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Record W1968782840 · doi:10.1021/nl504468a

Revealing Energy Level Structure of Individual Quantum Dots by Tunneling Rate Measured by Single-Electron Sensitive Electrostatic Force Spectroscopy

2015· article· en· W1968782840 on OpenAlexafffund
Antoine Roy-Gobeil, Yoichi Miyahara, Peter Grütter

Bibliographic record

VenueNano Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsQuantum dotQuantum tunnellingCoulomb blockadeScanning tunneling spectroscopySpectroscopyElectronElectrostatic force microscopeScanning tunneling microscopeChemistryAtomic physicsCondensed matter physicsMolecular physicsMaterials scienceNanotechnologyPhysicsAtomic force microscopyVoltageTransistorQuantum mechanics

Abstract

fetched live from OpenAlex

We present theoretical and experimental studies of the effect of the density of states of a quantum dot (QD) on the rate of single-electron tunneling that can be directly measured by electrostatic force microscopy (e-EFM) experiments. In e-EFM, the motion of a biased atomic force microscope cantilever tip modulates the charge state of a QD in the Coulomb blockade regime. The charge dynamics of the dot, which is detected through its back-action on the capacitavely coupled cantilever, depends on the tunneling rate of the QD to a back-electrode. The density of states of the QD can therefore be measured through its effect on the energy dependence of tunneling rate. We present experimental data on individual 5 nm colloidal gold nanoparticles that exhibit a near continuous density of state at 77 K. In contrast, our analysis of already published data on self-assembled InAs QDs at 4 K clearly reveals discrete degenerate energy levels.

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 categoriesMeta-epidemiology (narrow)
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 score0.081
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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.

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

Citations25
Published2015
Admission routes2
Has abstractyes

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