Solar PV Cells Research Articles

A new power management strategy for PV-FC-based autonomous DC microgrid

Solar energy is widely available in nature and electricity can be easily extracted using solar PV cells. A fuel cell being reliable and environment friendly becomes a good choice for the backup so as to compensate for continuously varying solar irradiation. This paper presents simple control schemes for power management of the DC microgrid consisting of PV modules and fuel cell as energy sources and a hydrogen electrolyzer system for storing the excess power generated. The supercapacitor bank is used as a short term energy storage device for providing the energy buffer whenever sudden fluctuations occur in the input power and the load demand. A new power control strategy is developed for a hydrogen storage system. The performance of the system is assessed with and without the supercapacitor bank and the results are compared. A comparative study of the voltage regulation of the microgrid is presented with the controller of the supercapacitor bank, realized using a traditional PI controller and an intelligent fuzzy logic controller.

Chaos inspired invasive weed optimization algorithm for parameter estimation of solar PV models

High performance solar photovoltaic models require precise knowledge of solar PV cell parameters. Numerous methods based on both deterministic and meta-heuristics have been developed for identifying solar cell parameters. However, the presented methods in the literature have a heavy computational load and limited ability to extract crucial parameters due to nonlinear dynamics of solar PV systems. In addition, because they rely on approximations to determine the objective function, the preceding state-of-the-art parameter estimation techniques do not provide accurate results. Thus, a novel chaos-inspired invasive weed optimization (CIIWO) has been developed for accurate solar PV system parameter estimation. Adding a chaotic map to IWO improves the performance of suggested method by expanding the search space globally. Moreover, to cope with the inadequacy in state-of-art objective functions, Newton Raphson approach has been combined with proposed CIIWO algorithm. The suggested approach for solar cell parametric identification has been tested on one-diode, two-diode, and three-diode models. By contrasting the outcomes with nine contemporary optimization strategies for parameter estimation, the superiority of the suggested algorithm has been demonstrated. Commercial PV cell RTC France has been used for the experimental validation. Comprehensive study of experimental data validates the efficacy and stability of the suggested algorithm.

Photoluminescence of Argan-Waste-Derived Carbon Nanodots Embedded in Polymer Matrices.

In this work, photoluminescent (PL) carbon nano dots (CNDs) prepared from argan waste were embedded in highly optical transparent poly(styrene-co-acrylonitrile) (PSA) and cyclo-olefin copolymer (COC) matrices, which were further processed into thin films. In the first step, the luminescent CNDs were prepared through thermal processing of fine-groundargan waste, followed, in the second step, by direct dispersion in the polymer solutions, obtained by solving PSA and COC in selected solvents. These two polymer matrices were selected due to their high optical transparency, resilience to various environmental factors, and ability to be processed as quality thin films. The structural configuration of the CNDs was investigated through EDX, XPS, and FTIR, while DLS, HR-SEM, and STEM were used for their morphology investigation. The luminescence of the prepared CNDs and resulted polymer nanocomposites was thoroughly investigated through steady-state, absolute PLQY, and lifetime fluorescence. The quality of the resulted CND-polymer nanocomposite thin films was evaluated through AFM. The prepared highly luminescent thin films with a PL conversion efficiency of 30% are intended to be applied as outer photonic conversion layers on solar PV cells for increasing their conversion efficiency through valorization of the UV component of the solar radiation.

Comprehensive investigation of a building integrated crossed compound parabolic concentrator photovoltaic window system: Thermal, optical and electrical performance

