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Record W4286696954 · doi:10.1002/app.52849

Design and optimization of superabsorbent hydrogels based on acrylic acid/<scp>2‐acrylamido‐2‐methylpropane</scp> sulfonic acid copolymers

2022· article· en· W4286696954 on OpenAlexaff
Zohreh Jomeh Farsangi, Xuedong Song, Kaiyuan Yang, Todd Hoare

Bibliographic record

VenueJournal of Applied Polymer Science · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsKimberly-Clark (Canada)McMaster University
FundersKimberly-Clark
KeywordsAcrylic acidSulfonic acidSelf-healing hydrogelsCopolymerSuperabsorbent polymerMaterials scienceChemistryPolymer chemistryPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Although acrylic acid‐based superabsorbent hydrogels (SAHs) are widely used in hygiene and personal care applications, the low degrees of true ionization in such SAHs under practical application places a limit on the degree of superabsorbency that can be achieved. Herein, the preparation and optimization of SAHs based on copolymers of acrylic acid (AA) with 2‐acrylamido‐2‐methylpropane sulfonic acid (AMPS), a strong acid comonomer that remains ionized at all relevant physiological pH values, is described. AA‐AMPS recipes optimized using a central composite design response surface method demonstrate significantly enhanced absorbance under load (AUL) values (&gt;15–20% increase) and comparable centrifuge retention capacity (CRC) values compared to the corresponding AA‐only controls while maintaining strong mechanics even in the swollen state (as much as twice the modulus of the commercial AA‐based superabsorbent). The simplicity of this recipe, coupled with the reproducible achievement of the improved superabsorbency properties using a small adaptation of the commercial SAH synthesis protocol, makes AA‐AMPS superabsorbent hydrogels potential candidates for use in commercial hygiene products.

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)0.001
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 score0.044
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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