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Record W1997944327 · doi:10.1021/nn401396y

Joint Mapping of Mobility and Trap Density in Colloidal Quantum Dot Solids

2013· article· en· W1997944327 on OpenAlexaff
Philipp Stadler, Brandon R. Sutherland, Yuan Ren, Zhijun Ning, Abdolreza Simchi, Susanna M. Thon, Sjoerd Hoogland, Edward H. Sargent

Bibliographic record

VenueACS Nano · 2013
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQuantum dotMaterials scienceTransistorBand gapNanotechnologyOptoelectronicsField-effect transistorNanocrystalPhysicsVoltage

Abstract

fetched live from OpenAlex

Field-effect transistors have been widely used to study electronic transport and doping in colloidal quantum dot solids to great effect. However, the full power of these devices to elucidate the electronic structure of materials has yet to be harnessed. Here, we deploy nanodielectric field-effect transistors to map the energy landscape within the band gap of a colloidal quantum dot solid. We exploit the self-limiting nature of the potentiostatic anodization growth mode to produce the thinnest usable gate dielectric, subject to our voltage breakdown requirements defined by the Fermi sweep range of interest. Lead sulfide colloidal quantum dots are applied as the active region and are treated with varying solvents and ligands. In an analysis complementary to the mobility trends commonly extracted from field-effect transistor studies, we focus instead on the subthreshold regime and map out the density of trap states in these nanocrystal films. The findings point to the importance of comprehensively mapping the electronic band- and gap-structure within real quantum solids, and they suggest a new focus in investigating quantum dot solids with an aim toward improving optoelectronic device performance.

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 score0.107
Threshold uncertainty score0.306

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 head0.033
GPT teacher head0.227
Teacher spread0.194 · 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

Citations35
Published2013
Admission routes1
Has abstractyes

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