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Record W4236500163 · doi:10.26434/chemrxiv.8038973.v2

Controlling Thermal Stability and Volatility of Organogold(I) Compounds for Vapor Deposition with Complementary Ligand Design

2019· preprint· en· W4236500163 on OpenAlexaff
Matthew B. E. Griffiths, Zachary S. Dubrawski, Goran Bačić, Jason D. Masuda, Achini Japahuge, Tao Zeng, Seán T. Barry

Bibliographic record

VenueChemRxiv · 2019
Typepreprint
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsCarleton University
Fundersnot available
KeywordsVolatility (finance)Thermogravimetric analysisThermal stabilityChemical vapor depositionDensity functional theoryChemistryGroup 2 organometallic chemistryStability (learning theory)ThermalMaterials scienceComputational chemistryPhysical chemistryThermodynamicsOrganic chemistryMathematicsMoleculeComputer sciencePhysicsEconometrics

Abstract

fetched live from OpenAlex

We compare and contrast the volatility and thermal stability of a family of twelve organometallic gold(I) compounds using a combination of X-ray crystallography, thermogravimetric analysis (TGA), and density functional theory (DFT) techniques. Pentafluorophenyl is used as a new ligand for vapor deposition which produces rather low volatility, but very thermally stable compounds when combined with PMe 3 and N , N '-di- tert -butylimidazolidin-2-ylidene. We introduce a precursor figure of merit that can be used to rank precursor usefulness. Using DFT, we find a linear correlation between Au-L bond strength and thermal stability, which demonstrates the power of computational techniques to predict successful synthetic targets for future precursor design studies.

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 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 score0.227
Threshold uncertainty score0.826

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)0.0000.000

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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.

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

Citations6
Published2019
Admission routes1
Has abstractyes

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