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Record W2306296774 · doi:10.1002/aic.15232

Mathematical modeling of the full molecular weight distribution in ATRP techniques

2016· article· en· W2306296774 on OpenAlexaff
Iván Zapata‐González, Robin A. Hutchinson, Kevin A. Payne, Enrique Saldívar‐Guerra

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsQueen's University
FundersConsejo Nacional de Ciencia y TecnologíaIndian Council of Agricultural Research
KeywordsAtom-transfer radical-polymerizationRadicalWork (physics)Molar mass distributionPolymerizationAtom (system on chip)ChemistryElectron transferRadical polymerizationThermodynamicsMaterials sciencePolymerPhotochemistryComputer scienceOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

In this work the molecular weight distribution (MWD) of several atom transfer radical polymerization (ATRP) techniques has been derived and solved using the Reduced Stiffness by Quasi Steady State Approximation (RSQSSA) methodology. The Quasi Steady State Approximation has been validated on the living radicals for normal, Simultaneous Reversible and Normal Initiation and Activators Regenerated by Electron Transfer (ARGET), and it is shown that the information lost due to its application is negligible. According to these results, RSQSSA shows the best performance in terms of wall‐clock time and required memory in comparison to implicit techniques and Predici. In the case of the ARGET technique, the model predictions show good agreement with experimental data. Finally, an analysis on the impact of the slow and fast activation of the initiator on the MWD using ARGET has been carried out, indicating that the optimal initiator to control the MWD should exhibit activation‐deactivation rates very similar to those of the polymeric equilibrium. © 2016 American Institute of Chemical Engineers AIChE J , 62: 2762–2777, 2016

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 score0.131
Threshold uncertainty score0.142

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 head0.005
GPT teacher head0.187
Teacher spread0.183 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

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