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Record W4380989079 · doi:10.4236/jmp.2023.147056

Realization of High Efficient Ferroelectric Perovskite Nanoparticles in Biopolymer-Matrix Solar Cells under Low Lighting

2023· article· en· W4380989079 on OpenAlexfundno aff
Rémi Ndioukane, Fanta Baldé, Ndéye Coumba Yandé Fall, Abdou Karim Diallo, Diouma Kobor, Jeanne Solard, Laurence Motte

Bibliographic record

VenueJournal of Modern Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsOpen-circuit voltageFerroelectricityMaterials sciencePolarization (electrochemistry)OptoelectronicsShort circuitPerovskite (structure)VoltageRealization (probability)Current densitySolar cellDielectricChemical engineeringElectrical engineeringPhysicsChemistry

Abstract

fetched live from OpenAlex

The idea to use ferroelectric materials (PZN-PT) came from the fact that the ferroelectric nature could facilitate electric charges accumulation on the interfaces of the solar cell. Thus, it would increase the open circuit voltage Voc which could reach more than 10 V. This would directly impact the efficiency which is proportional to Voc, thus hoping to obtain solar efficiency never equaled by the halide perovskites which are less stable and less resistant in aggressive environments. In this work, the solar cells produced gave an exceptional record efficiency of 39.32% with a very high open circuit voltage (Voc) of 3.50 V, a short-circuit current density (Jsc) of 0.118 mA/cm2 and an FF of 0.72 measured in the positive polarization direction under 3825 lux (5.6 W/m2) lighting. The negative polarization direction under 4781 lux (7 W/m2) lightning gave a current density of 2 mA/cm2, an open circuit voltage of 2.30 V and an FF of 0.35.

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 score0.001
Threshold uncertainty score0.002

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 head0.225
Teacher spread0.216 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueJournal of Modern PhysicsSame topicPerovskite Materials and ApplicationsFrench-language works237,207