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Record W4405075274 · doi:10.1021/acs.chemmater.4c02689

Insights into the Thermal Decomposition Mechanism of Molybdenum(VI) Iminopyridine Adducts

2024· article· en· W4405075274 on OpenAlexaff
Lara K. Watanabe, Aaron D. Rogers, M. J. ATHERTON, Kieran G. Lawford, Bonwook Gu, Supill Chun, Sanghyun Lee, Dong-Kyun Lee, Han‐Bo‐Ram Lee, Seán T. Barry

Bibliographic record

VenueChemistry of Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsCarleton University
FundersSK Hynix
KeywordsMolybdenumAdductThermal decompositionMechanism (biology)ThermalChemistryDecompositionPhotochemistryMaterials scienceChemical engineeringInorganic chemistryOrganic chemistryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Understanding the thermal behavior of vapor phase precursors is important to assess their potential for thin film deposition processes and to discern decomposition pathways. Here we present a series of dichloromolybdenum(VI) compounds on three different frameworks: the bis(alkylimido)-, dioxo- and mono(alkylimido)mono(oxo), which is the first foray into understanding the potential differences of decomposition mechanisms for molecules using these strongly bonded ligands. We report the synthesis of three novel metal complexes which have been fully characterized by spectroscopic techniques as well as single-crystal X-ray diffraction (SCXRD) and thermal analysis. Through in situ thermolysis reactions, we show the decomposition of all compounds generates the free N, N ′-chelate ligand. Further thermal decomposition deviated from previously reported γ-H elimination, showing that the presence of β-hydrogens in the complexes provided alternative low-temperature decomposition pathways.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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