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Record W3110818692 · doi:10.1016/j.ssel.2020.09.001

Impacts of Indirect Wider Bandgap of Non-Toxic AlxGa1-xAs Buffer in Copper-Indium-Gallium-Diselenide Photovoltaic Cell

2020· article· en· W3110818692 on OpenAlexaff
Sadia Islam Shachi, Nusrat Jahan, Ali Newaz Bahar, Md. Asaduzzaman

Bibliographic record

VenueSolid State Electronics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCopper indium gallium selenide solar cellsMaterials scienceGalliumOptoelectronicsIndiumBand gapBuffer (optical fiber)Photovoltaic systemOpen-circuit voltageDiselenideShort circuitSolar cellEnergy conversion efficiencyCopperLayer (electronics)Current densityAlloyVoltageComposite materialMetallurgySeleniumElectrical engineering

Abstract

fetched live from OpenAlex

A numerical simulation and substantiation have been accomplished to analyze the impact of Al0.9Ga0.1As alloy composite buffer layer band gap and thickness, absorber layer thickness on a ZnO:Al/i-ZnO/Al0.9Ga0.1As/CIGS/Mo/SLG structured non-toxic Cd-free CIGS photovoltaic cell. In this study, the cell output attributes including efficiency (η) and collection efficiency (ηc) have been optimized through short circuit current density (Jsc), open-circuit voltage (Voc) and fill factor (FF) optimization. Our study has been concluded with the maximum efficiency of 24.32% with Voc = 839.76 mV, Jsc = 36.21mA/cm2 and FF=76.96%, ηc = 83.16%. This enhanced efficiency is optimized by determining the bandgap of the buffer through altering the Al concentration to transit it from direct bandgap material to an indirect one. The thickness of the absorber on system performance is also investigated, and its extent is found in between 2 µm to 3 µm.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.212
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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Same venueSolid State Electronics LettersSame topicChalcogenide Semiconductor Thin FilmsFrench-language works237,207