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Record W6963083277 · doi:10.18419/darus-2727

Replication data for: "In Situ Monitoring of Mechanochemical Covalent Organic Framework Formation Reveals Templating Effect of Liquid Additive"

2022· dataset· en· W6963083277 on OpenAlexaff

Bibliographic record

VenueUniversitätsbibliothek Stuttgart · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsMcGill University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEuropean Commission
KeywordsMechanochemistryCovalent organic frameworkRaman spectroscopyPorosityCrystallinityCovalent bondMetal-organic frameworkInfrared spectroscopySorption

Abstract

fetched live from OpenAlex

Covalent organic frameworks (COFs) have emerged as a new class of molecularly precise, porous functional materials characterized by broad structural and chemical versatility, with a diverse range of applications. Despite their increasing popularity, fundamental aspects of COF formation are poorly understood, lacking profound experimental insights into their assembly. Here, we use a combination of in situ X-ray powder diffraction and Raman spectroscopy to elucidate the reaction mechanism of mechanochemical synthesis of imine COFs, leading to the observation of key reaction intermediates that offer direct experimental evidence of framework templating through liquid additives. Moreover, the solid-state catalyst scandium triflate is instrumental in directing the reaction kinetics and mechanism, yielding COFs with crystallinity and porosity on par with solvothermal products. This work provides the first experimental evidence of solvent-based COF templating, and is a significant advance in mechanistic understanding of mechanochemistry as a green route for COF synthesis. This dataset contains all data from analytical measurements including FT-IR spectra, raw XRD patterns, 1H and 13C ssNMR and lNMR spectra, N2 sorption isotherms, pore-size distributions, BET plots, calculated NMR chemical shifts, CIFs of the journal article mentioned under the related publication. Open *.cif and *.xyz files with a visualization software (see http://ww1.iucr.org/iucr-top/cif/), *.raw files with WinXPOW, *.sp files with PerkinElmer Spectrum software, *.mnova files with MestReNova, *.fq/.gr/iq/sq files with a text editor, .qps with ASiQwin, and *.pro files with TOPAS.

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)0.004
metaresearch head score (Gemma)0.004
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.006
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.320
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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