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Record W4251296357 · doi:10.26434/chemrxiv.11794404

Thermally-Induced Dehydrogenative Coupling of Organosilanes and H-Terminated Silicon Quantum Dots onto Germanane Surfaces

2020· preprint· en· W4251296357 on OpenAlexaff
Haoyang Yu, Alyxandra Thiessen, Md Asjad Hossain, Marc Julian Kloberg, Bernhard Rieger, Jonathan G. C. Veinot

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCovalent bondSurface modificationHeteronuclear moleculeMaterials scienceMonolayerSiliconNanotechnologyNanosheetQuantum dotExfoliation jointFabricationGrapheneChemistryMoleculeOptoelectronicsOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Covalently bonded organic monolayers play important roles in defining the solution processability, ambient stability, and electronic properties of two-dimensional (2D) materials such as Ge nanosheets (GeNSs); they also hold promise of providing avenues for the fabrication of future generation electronic and optical devices. Functionalization of GeNS normally involves surface moieties linked through covalent Ge−C bonds. In the present contribution we extend the scope of surface linkages to include Si−Ge bonding and present the first demonstration of heteronuclear dehydrocoupling of organosilanes to hydride-terminated GeNSs obtained from the deintercalation and exfoliation of CaGe2. We further exploit this new surface reactivity and demonstrated the preparation of directly bonded silicon quantum dot-Ge nanosheet hybrids.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.044
GPT teacher head0.302
Teacher spread0.258 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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