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Record W1501809206 · doi:10.1155/2015/450405

A Comparison of the Performances of Different Mesoporous Titanias in Dye‐Sensitized Solar Cells

2015· article· en· W1501809206 on OpenAlexaff
Alessandro Latini, Riccardo Panetta, Carmen Cavallo, D. Gozzi, Simone Quaranta

Bibliographic record

VenueJournal of Nanomaterials · 2015
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsOntario Tech University
FundersUniversità degli Studi della BasilicataSapienza Università di Roma
KeywordsMaterials scienceMesoporous materialDye-sensitized solar cellChemical engineeringNanotechnologyEngineering physicsPhysical chemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

The present work aims at optimizing titanium dioxide morphology for dye‐sensitized solar cells applications. Five different anatase phase mesoporous titanias were prepared and tested as photoanodes in dye‐sensitized solar cells. The materials were prepared by using a template approach. Two materials were synthesized by using monodisperse silica nanospheres and the other three using two different organic templating agents (Pluronic P123 and Brij 58). A complete characterization of the obtained materials was performed by powder XRD, FEG‐SEM, UV‐Vis reflectance spectroscopy, BET surface area measurements, and TG‐DTA. Several cells were assembled using N719 as dye and a nonvolatile electrolyte based on benzonitrile. The cells were tested by means of J-V curves under simulated solar radiation, IPCE, and dark current measurements. The highest efficiencies were achieved with titania prepared by using Pluronic P123 as template (ηmax = 6.8%), while the lowest efficiencies were recorded with using titania samples prepared with the silica nanospheres template (ηmin = 5.7%). The different performances of the samples are examined and discussed.

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 science0.0000.000
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 head0.035
GPT teacher head0.285
Teacher spread0.250 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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