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Record W2782943376 · doi:10.14447/jnmes.v18i4.352

A Heterojunction Based on Macro-porous Silicon and Zinc Oxide for Solar Cell Application

2015· article· en· W2782943376 on OpenAlexvenueno aff
N. Mendoza-Agüero, Vivechana Agarwal, Heidi Isabel Villafán-Vidales, J. Campos‐Álvarez, P.J. Sebastián

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceHeterojunctionOptoelectronicsSolar cellThin filmSiliconSubstrate (aquarium)Porous siliconLayer (electronics)Energy conversion efficiencyPhotovoltaic systemZincNanotechnologyMetallurgyElectrical engineering

Abstract

fetched live from OpenAlex

Transparent and conductive Al doped zinc oxide (AZO) films were reactively sputtered from metallic targets onto macro-porous silicon (MPS) substrate to fabricate a heterojunction interface structure. A tungsten oxide (WO3) thin film was placed between metallic aluminum back contact and bulk silicon to extract photogenerated holes from the absorber. Due to the susceptibility of PS to naturally oxidize over the period of time, a thin film of SiO2 was thermally grown to stabilize the electrical response of the junction. Such thin layer acts as passive film to prevent recombination and is placed between the p-n junction. Photovoltaic properties of this heterojunction were studied by using the current density-voltage (J-V) measurement under AM 1.5 illumination. The experimental results show an increase in photovoltaic performance of AZO/MPS solar cell with a buffer layers of WO3. Such heterostructures are promising for the development of the low-cost, clean, and durable devices with appreciable light-to-electricity conversion 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 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 science0.0010.000
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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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