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Record W2944766552 · doi:10.1002/app.47941

Preparation and characterization of amphoteric cellulose–montmorillonite composite beads with a controllable porous structure

2019· article· en· W2944766552 on OpenAlexaff
Hualei Xie, Yuanfeng Pan, Huining Xiao, Haiyan Liu

Bibliographic record

VenueJournal of Applied Polymer Science · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New Brunswick
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceCelluloseMontmorilloniteThermogravimetric analysisChemical engineeringMesoporous materialComposite numberAdsorptionPorosityPolymerComposite materialChemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

ABSTRACT Conventional amphoteric and porous materials are often synthetic and polymer based; this tends to raise environmental concerns because of their poor biodegradability. To address this issue, novel natural‐polymer‐ or amphoteric‐modified cellulose and MOt (ACeOMt) composite beads with a typical mesoporous structure were developed in this study. These green‐based porous beads, consisting of regenerated bagasse cellulose and oxalic acid modified montmorillonite (OMt), were successfully prepared by a facile coagulation method with fine calcium carbonate as a pore‐forming agent. The beads with the best sphericity were obtained at a 1:1 weight ratio of cellulose to OMt. Scanning electron microscopy observation showed that ACeOMt possessed a smooth surface with abundant macropores. X‐ray diffraction and thermogravimetric analysis characterizations demonstrated the success of the modification of montmorillonite and cellulose. The results of Brunauer–Emmett–Teller analysis indicate the presence of a typical mesoporous structure in the composite with a relatively high specific surface area. The resulting ACeOMt are expected to be biodegradable, nonhazardous, and applicable for various uses, including adsorption, chromatography, and soil remediation. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136 , 47941.

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 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 score0.003
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.240
Teacher spread0.235 · 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.

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

Citations13
Published2019
Admission routes1
Has abstractyes

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