MétaCan
Menu
Back to cohort
Record W2791559114 · doi:10.1002/star.201700271

Influence of Chain Structures of Starch on Water Absorption and Copper Binding of Starch‐Graft‐Itaconic Acid Hydrogels

2018· article· en· W2791559114 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueStarch - Stärke · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAmylopectinAmyloseStarchSelf-healing hydrogelsSwellingChemistryWaxy cornPolymer chemistryMaize starchCopolymerItaconic acidAcrylamideSodium bisulfiteChemical engineeringOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Bio‐based hydrogels destined for use as low cost renewable biosorbents are synthesized. The impact of the two components of starch, amylose, and amylopectin, on the properties of the hydrogels is assessed. Hydrogels are synthesized by solution based graft copolymerization of itaconic acid monomers on a corn starch backbone in the presence of an acrylamide crosslinker and the potassium persulfate/sodium bisulfite redox initiator pair. Three types of corn starch are used: normal corn starch, waxy starch, and high amylose starch. The amylose:amylopectin ratios are: 27:73 for normal starch (NS), 70:30 for high amylose starch (HAS), and 0:100 for waxy starch (WS). Swelling tests performed in a variety of solutions show no significant differences in swelling capacity among the hydrogels. The hydrogels also perform similarly in copper adsorption tests. Overall, these results indicate that native corn starch is as effective as amylopectin and amylose‐rich starch.

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.

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 score0.002
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.264
Teacher spread0.249 · 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