Integrating PV solar cells with concentrators into window systems can not only generate electricity for a building, but also has the potential to enhance the thermal resistance of the building's windows without a significant sacrifice of light transmittance for passive daylight. A novel system, known as the crossed compound parabolic concentrator photovoltaic window, has been recently studied for its electrical properties. However, its thermal and optical performance, particularly in terms of the overall heat transfer coefficient (U-value) and total optical transmittance when integrated into a building, remains unexamined. These factors are crucial for predicting the system's impact on a building's energy efficiency and indoor comfort. Therefore, this paper aims to investigate the U-value of this window system under various temperature scenarios and the optical transmittance of the window at different incident angles. The thermal conductance was assessed through numerical simulations using a computational fluid dynamics model, which was validated by experimental measurements conducted in a large climate chamber. The optical transmittance was investigated using a validated 3D ray-tracing model. The total optical transmittance and electricity generation were calculated for typical sunny days in winter and summer under London's climate conditions. Additionally, alternative designs were developed to explore the impact of pitch between adjacent optics on the thermal conductance and optical transmittance of the window. The results showed that the window with a configuration of Dx = Dy = 5 mm (where Dx and Dy represent the horizontal and vertical pitches, respectively, between two adjacent solar optics) achieved the lowest U-value (2.566 W/m2·K). This U-value is slightly lower than that of the original window design, which has a U-value of 2.575 W/m2·K. The original window configuration with Dx = Dy = 1.77 mm produces the highest power output. Specifically, it generates 499.25 Wh/m2 on a typical sunny day in winter and 162.73 Wh/m2 on a typical sunny day in summer. However, it exhibits the lowest transmittance (14.6 % on a typical sunny day in winter and 25.2 % on a typical sunny day in summer, respectively), indicating that it is more suitable for buildings with a higher window-to-wall ratio to ensure an adequate amount of natural light. For buildings with a lower window-to-wall ratio, the CCPC-PV window should be designed with a larger horizontal pitch, such as 15 mm and 30 mm, to meet indoor illuminance requirements while also providing enhanced thermal performance and additional power output.

Improved Performance of a Grid-Connected Inverter with Two PV Array Input under Different Environment Conditions using Adaptive Neuro Fis Based Control

Grid-connected inverters based on solar PV cells are becoming increasingly common. At times, the PV can be subjected to a certain environmental situation, such as varying irradiance or temperature. Present developments in gird-connected PV inverters do not have many reliable yields or reliability in this case. In this article, an improved control framework system is intended for two PV that are mismatched in various ways, resulting in a highly effective grid-connected solar inverter with THD under 1% for both voltage and current. The regulator employs a control scheme comprised of an ANFIS-based control system that works in tandem with the incremental conductance technique of MPPT. The outcomes are simulated and validated using MATLAB Simulink.

Parameter identification of solar cells using improved Archimedes Optimization Algorithm

The parameters of solar cells for five PV models are identified using an Improved Archimedes Optimization Algorithm (IAOA) in this paper. Two modifications are made to the original Archimedes Optimization Algorithm (AOA). To control the unequal exploration and exploitation phases, the initial adjustment is to incorporate an augmented density decreasing factor. A random average calculation between the current object position and the best object position is implemented for the second modification to solve the local optima issue. The proposed IAOA is then used to tackle the problem of identifying PV model parameters from experimental I-V data. Different PV models, such as the one-diode model (ODM), the two-diode model (TDM), and the PV module model (PMM), have been distinguished using the suggested IAOA. The proposed IAOA outperforms other present algorithms and even outperforms the original AOA based on the revealed results. As closely as feasible to the experimental I-V data of real PV solar cells and module models, the proposed IAOA can choose the best parameter values for PV models.

Experimental evaluation of thermal efficiency, electrical efficiency, and power production of low-concentrating photovoltaic-thermal system with micro-jet channel

The efficient behavior of a low-concentrating photovoltaic-thermal system with a micro-jet channel (LCPV/T-JET) and booster mirror reflector is experimentally evaluated here. Micro-jets promote the thermal management of PV solar cells by implementing jet water as active cooling, which is still in the early stages of development. The booster mirror reflector concentrates solar irradiance into solar cells and improves the thermal, electrical, and combined efficiencies of the LCPV/T-JET system. The LCPV/T-JET system was tested under ambient weather conditions in the city of Bangi, Selangor, Malaysia, and all data was recorded between 10:00 a.m. and 4:00 p.m. Parametric studies were conducted to compare the performance of the LCPV/T-JET system to that of a conventional PV module. It is found that the short-circuit current of a standard PV module could be increased by 28% and 11.7%, with and without the use of jet water and booster mirror reflectors, respectively. Moreover, the power supplied by the PV component increases to 31% and 16%, respectively, with and without jet water and a booster mirror reflector. The characteristic curves for current–voltage and power–voltage curves also confirmed the superior thermoelectric properties of the LCPV/T-JET system. The measurements reveal a drop in the electrical efficiency of solar cells from 14.5% to 12.25% as the temperature of the cells increases from 32.5 °C to 66.5 °C between 10:00 a.m. and 1:00 p.m. Meanwhile, the thermal and overall efficiencies declined from 84% to 81.5% and 96% to 93%, respectively.

