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Record W4400387953 · doi:10.1002/anie.202404539

Mechanochemical Synthesis of Boroxine‐linked Covalent Organic Frameworks

2024· article· en· W4400387953 on OpenAlexafffund
Ehsan Hamzehpoor, Farshid Effaty, Tristan H. Borchers, Robin S. Stein, Alexander Wahrhaftig‐Lewis, Xavier Ottenwaelder, Tomislav Friščić, Dmitrii F. Perepichka

Bibliographic record

VenueAngewandte Chemie International Edition · 2024
Typearticle
Languageen
FieldMaterials Science
TopicCovalent Organic Framework Applications
Canadian institutionsConcordia UniversityMcGill University
FundersFonds de recherche du Québec – Nature et technologiesLeverhulme TrustNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMechanochemistryMechanosynthesisCovalent bondRaman spectroscopyCrystallinityPorosityChemical engineeringChemistryMaterials scienceSolventNanotechnologyOrganic chemistryBall millCrystallography

Abstract

fetched live from OpenAlex

Abstract We report a rapid, room‐temperature mechanochemical synthesis of 2‐ and 3‐dimensional boroxine covalent organic frameworks (COFs), enabled by using trimethylboroxine as a dehydrating additive to overcome the hydrolytic sensitivity of boroxine‐based COFs. The resulting COFs display high porosity and crystallinity, with COF‐102 being the first example of a mechanochemically prepared 3D COF, exhibiting a surface area of ca . 2,500 m 2 g −1 . Mechanochemistry enabled a>20‐fold reduction in solvent use and ~100‐fold reduction in reaction time compared with solvothermal methods, providing target COFs quantitatively with no additional work‐up besides vacuum drying. Real‐time Raman spectroscopy permitted the first quantitative kinetic analysis of COF mechanosynthesis, while transferring the reaction design to Resonant Acoustic Mixing (RAM) enabled synthesis of multi‐gram amounts of the target COFs (tested up to 10 g).

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 score0.003

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 head0.011
GPT teacher head0.265
Teacher spread0.253 · 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

Citations46
Published2024
Admission routes2
Has abstractyes

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