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Record W2002111550 · doi:10.1117/12.905671

Fe doped TiO 2 nanofibers on the surface of graphene sheets for photovoltaics applications

2011· article· en· W2002111550 on OpenAlex
Nasrin Farhangi, Yaocihuatl Medina‐González, Paul A. Charpentier

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsWestern University
Fundersnot available
KeywordsPhotovoltaicsMaterials scienceGrapheneDopingNanofiberNanotechnologyOptoelectronicsPhotovoltaic systemElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Highly ordered, visible light driven TiO2 nanowire arrays doped with Fe photocatalysts were grown on the surface of functionalized graphene sheets (FGSs) using a sol-gel method with titanium isopropoxide (TIP) monomer, acetic acid (HAc) as the polycondensation agent and iron chloride in the green solvent, supercritical carbon dioxide (scCO 2 ). The morphology of the synthesized materials was studied by SEM and TEM, which showed uniform formation of Fe doped TiO 2 nanofibers on the surface of graphene sheets, which acted as a template for nanowire growth through surface -COOH functionalities. Increasing Fe content in the nanowires did not change the morphology significantly. Optical properties of the synthesized composites were examined by UV spectroscopy which showed a significant reduction in band gap with increasing Fe content, i.e. 2.25 eV at 0.6% Fe. The enhancement of the optical properties of synthesized materials was confirmed by photocurrent measurement. The optimum sample containing 0.6% Fe doped TiO 2 on the graphene sheets increased the power conversation efficiency by 6-fold in comparison to TiO 2 alone.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0000.000

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 head0.018
GPT teacher head0.221
Teacher spread0.203 · 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