Simulating the Energy, Economic and Environmental Performance of Concentrating Solar Power Technologies Using SAM

According to the Libyan government's newly released strategic plan, renewable and environmentally friendly energy sources would provide 30% of the country's power by 2030. The goal of this research is to shed light on solar energy technologies that may be used to generate clean and sustainable electricity. An energy-economic-environmental study of five Concentration Solar Power (CSP) technologies (parabolic trough, solar dish, linear Fresnel reflector, solar tower, and concentrated PV solar cell) was conducted for 22 selected locations in Libya. The Levelized Cost Of Energy (LCOE) was chosen as a reference for identifying which technology would be most suited for each site. The economic estimates include the cost of environmental damage caused by carbon dioxide gas (CO2) emissions from fossil-fuel-powered power plants. This technique allows clean and renewable energy to compete fairly in the global energy market, even in countries that produce oil and subsidize electricity. According to the data, the solar mirror technology in Libya has the lowest LCOE of all the technologies evaluated in this study. The LCOE estimates varied from 0.01 to 0.04 dollars per kWh. The clean energy produced by the solar tower surpassed 100 MW, or about 400.332 GWh. Furthermore, the adoption of clean concentrating solar energy technology avoided the discharge of 4,235 tCO2/year/MWp.

Numerical analysis of the heat transfer and fluid flow of a novel water-based hybrid photovoltaic-thermal solar collector integrated with flax fibers as natural porous materials

Photovoltaic thermal (PVT) collector-based active cooling technology makes it possible to increase the efficiency of PV solar cells and meanwhile generate heat through the direct conversion of solar irradiation into electricity. Hence, this study presents a detailed numerical analysis of the thermal performance of PVT solar collectors integrated with flax fibers as natural porous materials. To achieve this goal, a cooling channel is proposed, which contains porous flax fiber materials doping in pure water as a cooling fluid for the photovoltaic panels. A particular focus of this research is emphasized on the effects of the thickness of the porous material layer (5−50 mm), the solar flux (50−1000 W/m2), and the flow rate of coolant (0.40−1.0 m/s), to determine the best thickness of the porous material and the cooling fluid flowrate that achieves the highest performance of photovoltaic panels. The simulations are performed using ANSYS software, Navier Stokes equations, and Darcy-Brinkman-Forchheimer porous model. Moreover, the thermal performance of the proposed PVT system cooled with water/porous flax fibers mixture is analyzed and compared with the PVT collector using pure water and air as a coolant. The results presented that the optimal design for maximization of the cooling of photovoltaic panels is attained by incorporating porous flax fibers materials with a thickness of 50 mm and 0.907 m/s cooling water flowrate. It is indicated that the Nusselt number is increased from 18.65 to 51.0, with an improvement of 173.46% as compared to the use of only pure water at the optimal conditions. Moreover, the thermal efficiencies of the PVT system are obtained as 69.58%, 50.02%, and 34.60% using water with a flax fibers layer, pure water, and air, respectively.

Smart Operating Range Monitoring of Solar PV Cell with Integrated Phase Change Materials by Using Solar Deep Learning Model

This research aims to demonstrate that a one-of-a-kind microencapsulated bio-based PCM may be utilized in the passive collection of solar energy in buildings. The research makes use of a PCM with thermal properties within the comfort zone but is also able to track the performance of solar photovoltaic panels. This allows the research to be more accurate. Because of this, the research can produce outcomes that are more accurate. After finishing the step before this one, the next thing that needs to be done is creating novel composite concrete panels. This step follows the completion of the phase before it. The outcomes of the mechanical and thermal tests served as the basis for determining the best composition of the concrete mix that should be used for making this panel. The method concludes with the construction of a test structure using composite walls as the primary building material.

