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Record W4407751630 · doi:10.37394/232016.2025.20.5

Investigation of the Impact of Different Materials on the Efficiency of Lead-free Perovskite Solar Cell

2025· article· en· W4407751630 on OpenAlexaff
Mostafa M. Salah, Menna Elkomy, Omar E. Mohamed, Mohamed Hamouda, Marina Sobhy, Mohamed Mousa

Bibliographic record

VenueWSEAS TRANSACTIONS ON POWER SYSTEMS · 2025
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsIndependent Electricity System Operator
Fundersnot available
KeywordsLead (geology)Perovskite (structure)Solar cellMaterials sciencePerovskite solar cellEngineering physicsNanotechnologyChemical engineeringOptoelectronicsGeologyEngineering

Abstract

fetched live from OpenAlex

A solar cell is an electrical device that converts light energy into electrical energy via the photovoltaic effect. Sometimes called a photovoltaic cell or PV cell. In essence, a solar cell is a p-n junction diode. Solar cells are a type of photoelectric cell, which is characterized as an apparatus that changes its electrical properties in response to light, including resistance, voltage, and current. Organic-inorganic halide-based perovskite solar systems are getting closer to commercialization and have become more efficient. Because lead-based perovskite materials have toxicity issues, the scientific community has recently become interested in lead-free alternatives. A lead-free n-i-p based planar heterostructure perovskite solar cell made of intrinsic-CH3NH3SnI3 methyl ammonium tin iodide (MASnI3) as an i- and p-layer Spiro-OMeTAD with SnO2 for the n layer is optimized for device efficiency using SCAPS numerical simulation. The 3rd layer (electron layer) is modified with an efficiency of 2.03%, with another material SnO2, as the efficiency increased to 2.62%, Voc of 0.6428V, Jsc = 6.44 mA/cm2, and FF of 63.40% are achieved. After that the cell layers of the cell are optimized to achieve the highest efficiency of 10.13%.

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 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 score0.019
Threshold uncertainty score0.354

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)0.0000.000

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 head0.208
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.

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
Published2025
Admission routes1
Has abstractyes

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