2021
DOI: 10.1557/s43578-020-00095-0
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Enhanced light absorption of ultrathin crystalline silicon solar cells via the design of front nanostructured silicon nitride

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Cited by 5 publications
(2 citation statements)
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“…In the case of the latticed ARC cell, the obtained efficiency is 7.11%, but in the planar ARC cell or the bare cell is 4.37%, and 1.78%, respectively, which means the designed solar cell can have a better performance rather than the two other cases. In reference [31], as a similar work, four nanostructures are used in the front surface to enhance the performance of the silicon solar cell with a thickness of 2330 nm and short-circuit current density of 15.87 mA cm −2 . In this work, nanorod hole arrays, nanosquare hole arrays, inverted nanocone hole arrays, and inverted nanopyramid hole arrays are applied to a silicon nitride ARC layer.…”
Section: Resultsmentioning
confidence: 99%
“…In the case of the latticed ARC cell, the obtained efficiency is 7.11%, but in the planar ARC cell or the bare cell is 4.37%, and 1.78%, respectively, which means the designed solar cell can have a better performance rather than the two other cases. In reference [31], as a similar work, four nanostructures are used in the front surface to enhance the performance of the silicon solar cell with a thickness of 2330 nm and short-circuit current density of 15.87 mA cm −2 . In this work, nanorod hole arrays, nanosquare hole arrays, inverted nanocone hole arrays, and inverted nanopyramid hole arrays are applied to a silicon nitride ARC layer.…”
Section: Resultsmentioning
confidence: 99%
“…Researchers have conducted detailed research on defect detection methods for photovoltaic (PV) panels. Traditional PV defect detection primarily involves ground capacitance measurement [3] and time-domain reflectometry [4], all of which typically require the involvement of technical personnel for on-site assessment. Some researchers have applied deep learning models to PV defect detection to reduce human involvement and achieve automated defect identification.…”
Section: Photovoltaic Panel Defect Detectionmentioning
confidence: 99%