Analysis of Maximum Possible Utilization of Solar Radiation on a Solar Photovoltaic Cell with A Proposed Model

The paper proposes a possible solution to absorb and utilize all the solar radiation spectrum including ultraviolet and infrared along with the visible radiation. A typical solar PV cell usually absorb only the visible solar radiation to covert to electricity which is 46% of the total solar radiation but it can’t use the other non-visible spectrum like infrared (longer wavelength) and ultraviolet (shorter wavelength) which consists of the other 54% of solar energy. This non-visible solar radiation is either absorbed by the earth surface and atmosphere or reflected back to the space and it is wasted as far as the solar cell absorption is concerned. There are lots of researches going on in the semiconductor level to design a solar cell which can absorb the solar radiation in non-visible region using band-gap engineering but if we could somehow convert the non-visible solar radiation into visible range before it comes to the solar cell (semiconductor level) then the overall efficiency of a solar PV system would surely be increased to a satisfactory amount. After converting the non-visible spectrum into visible spectrum, we will then incident this radiation on a typical solar PV cell. In this paper the possible arrangement for such conversion is proposed which is still at a preliminary stage concept and yet to be implemented

A Case Study on PV-Aided Net Zero-Energy Building: the Daycare in IKCU

At the core of our growing societies, energy supply stands as one of the major concerns today, and it will be an inevitable challenge for our near future. As the nations are looking to find solutions for the transition from fossil fuels – depleting at a high rate – to alternative energy sources, solar energy through PV cells is getting attention as an affordable and easily implemented option especially for power supply in commercial and residential buildings. This work consists in analyzing the possibility to cover the entire energy needs of a building via PV solar cells for the case of a constructed daycare. In this case study, HVAC energy requirement has been calculated by the TS825 standard. The standard specifies a method for calculating the net heating/ventilation energy need and provides the rules for calculating the maximum allowable temperature in buildings. First, dimensions of the investigated building are taken and characteristics affecting the thermal insulation are assessed. Then, other energy needs, mainly lighting and electrical devices, are computed in the analysis as internal electricity needs. The scope of this work extends to the assessment of indoor air quality for occupants of building, which is an important aspect in our case study where the occupants are children. ASHRAE standards 62.1 is utilized for this purpose. The standard specifies minimum ventilation rates and other measures intended to provide acceptable indoor air quality to human occupants and that minimizes adverse health effects. The results are obtained for monthly varying solar exposition in the specified area where the building is located to provide supply for the determined energy demand via solar energy. Finally, monocrystalline PV panel system has been proposed with proper orientation and adequate power potential. Based on the obtained results, as well as the economical aspect, inferences and suggestions are made for improvements.

Using ZnO/PMMA Nanocomposite Coating to Improve the Polycrystalline Solar Cell in Hot Weather Conditions

Solar energy is the most significant future energy source as the world keeps working to minimize emissions caused by the use of fossil fuels. The most popular method for transforming sunlight into electrical energy is the photovoltaic solar cell (PV). The main two problems with PV solar cells highlighted in this paper are the high solar cell surface temperature and the high reflection light of PV solar cells. Because each 1 °C increase in surface temperature reduces efficiency by 0.45%, and approximately 35% of total sunlight reflected in crystalline silicon solar cells. In this experimental work, the sol-gel process was used to prepare different concentrations of zinc oxide/polymethyl methacrylate (ZnO/PMMA) nanocomposite coatings and apply them to the top side of the polycrystalline solar cell in order to increase the solar cell efficiency. The results demonstrate a maximum temperature drop of 8.6 ºC and light reflection reduced up to 5.6%, so this led to an increase in solar cell efficiency from (11.33%) to (15.22%), where the results of the electrical properties after one hour (test time) showed that the uncoated cell was (ISC = 0.33 A, VOC = 0.38 V, and Pmax = 97 mW), while the results of the coated cell with the best concentration (3.875 wt%) were (ISC = 0.36 A, VOC = 0.43 V, and Pmax = 130 mW).

Creating A Green Economic Ecosystem Through Lean Canvas Model: BIPV System Achieving Net Zero Emission

Green Economy is a sustainable economic activity to be able to improve people's welfare without neglecting the impact that causes damage to the environment. This concept prioritizes economic activities with low carbon emissions. Currently, the largest carbon emissions are caused by power plants that still use non-renewable natural resources. One of the efforts made to reduce carbon emissions is to use a solar power plant, known as PV Solarcell. However, with new developments and discoveries for solar power plants, BIPV is perfected. BIPV is a solar power plant where the existing solar panels are attached to buildings or buildings. The aim of this research is to be able to create a new green economy business model for BIPV power plants by using lean canvas tools. This research uses descriptive qualitative method. This research starts from May to October 2022. The results of this study using the lean canvas concept can create an integrated and sustainable green economy business model ecosystem to be able to create new value.

