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Record W21711035 · doi:10.1021/jp2017488

SURFACE MODIFICATION WITH POLYETHYLENE GLYCOL-PROTEIN CONJUGATES FOR IMPROVED BLOOD COMPATIBILITY

2011· dissertation· en· W21711035 on OpenAlexfundno aff
Sara Alibeik

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2011
Typedissertation
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPolyethylene glycolSurface modificationCompatibility (geochemistry)ConjugatePEG ratioChemistryMaterials scienceChemical engineeringBiochemistryEngineeringMathematicsComposite materialBusiness

Abstract

fetched live from OpenAlex

In this work, the molecular based Variable Range Statistical Associating Fluid Theory (SAFT-VR) has been used to estimate the global phase equilibria diagram of the ternary mixture water + carbon dioxide + methane, over a wide pressure and temperature range. An accurate determination of the phase equilibria of this mixture is relevant in Petrophysics, as, for instance, in enhanced natural gas recovery from low permeability reservoirs (the so-called tight gas reservoirs), or in geology, as it is the basic composition of many geological fluids. A previous study on the phase behavior of the binary mixtures involved is presented, using in a transferable manner the characteristic molecular parameters for the three molecules involved. The ternary mixture presents a very rich and complex phase behavior, with a wide region of the thermodynamic space of phases (at higher pressures) presenting a large gap of ternary liquid-liquid equilibria, that upon descending pressures leads to the transition to a three-phase liquid-liquid-vapor equilibria region, and both regions are separated by a continuous critical end point line. The ability of the theory to describe this complex multicomponent mixture phase transition with a reduced and physically sound set of characteristic parameters must be underlined.

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 score0.002
Threshold uncertainty score0.622

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 head0.011
GPT teacher head0.230
Teacher spread0.218 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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