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Record W3214065712 · doi:10.1021/acsanm.1c02754

Hydrogenated Silicon Nanoclusters with a Permanent Electric Dipole Moment for the Controlled Assembly of Silicon-Based Nanostructures

2021· article· en· W3214065712 on OpenAlexaff
Fatme Jardali, Bryan P. Keary, Tatiana Perrotin, François Silva, Jean‐Charles Vanel, Yvan Bonnassieux, Stéphane Mazouffre, Albert A. Ruth, Mohamed E. Leulmi, Holger Vach

Bibliographic record

VenueACS Applied Nano Materials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsConcordia University
FundersCentre National de la Recherche ScientifiqueScience Foundation Ireland
KeywordsNanoclustersSiliconMaterials scienceDipoleNanotechnologySubstrate (aquarium)Electric dipole momentMoment (physics)NanostructureEvaporationOptoelectronicsChemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

While silicon nanoclusters have extensively been used for their outstanding properties for many decades, never before has their dipole moment been exploited for any application. Here, we have succeeded in producing hydrogenated silicon nanoclusters with a strong permanent electric dipole moment. This dipole moment allows us to use electric fields in order to orient and guide individual clusters. As a first example, we demonstrate the catalyst-free one-by-one self-assembly of one of the thinnest silicon nanowires yet observed. As a second example, we show that the simple presence of those nanoclusters on LaB6 cathodes leads to a 30-fold enhancement of the thermionic electron current density over pristine LaB6. Last but not least, the nanoclusters provide a protective layer against chemical and mechanical attack and largely prevent the evaporation of substrate materials, potentially increasing the operational lifetime of cathodes substantially.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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