PV Solar Yield Applicable Simulator Based on Free Irradiance Data Source: Applied Comprehensive Tool for Solar Engineers

Accurate PV system simulators are implemented with expensive software platforms using paid irradiance data. The main purpose of this paper is to develop and validate a PV system simulator, beginning with a solar cell parameter extraction model, then test and validate long-term Irradiance data using free online source (Typical Meteorological Year TMY in (PVGIS) European website), and finally building full solar generator simulator to run in working real conditions. Comparing results with Accurate Paid PV simulators (which use the Muneer model) showed good accuracy of the proposed simulator. Work flow starts with the Irradiance model’s data processing, then solar cell 5 parameters model data processing (to extract cell parameters), and finally full system simulator. MATLAB coding programs in real working conditions are used for simulation. Results of solar cell parameter extraction show 99.6% to 99.99% matching with data sheet and cell performance under standard test conditions. System model simulation output shows 8% less yearly generated energy compared to the PVGIS 2022 long-term simulation (hourly basis (one-year time)). This is due to incident energy variations (between the years 2016 and 2022) of 4.02%. The novelty of the algorithm is the methodology, as it tests irradiance data on an hourly basis and validates results for a whole continuous year. Also, the 5-parameter solar cell model is used to be validated in long term analysis, not only STC conditions and could be applied on any PV solar cell. The algorithm and block diagram used are scalable, modular, and interchangeable with similar models to be tested. This simulator could test several methods and models in solar pv technology.

Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom Properties

Perovskite materials have high potential for the renewable energy sources such as solar PV cells, fuel cells, etc. Different structural distortions such as crystal structure and lattice parameters have a critical impact on the determination of the perovskite’s structure strength, stability, and overall performance of the materials in the applications. To improve the perovskite performance and accelerate the prediction of different structural distortions, few ML models have been established to predict the type of crystal structures and their lattice parameters using the basic atom characteristics of the perovskite materials. In this work, different ML models such as random forest (RF), support vector machine (SVM), neural network (NN), and genetic algorithm (GA) supported neural network (GA-NN) have been established, whereas support vector regression (SVR) and genetic algorithm-supported support vector regression (GA-SVR) models have been assessed for the prediction of the lattice parameters. The prediction model accuracy for the crystal structure classification is almost 88% in average for GA-NN whereas for the lattice constants regression model GA-SVR model gives ~95% in average which can be further improved by accumulating more robust datasets into the database. These ML models can be used as an alternative process to accelerate the development of finding out new perovskite material by providing valuable insight for the behaviours of the perovskite materials.

Numerical investigation of nanofluids comprising different metal oxide nanoparticles for cooling concentration photovoltaic thermal CPVT

The impact of utilizing Nanofluids with different metal oxide nanoparticles on heat transfer enhancement and fluid flow for concentration photovoltaic thermal the CPVT collector with Fresnel lens is demonstrated using CFD simulation. With the rising temperature, the electrical efficiency and output power of Multi-Junction Solar Cells drop. Four different types of nanofluids and water are utilized as cooling fluids to disperse heat from Multi-Junction Solar Cells by moving the fluid within the mini-channel beneath the PV Solar Cells. In the current analysis, the Reynolds numbers range from 2000 to 18000. For all forms of cooling fluids, the average Nusselt numbers are augmented by rising Reynolds numbers. The CFD results demonstrated that the Nusselt number of all nanofluid cooling fluids is greater than that of water. When compared to water in a CPVT collector, water-SiO2 nanofluid provides the maximum heat transfer improvement of 12.5%. The silicon dioxide nanoparticles have high thermal conductivity, with the water-SiO2 nanofluid exhibiting the greatest heat transfer increase, followed by water-Al2O3, water-ZnO, water-CuO, and water. The average top surface temperature of the mini-channel for the CPVT collector is reduced by 7% when water-SiO2 nanofluid is used as a cooling fluid and by 3.4% when water is used. The usage of water-SiO2 nanofluid has a significant impact on the heat dissipation with high heat transfer enhancement from Multi-Junction Solar Cells in CPVT collector, leading to an increase in electrical efficiency.

