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Record W3213810179 · doi:10.1002/pssb.202100504

Resistively Detected NMR Lineshapes in a Local Filling ν<1 Quantum Hall Breakdown

2021· article· en· W3213810179 on OpenAlexaff
Mohammad Hamzah Fauzi, Takeshi Sobue, Annisa Noorhidayati, Ken Sato, Katsushi Hashimoto, Y. Hirayama

Bibliographic record

Venuephysica status solidi (b) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsInnovation Cluster (Canada)
FundersTohoku University
KeywordsQuantum Hall effectCondensed matter physicsPhysicsNuclear magnetic resonanceMaterials scienceQuantum mechanicsMagnetic field

Abstract

fetched live from OpenAlex

Resistively‐detected nuclear magnetic resonance (RDNMR) is a unique characterization method enabling highly sensitive NMR detection for a single quantum nanostructure, such as a quantum point contact (QPC). In many studies, dynamic nuclear polarization and RDNMR detection are used in a quantum Hall breakdown regime of a local QPC filling factor of 1 ( ν qpc = 1). However, the RDNMR lineshapes are complicated and still not fully understood yet. Herein, the nuclear spins are systematically polarized by current pumping from the close vicinity of the ν qpc = 1 conductance plateau all the way down to pinch‐off point, providing clear evidence that the spin‐flip scattering between two edge channels at the lowest Landau level still occurs in the constriction even when it is close to the pinch off point. The collected RDNMR spectra reveal two sets of distinguished features. First, in a strong to intermediate tunneling regime, we observe an ordinary resistance dip lineshape with snake‐like transition frequencies, indicative of spatial modulation of electron density in the QPC. Second, in a weak tunneling regime, the spectrum turns into a dispersive lineshape, which is interpreted due to the build‐up of two sets of nuclear spin polarization that are in contact with different electron spin polarization.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
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
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.233
Teacher spread0.223 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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