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Record W2796123810 · doi:10.1002/cplu.201800046

Towards Long‐Term Thermal Stability of Dye‐Sensitized Solar Cells Using Multiwalled Carbon Nanotubes

2018· article· en· W2796123810 on OpenAlexaff
Mahyar Mohammadnezhad, Gurpreet Singh Selopal, Zhiming M. Wang, B.L. Stansfield, Haiguang Zhao, Federico Rosei

Bibliographic record

VenueChemPlusChem · 2018
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsDye-sensitized solar cellAnodeMaterials scienceNanocompositeCarbon nanotubeThermal stabilityChemical engineeringCarbon fibersDegradation (telecommunications)NanotechnologyThermalComposite materialElectrodeChemistryComposite number

Abstract

fetched live from OpenAlex

Abstract In this study, the effect of incorporating multiwalled carbon nanotubes (MWCNTs) on the thermal stability of dye‐sensitized solar cells (DSSCs) was investigated. Under identical measurement conditions (aging at 80 °C for 240 h), DSSCs based on a bare TiO2 anode presented a significant loss in photoconversion efficiency (PCE), dropping to 59 % of their initial value, whereas the DSSCs based on a TiO2–MWCNT nanocomposite anode attained a promising thermal stability with only 20 % loss of PCE. This degradation of cell performance is mainly associated with a dramatic reduction in the short‐circuit current density (Jsc). To understand the mechanisms that underpin these changes in device performance under thermal stress, both types of cells were investigated using various techniques. The incorporation of MWCNTs could eliminate the formation of cracks and improve electron charge transfer. The results of this study indicate a promising new method to enhance the thermal stability of DSSCs using a nanocomposite anode made of one‐dimensional carbon materials.

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)0.001
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
Metaresearch0.0000.001
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 head0.030
GPT teacher head0.256
Teacher spread0.227 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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