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Record W2995708044 · doi:10.1002/star.201900008

Eco‐Friendly Synthesis of Hydrogels from Starch, Citric Acid, and Itaconic Acid: Swelling Capacity and Metal Chelation Properties

2019· article· en· W2995708044 on OpenAlexafffund
Daniel Duquette, Christopher Nzediegwu, Guillermo Portillo‐Perez, Marie‐Josée Dumont, Shiv O. Prasher

Bibliographic record

VenueStarch - Stärke · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCitric acidItaconic acidSwellingSelf-healing hydrogelsPotassium persulfateSwelling capacityNuclear chemistryChelationChemistryStarchPolymer chemistryThermogravimetric analysisAdsorptionMaterials scienceInorganic chemistryOrganic chemistryCopolymerPolymerizationPolymerComposite material

Abstract

fetched live from OpenAlex

Abstract In this study, eco‐friendly superabsorbent hydrogels are prepared by the grafting and crosslinking reactions of itaconic acid and citric acid with starch, using potassium persulfate as the free radical initiator. The structure and morphology of the hydrogels are studied using Fourier transform infrared spectroscopy, scanning electron microscopy, and thermogravimetric analysis. The effect of the citric acid to starch ratio on the swelling properties is evaluated, and it is found that equal parts of citric acid and starch produced the highest swelling ratio of 188 g distilled water per g hydrogel. The hydrogels also exhibit pH‐dependent behavior, such that the maximum swelling ratio of 232 g/g is obtained at pH 8. Finally, the metal chelation properties of the hydrogel are tested in solutions of copper, nickel, and zinc, and the adsorption capacity followed the trend Cu > Ni > Zn based on the Freundlich parameter KF.

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 score0.000
Threshold uncertainty score0.002

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 head0.020
GPT teacher head0.225
Teacher spread0.206 · 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

Citations36
Published2019
Admission routes2
Has abstractyes

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