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Scanning gate imaging of two coupled quantum dots in single-walled carbon nanotubes

2014· article· en· W2038509819 on OpenAlexaff
Xin Zhou, James Hedberg, Yoichi Miyahara, Peter Grütter, Koji Ishibashi

Bibliographic record

VenueNanotechnology · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsMcGill University
Fundersnot available
KeywordsQuantum dotCarbon nanotubeConductanceMaterials scienceCarbon nanotube quantum dotCapacitanceScanning gate microscopyNanotubeCoupling (piping)NanotechnologyQuantum point contactQuantum capacitanceScanning electron microscopeQuantum wireCondensed matter physicsMolecular physicsElectrodeQuantumScanning capacitance microscopyQuantum wellOpticsPhysicsComposite materialScanning confocal electron microscopy

Abstract

fetched live from OpenAlex

Two coupled single wall carbon nanotube quantum dots in a multiple quantum dot system were characterized by using a low temperature scanning gate microscopy (SGM) technique, at a temperature of 170 mK. The locations of single wall carbon nanotube quantum dots were identified by taking the conductance images of a single wall carbon nanotube contacted by two metallic electrodes. The single electron transport through single wall carbon nanotube multiple quantum dots has been observed by varying either the position or voltage bias of a conductive atomic force microscopy tip. Clear hexagonal patterns were observed in the region of the conductance images where only two sets of overlapping conductance rings are visible. The values of coupling capacitance over the total capacitance of the two dots, C(m)/C(1(2)) have been extracted to be 0.21 ∼ 0.27 and 0.23 ∼ 0.28, respectively. In addition, the interdot coupling (conductance peak splitting) has also been confirmed in both conductance image measurement and current-voltage curves. The results show that a SGM technique enables spectroscopic investigation of coupled quantum dots even in the presence of unexpected multiple quantum dots.

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)0.001
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 score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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