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Record W2528393130

Donor‐Acceptor Stabilization in Main Group Elements: A Computational Study

2015· article· en· W2528393130 on OpenAlexaff
Evan R. Antoniuk, Eric Rivard, Alex Brown

Bibliographic record

VenueURSCA Proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGermaniumReagentCarbon groupHydrideAcceptorLithium (medication)Combinatorial chemistryChemistryElectron acceptorNanocrystalThermal decompositionSiliconLewis acids and basesMaterials scienceDecompositionNanotechnologyPhotochemistryGroup (periodic table)MetalOrganic chemistryCatalysisPhysics
DOInot available

Abstract

fetched live from OpenAlex

Recently, much synthetic effort has been placed in attempting to prepare low oxidation state main group hydrides. Primarily, this has been accomplished by using a combination of N-heterocyclic carbenes (electron donors) and Lewis acids (electron acceptors) to stabilize the reactive hydride species. The motivation for carrying out such work has been due to the role of SiH2 as an intermediate species in forming semi-conducting silicon surfaces for use in the electronics industry. However, this approach has also been effective in producing stable hydrides of Ge and Sn. Recently, germanium nanocrystals (GeNCs) with tunable size were synthesized by thermolysis of a Wittig reagent stabilized GeH2 species. These germanium nanocrystals serve to be potentially useful in solar cells, lithium-ion batteries, and biological imaging applications.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0230.001

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 head0.023
GPT teacher head0.243
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations0
Published2015
Admission routes1
Has abstractyes

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