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Record W2802531399 · doi:10.1039/c7nr08608c

An ultra-broadband perovskite-PbS quantum dot sensitized carbon nanotube photodetector

2018· article· en· W2802531399 on OpenAlexafffund
Ibrahima Ka, Luis Felipe Gerlein, Ivy M. Asuo, Riad Nechache, Sylvain G. Cloutier

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsInstitut National de la Recherche ScientifiqueÉcole de Technologie Supérieure
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials sciencePerovskite (structure)PhotodetectorQuantum dotOptoelectronicsCarbon nanotubePhotoconductivityHalideBroadbandFacilitatorNanotechnologyChemical engineeringOpticsChemistryInorganic chemistry

Abstract

fetched live from OpenAlex

Organic-inorganic perovskites have been hailed as promising candidates for optoelectronic and photovoltaic devices, but their operation remains limited to the visible spectrum. Here, we combine single-wall carbon nanotubes, PbS quantum dots and a perovskite to synthesize hybrid devices suitable for operation in both the visible and near-infrared. The photodetectors thus fabricated show responsivities as high as 0.5 A W-1 and 0.35 A W-1 at 500 nm and at 1300 nm, respectively, with an applied bias of 1 V. Moreover, the incorporation of nanotubes within the perovskite matrix facilitates the carrier extraction, resulting in response time under 250 μs, a gain-bandwidth product of 0.1 MHz and detectivities of 1.4 × 1011 Jones and 0.9 × 1011 Jones at 500 nm and at 1300 nm, respectively. This unique approach opens new pathways for the development of low-cost, high-speed and broadband perovskite-based optoelectronic devices for large-scale manufacturing.

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 score0.023
Threshold uncertainty score0.910

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 studies0.0000.000
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 head0.009
GPT teacher head0.226
Teacher spread0.218 · 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

Citations45
Published2018
Admission routes2
Has abstractyes

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