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Record W4399178912 · doi:10.18280/mmep.110520

Influence of Annealing Process on Cu2O Nanofilm and the Efficiency of Annealed p-Cu2O/n Si Nanostructure Solar Cell Prepared by Thermal Evaporation Technique

2024· article· en· W4399178912 on OpenAlexvenueno aff
Inass Abdulah Zgair, Abdulazeez O. Mousa Al-Ogail, Khalid Haneen Abass

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldMaterials Science
TopicCopper-based nanomaterials and applications
Canadian institutionsnot available
Fundersnot available
KeywordsNanostructureMaterials scienceAnnealing (glass)Solar cellChemical engineeringThermalEvaporationNanotechnologyOptoelectronicsMetallurgy

Abstract

fetched live from OpenAlex

Thin film solar cells are one of the significant electronic applications due to their beneficial characteristics, especially adjustable optical features low cost, and high efficiency.In this research, p-type cupric oxide (Cu2O) nanofilms have been successfully deposited onto glass and n-type Si substrates by thermal evaporation technique under 10 -7 mbar, rate of deposition of 0.3 nm/s after that annealed at 200℃.The annealing process leads to an increase in the uniformity and homogeneity distribution of particles on the thin film surface as well as an improvement of the roughness.FE-SEM images of Cu2O nanofilms show that the average size has been increased from 25.15 to 35.36 with the enhancement of the distribution of particles after the annealing process.Average roughness and root mean square have increased from (0.166 to 1.18) nm and (0.213 to 1.490) nm respectively.Electrical characteristics of annealed Al/ Cu2ONPs/Si/Al solar cell were examined by current-voltage measurement.The short circuit current (ISC.) of 15 mA, open circuit voltage (VOC) of 500 mV, fill factor (F.F) of 0.45, and the efficiency (η) was found to be 3.37%.The annealing temperature gives more roughness to the surface and raises the absorption of incident photons and solar cell efficiency.

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)0.001
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 score0.144
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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
Published2024
Admission routes1
Has abstractyes

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