Impact of annealing on the growth dynamics of indium sulphide buffer layers

Thin films of Indium sulphide are deposited on corning glass substrate by thermal evaporation at room temperature (300 K). The as-deposited films were annealed from 373 to 723 K under vacuum ∼1 × 10−3 mbar. An amorphous phase is obtained from 300 to 473 K; the polycrystalline β-In2S3 emerges at 523 K, and In2O3 is grown at 723 K. The intermediary phases of β-In2S3 and In2O3 are perceived from 573 to 673 K. A clear distinction between the morphology of β-In2S3 and In2O3 was observed in the micrographs of scanning electron microscopy (SEM) and atomic force microscopy (AFM). The β-In2S3 dominated films provide absorption coefficient (α) from 18 to 25 × 104 cm−1, while α values of In2O3 layers lie within 1–5 × 104 cm−1. The bandgap (Eg) of β-In2S3 thin films is low (2.34 eV), and that of In2O3 is high (3.47 eV); however, the intermediary phases of β-In2S3 and In2O3 exhibit bandgap tunning from 2.30 to 3.14 eV. Moreover, the β-In2S3 shows the highest carrier concentration (Nd; 3.25 × 1018 cm−3), In2O3 provides the highest mobility (μ; 228 Cm2/V.s), and intermediary phases exhibit the lowest resistivity (ρ; 1.27 × 103 Ω/cm) within the existing forms. The thin films β-In2S3 phase grown at 523 and 573 K meet its stoichiometric ratio. In comparison, the In2O3 phase emerges under high oxygen and sulphur deficit conditions. The films are suitable for photo-conduction devices and in the buffer/window layer of PV solar cell design.

Investigation of the impact of iron amounts on optical and physical properties of coated TiO2 thin films used for PV solar cells

This paper displays the influence of iron (Fe) amounts on the physical properties of anatase titanium dioxide (TiO2) obtained by the spin coating technique. The effect on each film's structural, morphological, optical, and dielectric properties was studied. The X-ray diffraction spectra shows the anatase phase, and many vibrational modes were seen via a Raman analysis. A fourier transform infrared (FTIR) analysis indicated the formation of Ti–O–Fe bonds into the host lattice. The X-ray photoelectron spectroscopy (XPS) results revealed Fe was present in all doped films. A scanning electron microscope (SEM) demonstrated that the surface of the film's had a non-spherical agglomeration formation. Atomic force microscopy (AFM) was also used to assess surface roughness. An energy-dispersive X-ray spectroscopy (EDXS) analysis suggested the presence of Fe used for doping in the TiO2 structure. A maximum transmittance of 65% was found for 2g of Iron (III) nitrate nanohydrate added in TiO2. Optical studies obtained via a UV-VIS-NIR spectroscopy demonstrated that the band gap was effectively reduced with increased amounts of iron. The single oscillator energy (E0), the dispersion energy (Ed), as well as the high frequency dielectric constant, are calculated via the Wemple-DiDomenico model.

Sequentially evaporated layer deposition stack of CuxS thin films for photonics applications

CuxS(x;1,2) layers were grown on thermal annealing of the sequentially evaporated layer deposition (SELD) stack of Cu/S from 373 to 573 K. The thicknesses of the deposited stacks were fixed at 200 and 600 nm. The 300 K deposited layers possess a CuS (covellite) phase and a few impurity peaks of Sulphur (S). The as-deposited films of both thicknesses provided the CuS (covellite) phase with impurity peaks of Sulphur (S). The CuS single phase was observed at 373 and 473 K. The CuS transformed into a copper-rich Cu2S (chalcocite) phase at 523 K, and the same phase continued at 573 K. The Cu2S phase was obtained on re-evaporation of S from CuS at 523 K. An intermediary digenite phase was also spotted at 523 K. The CuS and Cu2S phases were confirmed from the Raman spectra. The oxidation states of Cu and S of the CuS films were also determined from XPS analysis. The Cu 2p and S 2p levels of CuS (covellite) were established using high-resolution XPS spectra. The phase transformation from CuS to Cu2S and change in stoichiometry at higher temperatures give rise to bandgap variation from 2.30 to 1.20 eV for direct and 2.10–1.34 eV for indirect allowed bandgaps. The absorption coefficient (α) for both thickness values is > 1 × 105 cm-1. Further, the 600 nm films provided higher absorption coefficient (α) values than the 200 nm layers. The unique electrical and optical properties and bandgap tuning make CuS and Cu2S thin films suitable for gas sensors, photodetectors, absorber layers in low-cost PV solar cells, and energy storage applications.