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Record W2067374780 · doi:10.1002/ejic.201100262

Group 11 Amidinates and Guanidinates: Potential Precursors for Vapour Deposition

2011· article· en· W2067374780 on OpenAlexaff
Todd J. J. Whitehorne, Jason P. Coyle, Ahsan Mahmood, W.H. Monillas, Glenn P. A. Yap, Seán T. Barry

Bibliographic record

VenueEuropean Journal of Inorganic Chemistry · 2011
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsCarleton UniversityUniversité de Montréal
Fundersnot available
KeywordsChemistrySublimation (psychology)CopperMetalCarbodiimideLigand (biochemistry)HydrogenInorganic chemistryPhotochemistryPolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Several guanidinates of copper and silver, as well as amidinates and guanidinates of gold were synthesized as potential precursors for vapour deposition methods. These compounds were found to be dimers in the case of copper and gold, and trimers in the case of silver. The copper compounds showed good thermal and photostability, and were isolable by sublimation. The silver compounds proved to be very reactive to both heat and light, and were found to deposit silver metal when heated, suggesting that these sensitive compounds might be used as single source precursors. The gold compounds were found to exhibit some heat and light sensitivity, but were much more stable than their silver counterparts. Specifically, [Au(N i Pr) 2 NMe 2 ] 2 ( 8 ) was found to be sublimable at 85 °C and 20 mTorr, and deposited gold metal under higher temperatures. These metal‐depositing thermal reactions were thought to abstract a hydrogen from the guanidinate ligand, which acts as the reducing agent. Interestingly, the gold amidinate compounds were found to produce diisopropylcarbodiimide when heated, suggesting that these compounds deinsert carbodiimide rather than abstract a hydrogen atom from the ligand.

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 score0.009
Threshold uncertainty score0.992

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)0.0080.000

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 head0.014
GPT teacher head0.188
Teacher spread0.174 · 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

Citations54
Published2011
Admission routes1
Has abstractyes

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