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Record W2328660985 · doi:10.1021/la500474j

Comprehensive Study of the Chelation and Coacervation of Alkaline Earth Metals in the Presence of Sodium Polyphosphate Solution

2014· article· en· W2328660985 on OpenAlexafffund
Arash Momeni, Mark Filiaggi

Bibliographic record

VenueLangmuir · 2014
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyphosphateCoacervateChemistryDivalentInorganic chemistryChelationIonic radiusAlkaline earth metalPhosphateAlkali metalChromatographyOrganic chemistryIon

Abstract

fetched live from OpenAlex

The effect of chelation of three alkaline earth metals (Ca, Sr, and Ba) by polyphosphates on the pH and viscosity of the solution is examined and correlated to the phosphate glass properties. Also, the impact of the polyphosphate average degree of polymerization (D(p)) as well as the type and amount of chelated divalent cation on the degradation rate of the chains is studied. Subsequently, the number of divalent cations required for polyphosphate chain agglomeration to form a coacervate, and the resulting composition of these coacervates, was investigated. A decrease in polyphosphate solution pH during chelation was routinely obtained, with a sudden shift in the rate of pH drop occurring around a divalent cation/phosphorus molar ratio of 0.18. Longer chains or cations with a smaller ionic radius accelerated the rate of D(p) reduction. The number of divalent cations required for coacervation depends on different variables such as the polyphosphate concentration, the D(p), and the type of divalent cation. The formed coacervate retains the D(p) of polyphosphate originally used for coacervation, and the resulting Ca/P molar ratio depends largely on the amount of calcium being used during coacervation. Overall, this article helps one to understand the coacervation of polyphosphates in order to exploit their potential as a biomaterial.

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 score0.258
Threshold uncertainty score0.371

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 head0.035
GPT teacher head0.240
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 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

Citations47
Published2014
Admission routes2
Has abstractyes

Explore more

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