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Record W4399629458 · doi:10.1021/acs.organomet.4c00084

Put a Ring on It: Improving the Thermal Stability of Molybdenum Imides through Ligand Rigidification

2024· article· en· W4399629458 on OpenAlexafffund
Michael A. Land, Kieran G. Lawford, Lara K. Watanabe, M. J. ATHERTON, Seán T. Barry

Bibliographic record

VenueOrganometallics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryMolybdenumRing (chemistry)Ligand (biochemistry)Thermal stabilityThermalChemical engineeringCombinatorial chemistryStereochemistryOrganic chemistryReceptorThermodynamicsBiochemistry

Abstract

fetched live from OpenAlex

Volatile bis( tert -butylimido)-dichloromolybdenum(VI) compounds containing N,N’ -chelating ligands, ( t BuN) 2 MoCl 2 ·L, have previously been used as single-source precursors for the chemical vapor deposition of high-purity Mo 2 N thin films. The first step in the thermolysis of these compounds is the partial dissociation of the chelating ligand to yield ( t BuN) 2 MoCl 2, which further decomposes by eliminating isobutylene and t BuNH 2 . The rate-determining step in this process is the formation of a pentacoordinate intermediate where the previously bidentate ligand adopts a κ 1 -coordination. Here we show that rigidification of the ligand backbone, by incorporating various heterocycles, led to an overall increase in thermal stability (21–38 °C) of these complexes by preventing the formation of the κ 1 -intermediate. Formation of the κ 1 -intermediates is highlighted by high level calculations and is supported by experimental activation barriers. Finally, a model for the κ 1 -bipyridine adduct was isolated and characterized using 2-phenylpyridine. This careful control of the thermal stabilities of these compounds can lead to new vapor-phase deposition precursors for the preparation of Mo 2 N films.

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 score0.007
Threshold uncertainty score0.341

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 head0.227
Teacher spread0.209 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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