Photovoltaic Solar Cells: A Review

Efficient Integration of Ultra-low Power Techniques and Energy Harvesting in Self-Sufficient Devices: A Comprehensive Overview of Current Progress and Future Directions

Compact, energy-efficient, and autonomous wireless sensor nodes offer incredible versatility for various applications across different environments. Although these devices transmit and receive real-time data, efficient energy storage (ES) is crucial for their operation, especially in remote or hard-to-reach locations. Rechargeable batteries are commonly used, although they often have limited storage capacity. To address this, ultra-low-power design techniques (ULPDT) can be implemented to reduce energy consumption and prolong battery life. The Energy Harvesting Technique (EHT) enables perpetual operation in an eco-friendly manner, but may not fully replace batteries due to its intermittent nature and limited power generation. To ensure uninterrupted power supply, devices such as ES and power management unit (PMU) are needed. This review focuses on the importance of minimizing power consumption and maximizing energy efficiency to improve the autonomy and longevity of these sensor nodes. It examines current advancements, challenges, and future direction in ULPDT, ES, PMU, wireless communication protocols, and EHT to develop and implement robust and eco-friendly technology solutions for practical and long-lasting use in real-world scenarios.

Transparent and Colorless Luminescent Solar Concentrators Based on ZnO Quantum Dots for Building-Integrated Photovoltaics

Scientific interest in luminescent solar concentrators (LSCs) has reemerged mainly due to the application of semiconductor quantum dots (QDs) as highly efficient luminophores. Recently, LSCs have become attractive proposals for Building-Integrated photovoltaics (BIPV) since they could help conventional photovoltaics to improve sunlight harvesting and reduce production costs. However, most of the modern LSCs rely on heavy-metal QDs which are highly toxic and may cause environmental concerns. Additionally, their absorption spectra give them a characteristic color limiting their potential application in BIPV. Herein, we fabricated transparent and colorless LSCs by embedding nontoxic and cost-effective zinc oxide quantum dots (ZnO QDs) in a PMMA polymer matrix (ZnO-LSC), preserving the QD optical properties and PMMA transparency. The synthesized colloidal ZnO QDs have an average size of 5.5 nm, a hexagonal wurtzite crystalline structure, a broad yellow photoluminescent signal under ultraviolet excitation, and are highly visibly transparent at the employed concentrations (>95% in wavelengths above 400 nm). The optical characterization of the fabricated ZnO-LSCs showed a good visible transparency of 80.3% average visible transmission (AVT), with an LSC concentration factor (C) of 1.02. An optimal device (ZnO-LSC-O) could reach a C value of 2.66 with the combination of optical properties of colloidal ZnO QDs and PMMA. Finally, simulations of the performance of silicon solar cells coupled to the fabricated and optimal LSCs under standard AM 1.5G illumination were performed employing the software COMSOL Multiphysics. The fabricated ZnO-LSC achieved a simulated maximum power conversion efficiency (PCE) of 3.80%, while the optimal ZnO-LSC-O reached 5.45%. Also, the ZnO-LSC generated a maximum power of 15.02 mW and the ZnO-LSC-O generated 40.33 mW, employing the same active area as the simulated solar cell directly illuminated, which generated 14.39 mW. These results indicate that the ZnO QD-based LSCs may be useful as transparent photovoltaic windows for BIPV applications.

Hydrogel composites based on chitosan and CuAuTiO2 photocatalysts for hydrogen production under simulated sunlight irradiation

This study explored the photocatalytic hydrogen evolution reaction (HER) using novel biohydrogel composites comprising chitosan, and a photocatalyst consisting in TiO2 P25 decorated with Au and/or Cu mono- and bimetallic nanoparticles (NPs) to boost its optical and catalytic properties. Low loads of Cu and Au (1 mol%) were incorporated onto TiO2 via a green photodeposition methodology. Characterization techniques confirmed the incorporation of decoration metals as well as improvements in the light absorption properties in the visible light interval (λ > 390 nm) and electron transfer capability of the semiconductors. Thereafter, Au and/or Cu NP-supported TiO2 were incorporated into chitosan-based physically crosslinked hydrogels revealing significant interactions between chitosan functional groups (hydroxyls, amines and amides) with the NPs to ensure its encapsulation. These materials were evaluated as photocatalysts for the HER using water and methanol mixtures under simulated sunlight and visible light irradiation. Sample CuAuTiO2/ChTPP exhibited a maximum hydrogen generation of 1790 μmol g-1 h-1 under simulated sunlight irradiation, almost 12-folds higher compared with TiO2/ChTPP. Also, the nanocomposites revealed a similar tendency under visible light with a maximum hydrogen production of 590 μmol g-1 h-1. These results agree with the efficiency of photoinduced charge separation revealed by transient photocurrent and EIS.

