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Record W2061964060 · doi:10.1002/adfm.201470169

Solar Cells: Pulsed Laser Ablation Based Synthesis of PbS‐Quantum Dots‐Decorated One‐Dimensional Nanostructures and Their Direct Integration into Highly Efficient Nanohybrid Heterojunction‐Based Solar Cells (Adv. Funct. Mater. 26/2014)

2014· article· en· W2061964060 on OpenAlexaff
Ibrahima Ka, Belete Atomsa Gonfa, Vincent Le Borgne, Dongling Ma, My Alı El Khakani

Bibliographic record

VenueAdvanced Functional Materials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMaterials scienceNanorodNanotechnologyHeterojunctionNanomaterialsQuantum dotPulsed laser depositionLaser ablationNanostructureNanoparticleCarbon nanotubePolymer solar cellSolar cellHybrid solar cellLaserOptoelectronicsThin filmOptics

Abstract

fetched live from OpenAlex

Nanohybrid heterojunction (NH-HJ) solar cells are developed on page 4042 by M. A. El Khakani and co-workers using pulsed laser deposition (PLD) to decorate TiO2 nanorods and single carbon nanotubes with PbS nanoparticles, and to integrate the formed nanohybrids directly into photovoltaic devices. This approach offers the latitude to achieve the direct assembly of different nanomaterials, thereby forming novel nanohybrids. By optimizing the size of the particles and tube lengths, NH-HJ solar cells exhibiting power conversion efficiencies as high as 5.3% are achieved.

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 score0.003

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 head0.193
Teacher spread0.184 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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