Buckypaper (BP), a flexible and porous material, exhibits photovoltaic properties when exposed to light. In this study, we employed radio frequency (RF) sputtering of zinc oxide (ZnO) followed by rapid thermal annealing to enhance the photovoltaic response of BP. We investigated the impact of various sputtering parameters, such as the gas flow ratio of argon to oxygen and deposition time, on the morphology, composition, resistivity, and photovoltaic characteristics of ZnO-modified BP. Additionally, the photovoltaic performance of the samples under different illumination modes and wavelengths was compared. It was found that optimal sputtering conditions-argon to oxygen flow ratio of 1:2, deposition time of 20 min, and power of 100 watts-resulted in a ZnO film thickness of approximately 45 nanometers. After annealing at 400 °C for 10 min, the ZnO-modified BP demonstrated a significant increase in photocurrent and photovoltage, along with a reduction in resistivity, compared to unmodified BP. Moreover, under gradient illumination, the ZnO-modified BP exhibited a photovoltage enhancement of 14.70-fold and a photocurrent increase of 13.86-fold, compared to uniform illumination. Under blue light, it showed a higher photovoltaic response than under other colors. The enhancement in photovoltaic response is attributed to the formation of a Schottky junction between ZnO and BP, an increased carrier concentration gradient, and an expanded light absorption spectrum. Our results validate that ZnO sputtering followed by annealing is an effective method for modifying BP for photovoltaic applications such as solar cells and photodetectors.
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http://dx.doi.org/10.3390/nano14090792 | DOI Listing |
Phys Chem Chem Phys
January 2025
The Quartz Corp, Hellandsveien 14, 8270, Drag, Norway.
High purity quartz glass is an important material in high-tech industries like semiconductors and photovoltaics due to, among other properties, its good mechanical performance at high temperatures. Small amounts of Al in silica glass (in the range between 20 ppm and 100 ppm) have previously been shown to increase the viscosity of the SiO glass. The underlying mechanism for this increase is, however, not well understood.
View Article and Find Full Text PDFNanoscale
January 2025
Beijing Academy of Quantum Information Sciences, Beijing 100193, P.R. China.
The bulk photovoltaic effect, arising from the separation of charge carriers driven by crystal symmetry, is an intriguing physical phenomenon that has been attracting broad interest in the field of photovoltaic applications due to its junction-free nature and potential to surpass the Shockley-Queisser limit. The photovoltaic applications of conventional ferroelectric materials with wide bandgaps (2.7-4 eV) are limited due to their low photocurrent densities and weak photovoltaic response in the visible light region.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
LPHE-MS, Faculty of Science, Mohammed V University in Rabat, Morocco.
This study explores the optoelectronic and photovoltaic potential of acceptor-π-donor (A-π-D) architectures utilizing CSi quantum dots (CSiQDs) through a combination of density functional theory (DFT) and time-dependent DFT (TDDFT). We examined two key structural configurations: C-C and Si-C conformers. In these systems, CSiQDs serve as the acceptor, CHSF as the π-bridge, and 3 × (CHO) as the donor.
View Article and Find Full Text PDFJ Org Chem
January 2025
Institute of Theoretical Chemistry and College of Chemistry, Jilin University, Changchun 130023, P.R. China.
Thiophene and pyrrole units are extensively utilized in light-responsive materials and have significantly advanced the field of organic photovoltaics (OPV). This progress has inspired our exploration of photosensitizers (PS) for photodynamic therapy (PDT). Currently, traditional PS face limitations in clinical application, including a restricted variety and narrow applicability.
View Article and Find Full Text PDFSci Rep
January 2025
Instituto de Ingeniería Energética, Universitat Politècnica de València, Valencia, Spain.
Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction.
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