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Record W2111329502 · doi:10.1002/cssc.201301139

Green‐Engineered All‐Substrate Mesoporous TiO<sub>2</sub> Photoanodes with Superior Light‐Harvesting Structure and Performance

2014· article· en· W2111329502 on OpenAlexafffund
Nima Parsi Benehkohal, George P. Demopoulos

Bibliographic record

VenueChemSusChem · 2014
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrophoretic depositionDye-sensitized solar cellBifunctionalMaterials scienceMesoporous materialAnataseElectrodeNanotechnologyChemical engineeringRutileSubstrate (aquarium)PhotocatalysisChemistryCoatingOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Electrophoretic deposition (EPD) is employed successfully in a suspension of multicomponent TiO2 nanoparticulates of different sizes and morphologies to engineer a very robust bifunctional electrode structure for dye-sensitized solar cell (DSSC) applications that shows excellent light-harvesting and photoelectrochemical performance. Aqueous-synthesized anatase nanocrystallites and sub-micrometer-sized "sea urchin"-like rutile aggregates are formulated in a stable isopropanol suspension without resorting to binders or charging agents. Interestingly, extremely robust films are obtained because of the high surface reactivity, electrophoretic mobility, and unique morphology of the rutile aggregates. DSSCs built with the newly configured bifunctional electrode yielded a record efficiency (8.59 %) for EPD-fabricated devices without resorting to mechanical compression. Such green-engineered mesoporous electrode structures can be built on both metallic and plastic substrates and can find applications in various energy and environmental fields.

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)0.0000.000

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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

Citations18
Published2014
Admission routes2
Has abstractyes

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