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Record W4214913667 · doi:10.3390/nano12050826

Low-Cost Inorganic Strontium Ferrite a Novel Hole Transporting Material for Efficient Perovskite Solar Cells

2022· article· en· W4214913667 on OpenAlexaff
Ankush Kumar Tangra, Mohammed Benali Kanoun, Souraya Goumri‐Said, Ahmed‐Ali Kanoun, Kevin P. Musselman, Jaspinder Kaur, Gurmeet Singh Lotey

Bibliographic record

VenueNanomaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Waterloo
FundersAlfaisal UniversityIndian Institute of Technology Delhi
KeywordsMaterials sciencePerovskite (structure)Energy conversion efficiencyStrontiumZinc ferriteOptoelectronicsPerovskite solar cellSolar cellNanotechnologyZincChemical engineeringMetallurgyChemistry

Abstract

fetched live from OpenAlex

Perovskite solar cells attract significant interest due to their high-power conversion efficiencies. The replacement of charge-transporting layers using inorganic materials is an effective approach for improving stability and performance, as these materials are low-cost, highly durable, and environmentally friendly. This work focuses on the inorganic hole and electron transport layers (HTL and ETL), strontium ferrite (SrFe2O4), and zinc oxide (ZnO), respectively, to enhance the efficiency of perovskite solar cells. Favorable band alignment and high charge-collection capability make these materials promising. Experimental and computational studies revealed that the power conversion efficiency of the fabricated device is 7.80% and 8.83%, respectively. Investigating electronic properties and interface charge transfer through density functional theory calculations further corroborated that SrFe2O4 is a good HTL candidate. Our numerical device modeling reveals the importance of optimizing the thickness (100 nm and 300 nm) of the HTL and perovskite layers and defect density (1016 cm−3) of the absorber to achieve better solar cell performance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0020.000

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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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 teacher head, not a consensus.

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

Citations13
Published2022
Admission routes1
Has abstractyes

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