Photoelectrochemical Proton-Coupled Electron Transfer of TiO2 Thin Films on Silicon

TiO2 thin films are often used as protective layers on semiconductors for applications in photovoltaics, molecule-semiconductor hybrid photoelectrodes, and more. Experiments reported here show that TiO2 thin films on silicon are electrochemically and photoelectrochemically reduced in buffered acetonitrile at potentials relevant to photoelectrocatalysis of CO2 reduction, N2 reduction, and H2 evolution. On both n-type Si and irradiated p-type Si, TiO2 reduction is proton-coupled with a 1e-:1H+ stoichiometry, as demonstrated by the Nernstian dependence of the Ti4+/3+ E1/2 on the buffer pKa. Experiments were conducted with and without illumination, and a photovoltage of ∼0.6 V was observed across 20 orders of magnitude in proton activity. The 4 nm films are almost stoichiometrically reduced under mild conditions. The reduced films catalytically transfer protons and electrons to hydrogen atom acceptors, based on cyclic voltammogram, bulk electrolysis, and other mechanistic evidence. TiO2/Si thus has the potential to photoelectrochemically generate high-energy H atom carriers. Characterization of the TiO2 films after reduction reveals restructuring with the formation of islands, rendering TiO2 films as a potentially poor choice as protecting films or catalyst supports under reducing and protic conditions. Overall, this work demonstrates that atomic layer deposition TiO2 films on silicon photoelectrodes undergo both chemical and morphological changes upon application of potentials only modestly negative of RHE in these media. While the results should serve as a cautionary tale for researchers aiming to immobilize molecular monolayers on "protective" metal oxides, the robust proton-coupled electron transfer reactivity of the films introduces opportunities for the photoelectrochemical generation of reactive charge-carrying mediators.

Ultra-High Concentration Vertical Homo-Multijunction Solar Cells for CubeSats and Terrestrial Applications

This paper examines advances in ultra-high concentration photovoltaics (UHCPV), focusing specifically on vertical multijunction (VMJ) solar cells. The use of gallium arsenide (GaAs) in these cells increases their efficiency in a range of applications, including terrestrial and space settings. Several multijunction structures are designed to maximize conversion efficiency, including a vertical tunnel junction, which minimizes resistive losses at high concentration levels compared with standard designs. Therefore, careful optimization of interconnect layers in terms of thickness and doping concentration is needed. Homo-multijunction GaAs solar cells have been simulated and analyzed by using ATLAS Silvaco 5.36 R, a sophisticated technology computer-aided design (TCAD) tool aimed to ensure the reliability of simulation by targeting a high conversion efficiency and a good fill factor for our proposed structure model. Several design parameters, such as the dimensional cell structure, doping density, and sun concentrations, have been analyzed to improve device performance under direct air mass conditions AM1.5D. The optimized conversion efficiency of 30.2% has been achieved with investigated GaAs solar cell configuration at maximum concentration levels.

Hybrid global search with enhanced INC MPPT under partial shading condition

The photovoltaic system is quickly emerging as a highly favored option among renewable energy sources. However, it faces several significant challenges, including variable solar irradiance, temperature, and partial shading. Unfortunately, conventional Maximum Power Point Trackers (MPPTs) cannot accurately track partial shading. Artificial intelligence and optimization techniques have been proposed as alternatives, but they require extensive training and can take a long time to achieve maximum power point (MPP) under partial shading circumstances. In this paper, a dynamic and fast-moving method of MPP tracking is proposed for use under both uniform solar irradiance and partial shade. This method combines an enhanced incremental conductance (INC) algorithm with a global search algorithm that looks at how well solar cells work when partly shaded. Simulation investigations are performed to validate the method's applicability and ensure that it reaches the most accurate value of MPP with a short-tracking time of less than 0.2 s and a steady-state error of less than 0.3% of the PV power. The results confirm the efficacy of the suggested tracking method under uniform solar irradiance and partial shade.

Photoluminescent Thin Films of Room-Temperature Glassy Tris(keto-hydrozone) Discotic Liquid Crystals and Their Nanocomposites with Single-Walled Carbon Nanotubes for Optoelectronics

This study addresses the photoresponse of liquid-crystalline tris(keto-hydrozone) discotic (TKHD)-a star-shaped molecular structure with three branches. Object of our research interest was also TKHD filled with single-walled carbon nanotubes (SWCNTs) at a concentration of 1 wt %. At room temperature, the discotic liquid crystals in thin films (thickness 3 μm) of both TKHD and nanocomposite SWCNT/TKHD were in a glassy state. Such glassy thin films exhibited photoluminescence ranging from the deep-red to the near-infrared spectral region, being attractive for organic optoelectronics. The addition of SWCNTs to TKHD was found to stabilize the photoluminescence of TKHD, which is of significance for optoelectronic device applications. The photothermoelectrical response of highly conductive SWCNT/TKHD nanocomposite films was characterized by electrical impedance spectroscopy in the frequency range from 1 Hz to 1 MHz of the applied electric field. It was elucidated that the reversible photothermoelectrical effect in SWCNT/TKHD films occurs through SWCNTs and their network.

Optimization of water reuse and modelling by saline composition with nanoparticles based on machine learning architectures

Water is a necessary resource that enables the existence of all life forms, including humans. Freshwater usage has become increasingly necessary in recent years. Facilities for treating seawater are less dependable and effective. Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants. This research proposes a novel technique in optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cell for saline water treatment and the saline composition has been analyzed using a gradient discriminant random field. Experimental analysis is carried out in terms of specificity, computational cost, kappa coefficient, training accuracy, and mean average precision for various tunnelling electron microscope (TEM) image datasets. The bright-field TEM (BF-TEM) dataset attained a specificity of 75%, kappa coefficient of 44%, training accuracy of 81%, and mean average precision of 61%, whereas the annular dark-field scanning TEM (ADF-STEM) dataset produced specificity of 79%, kappa coefficient of 49%, training accuracy of 85%, and mean average precision of 66% as compared with the existing artificial neural network (ANN) approach.