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Record W4413051632 · doi:10.1021/acsnano.4c18016

Electronic Control of Silicon Surface Atomic Structures with Two-Probe Scanning Tunneling Microscopy

2025· article· en· W4413051632 on OpenAlexafffund
Jo Onoda, Lucian Livadaru, Robert A. Wolkow, Jason Pitters

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsNational Research Council CanadaNational Institute for NanotechnologyUniversity of Alberta
FundersAlberta Innovates - Technology FuturesCompute CanadaNatural Sciences and Engineering Research Council of CanadaIketani Science and Technology FoundationNational Research CouncilSumitomo Foundation
KeywordsScanning tunneling microscopeDangling bondSiliconConductive atomic force microscopyMaterials scienceAtomic unitsOptoelectronicsQuantum tunnellingScanning probe microscopyElectrochemical scanning tunneling microscopeBand bendingNanotechnologyScanning tunneling spectroscopyMolecular physicsChemistryAtomic force microscopyPhysics

Abstract

fetched live from OpenAlex

Dangling bonds (DBs) on the H-terminated Si(100) surface have stimulated much interest in exploring atomic-scale devices. Although multiprobe scanning tunneling microscope (STM) can be utilized as an ideal tool to characterize DB architectures, studying these on low conductive Si substrates remains a challenge since the effects such as large screening length and long mean-free path for carriers can emerge during measurements. Here, we report the effects of minority carrier (hole for n-type Si) injection on DBs with two-probe STM. While one STM probe was used to characterize the surfaces, another one was placed in the distance to inject holes into the Si substrates. We found that in steady state, migrating holes can negate band bending at the STM imaging areas and that the average charge states of DBs can be controlled by the amount of injected holes. We also investigated a DB island crafted on the H-terminated Si surface, which, as a result of hole injection, shows image features not ordinarily seen at the applied bias, confirming that the hole injection induces a shift of the STM apparent imaging bias and additional gap states in I – V measurements. These findings are important for understanding atomic-scale devices on low conductive substrates.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.660

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 head0.004
GPT teacher head0.238
Teacher spread0.234 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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