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Record W2983902613 · doi:10.1021/acs.cgd.9b01477

Real-Time in Situ Monitoring of Particle and Structure Evolution in the Mechanochemical Synthesis of UiO-66 Metal–Organic Frameworks

2019· article· en· W2983902613 on OpenAlexafffund
Luzia S. Germann, Athanassios D. Katsenis, Igor Huskić, Patrick Julien, Krunoslav Užarević, Martin Etter, Omar K. Farha, Tomislav Friščić, Robert E. Dinnebier

Bibliographic record

VenueCrystal Growth & Design · 2019
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsMcGill University
FundersHrvatska Zaklada za ZnanostDefense Threat Reduction AgencySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaEuropean Cooperation in Science and Technology
KeywordsMechanochemistrySynchrotronCrystalliteMetal-organic frameworkDiffractionIn situParticle (ecology)Materials scienceNanotechnologyKineticsPowder diffractionParticle sizeX-ray crystallographyChemical engineeringMetalChemistryCrystallographyMetallurgyPhysical chemistryOrganic chemistryPhysicsEngineeringOptics

Abstract

fetched live from OpenAlex

Abstract While in situ synchrotron X-ray powder diffraction has revolutionized mechanistic studies of mechanochemistry and inspired the development of other real-time monitoring approaches, the full potential of such data remains unexploited, with most applications addressing only reaction kinetics. Focusing on UiO-type metal?organic frameworks (MOFs) as models, we now show how in-depth analysis of real-time diffraction data can provide insight into the evolution of both crystallographic structure and crystallite size, including the direct relation to the particle size of mechanochemically formed MOFs.

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 score0.002
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations68
Published2019
Admission routes2
Has abstractyes

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