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Record W2536254145 · doi:10.1021/acs.chemmater.6b02667

From Hydrogen Silsesquioxane to Functionalized Silicon Nanocrystals

2016· article· en· W2536254145 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

VenueChemistry of Materials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsConcordia University of EdmontonUniversity of Alberta
FundersUniversity of AlbertaNatural Sciences and Engineering Research Council of CanadaConcordia University of Edmonton
KeywordsSilsesquioxaneDispersitySiliconMaterials scienceNanotechnologyHydrogen silsesquioxanePhotovoltaicsNanocrystalNanoparticleEtching (microfabrication)Surface modificationCharacterization (materials science)Chemical engineeringOptoelectronicsPolymerPolymer chemistryPhotovoltaic system

Abstract

fetched live from OpenAlex

Silicon nanocrystals exhibit size-dependent optical and electronic properties that may be exploited for applications ranging from sensors to photovoltaics. In addition, they can be utilized in biological and environmental systems thanks to the nontoxicity of silicon. Synthesis of silicon nanocrystals has been accomplished using a variety of methods. However, creating near monodisperse systems of high purity has been a challenge. The high temperature processing of hydrogen silsesquioxane method of particle synthesis reproducibly provides pure, near monodisperse particles in scalable quantities. These particles can then be liberated using HF etching and functionalized using a variety of methods. This paper outlines our lab procedures for creating silicon nanocrystals, the various functionalization methods and the most commonly used characterization techniques.

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 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 score0.046
Threshold uncertainty score0.954

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)0.0470.000

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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