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Record W2577495981 · doi:10.3390/molecules22010144

Advances in Solid-State Transformations of Coordination Bonds: From the Ball Mill to the Aging Chamber

2017· review· en· W2577495981 on OpenAlexafffund
Cristina Mottillo, Tomislav Friščić

Bibliographic record

VenueMolecules · 2017
Typereview
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCoordination polymerMicroporous materialNanotechnologyMetal-organic frameworkCoordination complexCommercializationSolid-stateMechanochemistryPolymerChemistryMoleculeSolid-state chemistryMaterials scienceMetalOrganic chemistryAdsorptionPhysical chemistry

Abstract

fetched live from OpenAlex

Controlling the formation of coordination bonds is pivotal to the development of a plethora of functional metal-organic materials, ranging from coordination polymers, metal-organic frameworks (MOFs) to metallodrugs. The interest in and commercialization of such materials has created a need for more efficient, environmentally-friendly routes for making coordination bonds. Solid-state coordination chemistry is a versatile greener alternative to conventional synthesis, offering quantitative yields, enhanced stoichiometric and topological selectivity, access to a wider range of precursors, as well as to molecules and materials not readily accessible in solution or solvothermally. With a focus on mechanochemical, thermochemical and "accelerated aging" approaches to coordination polymers, including pharmaceutically-relevant materials and microporous MOFs, this review highlights the recent advances in solid-state coordination chemistry and techniques for understanding the underlying reaction mechanisms.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.034
GPT teacher head0.328
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations158
Published2017
Admission routes2
Has abstractyes

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