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Record W2317457062 · doi:10.1021/cg300297p

Rational Synthesis of Metal–Organic Framework Nanocubes and Nanosheets Using Selective Modulators and Their Morphology-Dependent Gas-Sorption Properties

2012· article· en· W2317457062 on OpenAlexafffund
Minh-Hao Pham, Gia‐Thanh Vuong, Frédéric‐Georges Fontaine, Trong‐On Do

Bibliographic record

VenueCrystal Growth & Design · 2012
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversité LavalCentre in Green Chemistry and Catalysis
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDABCOCrystallinitySorptionOctaneKirkendall effectMetal-organic frameworkAcetic acidNanorodPyridineChemical engineeringMaterials scienceMetalChemistryNanocrystalInorganic chemistryNanotechnologyOrganic chemistryCrystallographyAdsorption

Abstract

fetched live from OpenAlex

Nanocubes and nanosheets of [Cu 2 (ndc) 2 (dabco)] n metal–organic framework (ndc = 1,4-naphthalene dicarboxylate; dabco = 1,4-diazabicyclo[2.2.2]octane) were synthesized by using simultaneously acetic acid and pyridine or only pyridine, respectively, as selective modulators. This approach can tailor crystal growing on different directions for the size- and shape-controlled synthesis of metal–organic framework (MOF) nanocrystals whose structure is composed of two or more types of linkers using selective modulators. These MOF nanocrystals exhibit high crystallinity and higher CO 2 uptakes compared to that of the bulk MOF material or of the [Cu 2 (ndc) 2 (dabco)] n nanorods generated by using only acetic acid as the selective modulator, which may be due to the morphology effect on their gas sorption properties.

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 score0.005

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)0.0000.000

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 head0.027
GPT teacher head0.224
Teacher spread0.197 · 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

Citations157
Published2012
Admission routes2
Has abstractyes

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