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Reliable lateral and vertical manipulations of a single Cu adatom on a Cu(111) surface with multi-atom apex tip: semiempirical and first-principles simulations

2008· article· en· W2073160622 on OpenAlexaff
Yiqun Xie, Qingwei Liu, Peng Zhang, Wenqing Zhang, Songyou Wang, Min Zhuang, Yufen Li, Fuxi Gan, Jun Zhuang

Bibliographic record

VenueNanotechnology · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversité de Montréal
FundersFudan UniversityNational Natural Science Foundation of China
KeywordsTrimerApex (geometry)Materials scienceAtom (system on chip)DimerMolecular physicsPhysicsGeometryNuclear magnetic resonance

Abstract

fetched live from OpenAlex

We study the reliability of the lateral manipulation of a single Cu adatom on a Cu(111) surface with single-atom, dimer and trimer apex tips using both semiempirical and first-principles simulations. The dependence of the manipulation reliability on tip height is investigated. For the single-atom apex tip the manipulation reliability increases monotonically with decreasing tip height. For the dimer and trimer apex tips the manipulation reliability is greatly improved compared to that for the single-atom apex tip over a certain tip-height range. Two kinds of mechanism are found responsible for this improvement. One is the so-called enhanced interaction mechanism in which the lateral tip-adatom interaction in the manipulation direction is improved. The other is the suspended atom mechanism in which the relative lateral trapping ability of the tip is improved due to the strong vertical attraction of the tip on the adatom. Both mechanisms occur in the manipulations with the trimer apex tip, while in those with the dimer apex tip only the former is effective. Moreover, we present a method to realize reversible vertical manipulation of a single atom on a Cu(111) surface with the trimer apex tip, based on its strong vertical and lateral attraction on the adatom.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.562

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 head0.037
GPT teacher head0.240
Teacher spread0.204 · 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 designObservational
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

Citations13
Published2008
Admission routes1
Has abstractyes

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