM, offering readers an introduction to this multifaceted statistical technique. Subsequently, the study delves into a comprehensive examination of the recent applications of alternative renewable fuels, focusing on their impact on combustion, performance, and emissions in the domain of internal combustion engines. Furthermore, the study sheds light on the advantages and limitations of employing RSM, and discusses the potential of combining RSM with other modelling techniques to optimise results. The overarching objective is to provide a thorough insight into the role and efficacy of RSM in the evaluation of renewable alternative fuels, thereby contributing to the ongoing discourse in the field of internal combustion engines.

Lattice mismatch alleviation in p-CdTe/n-Si heterostructure by surface engineering on Si substrate.

The study used magnetron sputtering to investigate the growth of cadmium telluride (CdTe) thin films on surface treated n-type silicon (n-Si) substrates. The n-Si substrates were textured using potassium hydroxide (KOH) before the sputter deposition of CdTe. This was followed by cadmium chloride treatment to reduce the strain at the interface of CdTe and Si, which is caused by the incompatible lattice and thermal expansion mismatch (CTE). X-ray diffraction (XRD) analysis showed that the lowest FWHM and dislocation densities were obtained for CdCl2/CdTe/txt-nSi, which aligns with the scanning electron microscopy (SEM) results. In the SEM images, the interface bonding between the CdTe and Si surfaces was visible in the cross-sections, and the top-view images revealed sputtered CdTe thin films conforming to the patterns of pyramidal textured Si as an engineered surface to capture more light to maximize absorption in the CdTe/Si tandem design. The Energy dispersive X-ray (EDX) results showed that all the CdTe deposited on textured n-Si exhibited more Te atoms than Cd atoms, irrespective of the CdCl2 treatment. The presented results suggest that the texturization and CdCl2 treatment improved the morphology and grain boundary passivation of the sputtered CdTe. The adhesiveness of CdTe on the n-Si substrate was also significantly enhanced. Our findings further demonstrate that proper surface treatment of the Si substrate can greatly improve the quality of CdTe grown on Si by reducing the strain that occurs during the growth process. This study demonstrates a valuable method for enhancing the integration of CdTe with Si for two-junction tandem solar cell applications.

A comprehensive review of flexible cadmium telluride solar cells with back surface field layer.

Recent advancements in CdTe solar cell technology have introduced the integration of flexible substrates, providing lightweight and adaptable energy solutions for various applications. Some of the notable applications of flexible solar photovoltaic technology include building integrated photovoltaic systems (BIPV), transportation, aerospace, satellites, etc. However, despite this advancement, certain issues regarding metal and p-CdTe remained unresolved. Besides, the fabrication of a full-working device on flexible glass is challenging and requires special consideration due to the unstable morphology and structural properties of deposited film on ultra-thin glass substrates. The existing gap in knowledge about the vast potential of flexible CdTe solar cells on UTG substrates and their possible applications blocks their full capacity utilization. Hence, this comprehensive review paper exclusively concentrates on the obstacles associated with the implementation of CdTe solar cells on UTG substrates with a potential back surface field (BSF) layer. The significance of this study lies in its meticulous identification and analysis of the substantial challenges associated with integrating flexible CdTe onto UTG substrates and leveraging Cu-doped ZnTe as a potential BSF layer to enhance the performance of flexible CdTe solar cells.

Physical-Vapor-Deposited Metal Oxide Thin Films for pH Sensing Applications: Last Decade of Research Progress.

In the last several decades, metal oxide thin films have attracted significant attention for the development of various existing and emerging technological applications, including pH sensors. The mandate for consistent and precise pH sensing techniques has been increasing across various fields, including environmental monitoring, biotechnology, food and agricultural industries, and medical diagnostics. Metal oxide thin films grown using physical vapor deposition (PVD) with precise control over film thickness, composition, and morphology are beneficial for pH sensing applications such as enhancing pH sensitivity and stability, quicker response, repeatability, and compatibility with miniaturization. Various PVD techniques, including sputtering, evaporation, and ion beam deposition, used to fabricate thin films for tailoring materials' properties for the advanced design and development of high-performing pH sensors, have been explored worldwide by many research groups. In addition, various thin film materials have also been investigated, including metal oxides, nitrides, and nanostructured films, to make very robust pH sensing electrodes with higher pH sensing performance. The development of novel materials and structures has enabled higher sensitivity, improved selectivity, and enhanced durability in harsh pH environments. The last decade has witnessed significant advancements in PVD thin films for pH sensing applications. The combination of precise film deposition techniques, novel materials, and surface functionalization strategies has led to improved pH sensing performance, making PVD thin films a promising choice for future pH sensing technologies.

A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic.

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic's dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm.

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

Minimum variance distortionless response beamformer with enhanced nulling level control via dynamic mutated artificial immune system.

In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals.