MétaCan
Menu
Back to cohort
Record W2067444692 · doi:10.1021/ic051431r

Dehydrogenative Silane Coupling on Silicon Surfaces via Early Transition Metal Catalysis

2006· article· en· W2067444692 on OpenAlexaff
Yunhui Li, Jillian M. Buriak

Bibliographic record

VenueInorganic Chemistry · 2006
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsSilanesChemistrySilaneSiliconHydrosilylationHydrideCatalysisSurface modificationOrganosiliconTitaniumTransition metalPhotochemistryMetalChemical engineeringPolymer chemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Derivatization of silicon surfaces is an area of intense interest due to the centrality of silicon in the microelectronics industry and because of potential promise for a myriad of other applications. In this paper, we investigate the feasibility of Si-Si bond formation directly on the surface to contrast with the more widely studied Si-C and Si-O bond forming reactions. Functionalization of hydride-terminated silicon surfaces with silanes is carried out via early transition metal mediated dehydrogenative silane coupling reactions. Zirconocene and titanocene catalyst systems were evaluated for heterocoupling of a molecular silane, RSiH3, with a surface Si-H group on Si(s). The zirconocene catalysts proved to be much more reactive than the titanium system, and so the former was examined exclusively. The silanes, aromatic or aliphatic, are bonded to the silicon surface through direct Si-Si bonds, although the level of incorporation of the trihydroarylsilanes was substantially higher than that of the aliphatic silanes. The reaction proceeds on nanocrystalline hydride-terminated porous silicon surfaces, as well as flat Si(100)-H(x) and Si(111)-H interfaces. The reactions were studied by a variety of techniques, including FTIR, SIMS, and XPS.

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 categoriesInsufficient payload (model declined to judge)
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 score0.002
Threshold uncertainty score1.000

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)0.0010.000

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Citations44
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueInorganic ChemistrySame topicSilicon Nanostructures and PhotoluminescenceFrench-language works237,207