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Record W4313308197 · doi:10.1002/chem.202203557

Carbonylmetallates as Versatile 2‐, 4‐ or 6‐Electron Donor Metalloligands in Transition‐Metal Complexes and Clusters: A Global Approach

2022· article· en· W4313308197 on OpenAlexfundno aff
Noura Naili, Samia Kahlal, Bachir Zouchoune, Jean‐Yves Saillard, Pierre Braunstein

Bibliographic record

VenueChemistry - A European Journal · 2022
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueMinistère de l'Enseignement Supérieur, de la Recherche, de la Science et de la Technologie
KeywordsElectron countingMetalTransition metalChemistryBridging (networking)Computational chemistryElectronCrystallographyChemical physicsPhysicsComputer scienceQuantum mechanicsCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Carbonylmetallates [m] − , such as [MoCp(CO) 3 ] − , [Mn(CO) 5 ] − , [Co(CO) 4 ] − , have long been successfully used in the preparation of hundreds of metal‐metal bonded carbonyl complexes and clusters, in particular of the heterometallic type. We focus here on situations where [m] − can be viewed as a terminal, doubly or even triply bridging metalloligand, developing metal‐metal interactions with one, two or three metal centers M, respectively. With metals M from the Groups 10–12, it is not straightforward or even impossible to rationalize the structure of the resulting clusters by applying the well‐known Wade‐Mingos rules. A very simple but global approach is presented to rationalize structures not obeying usual electron‐counting rules by considering the anionic building blocks [m] − as metalloligands behaving formally as potential 2‐, 4‐ or 6‐electron donors, similarly to what is typically encountered with for example halido ligands. Qualitative and theoretical arguments by using DFT calculations highlight similarities between seemingly unrelated metal complexes and clusters and also entail a predicting power with high synthetic potential.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0220.000

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

Explore more

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