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Record W2737391574 · doi:10.1002/admi.201700560

Engineering Elastic ZIF‐8‐Sponges for Oil–Water Separation

2017· article· en· W2737391574 on OpenAlexafffund
He Zhu, Qi Zhang, Bo‐Geng Li, Shiping Zhu

Bibliographic record

VenueAdvanced Materials Interfaces · 2017
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsMaterials scienceSpongeMelamineComposite numberComposite materialPolymerLayer (electronics)Chemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Abstract This work reports a rapid and straightforward method to fabricate ZIF‐8‐melamine sponge composite with good mechanical properties, which overcomes the outstanding challenge of fabricating mechanically stable metal–organic framework (MOF)‐based 3D structures. A continuous ZIF‐8 thin layer is grown onto sponge skeleton by simple immersion of pristine sponge in ZIF‐8 precursor solution. This method is rapid, cost‐effective, easily scaled‐up, and it does not require any premodification. The obtained ZIF‐8 sponge shows an excellent compressive behavior and ZIF‐8 thin layer remained intact after over ten cycles of compression tests. In addition, the MOF/polymer shows an outstanding absorption performance of organic solvents, good recoverability, and good recyclability, demonstrating its potential in cleaning up oil spills.

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 score0.001
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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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

Citations63
Published2017
Admission routes2
Has abstractyes

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