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Record W4231060808 · doi:10.26434/chemrxiv.11577561.v1

Simple, Scalable Mechanosynthesis of Metal-Organic Frameworks Using Liquid-Assisted Resonant Acoustic Mixing (LA-RAM)

2020· preprint· en· W4231060808 on OpenAlexafffund
Hatem M. Titi, Jean‐Louis Do, Ashlee J. Howarth, Karthik Nagapudi, Tomislav Friščić

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsConcordia UniversityMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanosynthesisMetal-organic frameworkMixing (physics)ImidazolateScalabilityMechanochemistryMaterials scienceLigand (biochemistry)MetalZeolitic imidazolate frameworkNanotechnologyChemistryInorganic chemistryPhysical chemistryComputer scienceMetallurgyAdsorptionPhysics

Abstract

fetched live from OpenAlex

We present a methodology for the rapid and readily scalable mechanosynthesis of diverse metal-organic frameworks (MOFs) in the absence of milling media, typically required for other types of mechanochemical syntheses. We demonstrate the use of liquid-assisted resonant acoustic mixing (LA-RAM) methodology for the synthesis of two- and three-dimensional MOFs based on Zn, Co(II) and Cu(II), including single- and mixed-ligand systems, imidazolate or carboxylate ligands. The LA-RAM approach also allowed the synthesis of the ZIF-L, a framework never previously obtained in a solventless environment, as well as its Co(II) analogue. Straightforward scale-up from milligrams to at least 25 grams is demonstrated using ZIF-L as the model.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
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 score0.159
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.034
GPT teacher head0.270
Teacher spread0.236 · 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
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

Citations4
Published2020
Admission routes2
Has abstractyes

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