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Record W4384827218 · doi:10.32920/23709633.v1

The Role of pH, Ionic Strength and Monomer Concentration on the Terpolymerization of 2-Acrylamido-2-methylpropane Sulfonic Acid, Acrylamide and Acrylic Acid

2023· preprint· en· W4384827218 on OpenAlexafffund
Alison J. Scott, Thomas A. Duever, Alexander Penlidis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSulfonic acidReactivity (psychology)Ionic strengthMonomerAcrylic acidAcrylamideChemistryPolymer chemistryCopolymerLabilityIonic bondingPolymerOrganic chemistryAqueous solutionIon

Abstract

fetched live from OpenAlex

<p>The current study examines the effects of important factors (namely, pH, ionic strength and monomer concentration) on the terpolymerization of 2-acrylamido-2-methylpropane sulfonic acid (AMPS), acrylamide (AAm) and acrylic acid (AAc). A good understanding of how these factor levels affect terpolymerization reactivity ratios, and terpolymer composition, microstructure and molecular weight paves the way for the synthesis of custom-made polymers for specific applications. For the range of conditions studied, ionic strength has the greatest influence on reactivity ratios; results indicate that cross-over behavior exists for AMPS-based reactivity ratios. No clear correlation is observed between pH and reactivity ratio estimates (for 5 ≤ pH ≤ 9), but parameter estimation results suggest that the incorporation of acidic comonomers (AMPS and AAc) is affected by pH within this range. Finally, monomer concentration has a dominant impact on molecular weight averages, even when other factors are varied.</p>

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 categoriesMeta-epidemiology (narrow)
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 score0.068
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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