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Record W4412654256 · doi:10.1002/adma.202418707

Mechanochemistry for Metal–Organic Frameworks and Covalent–Organic Frameworks (MOFs, COFs): Methods, Materials, and Mechanisms

2025· review· en· W4412654256 on OpenAlexafffund
Joseph M. Marrett, Farshid Effaty, Xavier Ottenwaelder, Tomislav Friščić

Bibliographic record

VenueAdvanced Materials · 2025
Typereview
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesLeverhulme Trust
KeywordsMechanochemistryMetal-organic frameworkMaterials scienceNanotechnologyCovalent bondOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

The mechanochemistry of metal-organic frameworks (MOFs) is a well-established field whose development has advanced the understanding and the design of both MOF materials and mechanochemical reactions. This review outlines the close and mutually beneficial interplay of these two fields over the past two decades, including the description of mechanochemical strategies to access MOFs as well as the response of these materials to mechanical treatment and/or stress. Furthermore, we highlight how the use of MOFs as model targets for mechanochemical synthesis simultaneously improves the accessibility and understanding of this class of materials and, conversely, advances the experimental and fundamental understanding of mechanochemical reactions. Similarly, we show the reciprocal benefits of comparing the mechanochemistry of organic molecular solids to that of MOFs. Finally, this review also portrays the rapid emergence of mechanochemistry of covalent-organic frameworks, a young area that promises to deliver new, rapid, efficient, solventless, and room-temperature access to these materials.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0060.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.020
GPT teacher head0.330
Teacher spread0.310 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations61
Published2025
Admission routes2
Has abstractyes

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