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Record W3203767172

Ultrasensitive solution-cast quantum dot photodetectors

2007· article· en· W3203767172 on OpenAlexaffabout
Gerasimos Konstantatos, Ian A. Howard, A. Fischer, Sjoerd Hoogland, Jason Clifford, Ethan J. D. Klem, Larissa Levina, Edward H. Sargent

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhotodetectorSpecific detectivityOptoelectronicsPhotoconductivityMaterials scienceEpitaxyQuantum dotFigure of meritFabricationQuantum efficiencyDark currentNanotechnologyLayer (electronics)
DOInot available

Abstract

fetched live from OpenAlex

A practical solution The best electronic and optoelectronic devices are built via semiconductor crystal growth on a single-crystal substrate. Over 100 papers have been published in recent years in Nature on alternative devices, produced instead from the solution phase. They have some advantages over conventional crystalline semiconductor devices: ease of fabrication, physical flexibility and — most important — low cost. The problem was the poor electronic performance of solution-processed devices, compared with single-crystal counterparts. But that could change now: a team from the University of Toronto reports that one such system — colloidal quantum dots of lead sulphide — can actually outperform the state-of-the-art crystalline alternative.

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)0.001
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 score0.032
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.282
Teacher spread0.260 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

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