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Record W2332383468 · doi:10.1021/nn302744e

Current Saturation in Field Emission from H-Passivated Si Nanowires

2012· article· en· W2332383468 on OpenAlex
M. Choueib, Richard Martel, Costel‐Sorin Cojocaru, A. Ayari, P. Vincent, Stephen T. Purcell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Nano · 2012
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPassivationDangling bondMaterials scienceNanowireSaturation (graph theory)DopingSemiconductorOptoelectronicsField electron emissionSaturation currentHydrogenAnalytical Chemistry (journal)NanotechnologyChemical physicsSiliconVoltageChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

This paper explores the field emission (FE) properties of highly crystalline Si nanowires (NWs) with controlled surface passivation. The NWs were batch-grown by the vapor-liquid-solid process using Au catalysts with no intentional doping. The FE current-voltage characteristics showed quasi-ideal current saturation that resembles those predicted by the basic theory for emission from semiconductors, even at room temperature. In the saturation region, the currents were extremely sensitive to temperature and also increased linearly with voltage drop along the nanowire. The latter permits the estimation of the doping concentration and the carrier lifetime, which is limited by surface recombination. The conductivity could be tuned over 2 orders of magnitude by in situ hydrogen passivation/desorption cycles. This work highlights the role of dangling bonds in surface leakage currents and demonstrates the use of hydrogen passivation for optimizing the FE characteristics of Si NWs.

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.

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 score0.116
Threshold uncertainty score0.337

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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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