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Record W2045593685 · doi:10.1103/physrevb.62.2669

Photoconductivity in CdSe quantum dot solids

2000· article· en· W2045593685 on OpenAlexfundno aff
C. A. Leatherdale, Cherie R. Kagan, Nicole Y. Morgan, S. A. Empedocles, M. A. Kastner, Moungi G. Bawendi

Bibliographic record

VenuePhysical review. B, Condensed matter · 2000
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMaterials Research Science and Engineering Center, Harvard UniversityNational Science Foundation
KeywordsQuantum dotPhotoconductivityQuantum tunnellingElectric fieldMaterials scienceExcitonCondensed matter physicsPhotoluminescenceElectronQuenching (fluorescence)Atomic physicsOptoelectronicsPhysicsFluorescenceOptics

Abstract

fetched live from OpenAlex

We report measurements of photoconductivity and electric field induced photoluminescence quenching in three-dimensional close-packed solids of colloidal CdSe quantum dots. Our measurements suggest that photoexcited, quantum confined excitons are ionized by the applied electric field with a rate that depends on both the size and surface passivation of the quantum dots. Separation of electron-hole pairs confined to the core of the quantum dot requires significantly more energy than separation of carriers trapped at the surface and occurs through tunneling processes. We present a simple resonant tunneling model for the initial charge separation step that qualitatively reproduces both the size and surface dependence of the photoconductivity as a function of applied field. We show that the charge generation efficiency increases with increasing temperature as nonradiative and radiative recombination pathways increasingly compete with charge separation.

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 score0.002
Threshold uncertainty score0.004

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 studies0.0000.001
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 head0.027
GPT teacher head0.308
Teacher spread0.280 · 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

Citations293
Published2000
Admission routes1
Has abstractyes

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