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Record W2343393866 · doi:10.1021/acs.langmuir.5b02307

Radical Initiated Hydrosilylation on Silicon Nanocrystal Surfaces: An Evaluation of Functional Group Tolerance and Mechanistic Study

2015· article· en· W2343393866 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLangmuir · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationGovernment of Alberta
KeywordsHydrosilylationAlkeneMonolayerPassivationChemistryPhotochemistrySiliconSurface modificationFunctional groupNanocrystalPolymer chemistryCatalysisMaterials scienceOrganic chemistryNanotechnologyPhysical chemistryLayer (electronics)Polymer

Abstract

fetched live from OpenAlex

Hydrosilylation is among the most common methods used for modifying silicon surface chemistry. It provides a wide range of surface functionalities and effective passivation of surface sites. Herein, we report a systematic study of radical initiated hydrosilylation of silicon nanocrystal (SiNC) surfaces using two common radical initiators (i.e., 2,2'-azobis(2-methylpropionitrile) and benzoyl peroxide). Compared to other widely applied hydrosilylation methods (e.g., thermal, photochemical, and catalytic), the radical initiator based approach is particle size independent, requires comparatively low reaction temperatures, and yields monolayer surface passivation after short reaction times. The effects of differing functional groups (i.e., alkene, alkyne, carboxylic acid, and ester) on the radical initiated hydrosilylation are also explored. The results indicate functionalization occurs and results in the formation of monolayer passivated surfaces.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.095
GPT teacher head0.306
Teacher spread0.211 · 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