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Record W1994918237 · doi:10.1063/1.4826198

Monitoring the Fermi-level position within the bandgap on a single nanowire: A tool for local investigations of doping

2013· article· en· W1994918237 on OpenAlexafffund
Mattia Fanetti, Stefano Ambrosini, Matteo Amati, Luca Gregoratti, Majid Kazemian Abyaneh, A. Franciosi, A. C. E. Chia, Ray LaPierre, S. Rubini

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanowireDopingMaterials scienceDopantFermi levelBand gapPhotoemission spectroscopyOptoelectronicsCondensed matter physicsNanotechnologyX-ray photoelectron spectroscopyPhysics

Abstract

fetched live from OpenAlex

The control of the doping in nanowires (NWs) is of fundamental importance for the implementation of NW-based devices. A method is presented to obtain local information about doping by monitoring the Fermi-energy position within the bandgap at the surface along single NWs through spatially resolved x-ray photoemission spectroscopy. The experimental results are complemented by theoretical simulations of the carrier profile, taking into account the presence of electronic surface states and quantifying the impact of carrier depletion at the NW surface. This combined approach allows to determine the effect of the incorporation of Si dopants in GaAs NWs following different growth protocols, such as vapor-liquid-solid axial growth or vapor-solid radial growth, and in the resulting core-shell structures and axial junctions. The method also revelaed the strong dependence of the resulting doping on the morphology of the single NW (orientation, shell thickness). This approach can be easily applied to other nanoscale objects, allowing the direct observation of how doping (or junctions, or adsorbates,…) may locally affect the position of the Fermi level at the surface, which is a crucial factor in several application fields, such as photovoltaic and photocatalysis.

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 score0.309
Threshold uncertainty score0.249

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 head0.029
GPT teacher head0.227
Teacher spread0.198 · 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

Citations10
Published2013
Admission routes2
Has abstractyes

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