MétaCan
Menu
Back to cohort
Record W2343176728 · doi:10.1021/acs.jpcc.5b04307

Tuning the Electronic Properties of a Boron-Doped Si(111) Surface by Self-Assembling of Trimesic Acid

2015· article· en· W2343176728 on OpenAlexafffund
Farzaneh Shayeganfar, Alain Rochefort

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTrimesic acidMonolayerMaterials scienceAdsorptionHydrogen bondChemical physicsSubstrate (aquarium)Surface energyDopingSiliconMoleculeNanotechnologyCrystallographyComputational chemistryPhysical chemistryChemistryOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

The influence of self-assembled trimesic acid (TMA) on the electronic properties of a heavily boron-doped silicon surface was investigated using first-principles DFT calculations. Our results demonstrate that the adsorption of isolated TMA molecules, small molecular islands, or complete monolayers is characterized by significant adsorption energy and electron charge transfer to the Si–B interface, while the bonding character of TMA to the surface remains essentially noncovalent. The stability of the adsorbed species was ensured by an attractive interaction from the Si–B interface but also through the formation of hydrogen bonds between TMA units. Beyond this significant stability of the different TMA adlayers, the weak dispersion and the energy level position of states associated with the TMA moieties observed in the band gap region of the Si–B interface suggest that the adsorbed layer can be used to tune the electronic properties of the substrate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicSurface and Thin Film PhenomenaFrench-language works237,207