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Record W2784848174 · doi:10.1039/c7sc05312f

Enthalpy <i>vs.</i> friction: heat flow modelling of unexpected temperature profiles in mechanochemistry of metal–organic frameworks

2018· article· en· W2784848174 on OpenAlexafffund
Krunoslav Užarević, Nenad Ferdelji, Tomislav Mrla, Patrick Julien, Boris Halasz, Tomislav Friščić, Iván Halász

Bibliographic record

VenueChemical Science · 2018
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsMcGill University
FundersEuropean Synchrotron Radiation FacilityFond za Zaštitu Okoliša i Energetsku UčinkovitostHrvatska Zaklada za ZnanostMinistry of EnvironmentNatural Sciences and Engineering Research Council of CanadaZaklade Adris
KeywordsMechanochemistryEnthalpyMetal-organic frameworkHeat flowFlow (mathematics)ThermodynamicsMaterials scienceMetalMetallurgyChemistryThermalPhysical chemistryMechanicsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

reaction monitoring by synchrotron X-ray diffraction and numerical simulations of heat flow, we have shown that the temperature profiles of mechanochemical reactions are dominantly determined by the energy dissipated through friction between the sample and the moving milling assembly, while the reaction enthalpy will usually be comparatively insignificant. With the changes in composition during mechanochemical reactions, frictional properties of the milled material change, leading to either better or worse energy absorption upon collisions in the process of milling. This approach explains unexpected and rapid temperature drops during exothermic transformations of ZIF-8 polymorphs. Since reaction kinetics are highly sensitive to changes in temperature, precise temperature profiles provided here will be mandatory to understand kinetics and its changes during milling, and will aid in developing the comprehensive model of mechanochemical reactivity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.232
Teacher spread0.219 · 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 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

Citations93
Published2018
Admission routes2
Has abstractyes

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