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Record W2597977089 · doi:10.1038/srep45543

High-performance nanotube-enhanced perovskite photodetectors

2017· article· en· W2597977089 on OpenAlexafffund
Ibrahima Ka, Luis Felipe Gerlein, Riad Nechache, Sylvain G. Cloutier

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotodetectorPhotocurrentPerovskite (structure)Materials scienceOptoelectronicsCarbon nanotubeNanotubeCharge carrierNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Abstract Organic-inorganic perovskites have already shown a tremendous potential for low-cost light-harvesting devices. Yet, the relatively low carrier mobilities in bulk perovskites still prevent large-area devices with performances competing with state-of-the-art technologies. Here, we tackle this fundamental challenge by incorporating single-wall carbon nanotubes within a perovskite matrix by means of a simple two-step method in ambient air. Using this nano-engineered hybrid film, we demonstrate large-area photodetectors with responsivities up-to 13.8 A.W −1 and a broad spectral response from 300 to 800 nm, indicating that photocurrent generation arises from the charge transfer from the perovskite matrix to the embedded nanotube network. As the nanotubes facilitate the carrier extraction, these photodetectors also show a fast response time of 10 ms. This is significantly faster than most of previous reports on perovskite-based photodetectors, including devices with much smaller photosensitive areas. This approach is also well-suited for large-scale production of other perovskite-based light-harvesting devices.

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 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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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 teacher head, 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

Citations44
Published2017
Admission routes2
Has abstractyes

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