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Record W2973511982 · doi:10.6000/1929-5030.2019.08.01

Modified Poly(ethylene terephthalate) Nano Fibers for Removal of Copper(II) Ions

2019· article· en· W2973511982 on OpenAlexvenueno aff
Fariborz Azizinezhad

Bibliographic record

VenueJournal of Applied Solution Chemistry and Modeling · 2019
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsPoly ethyleneCopperEthyleneIonEthylene diamineNano-Materials sciencePolymer chemistryChemical engineeringChemistryComposite materialNuclear chemistryMetallurgyOrganic chemistryCatalysisEngineering

Abstract

fetched live from OpenAlex

In this research, the adsorption of Cu2+ ions from aqueous solution was investigated by the modified poly (ethylene terephthalate) fibers as a new adsorbent. The best condition of electrospinning was recorded (voltage= 10 kV- distance from nozzle= 12.5 cm - solution convcentration= 0.2 g/mL). The optimum condition for removal of Cu2+ ions were determined (pH=10 - time= 60 min - C= 120 mg/L, qm = 62.4 mg/g). The adsorption data were best fitted by dubinin-radushkevich isotherm and pseudo-second order kinetic models, as well. Structure of the nanofibers were emphasized by scanning electron microscpy (SEM) and thermo gravimetric analysis (TGA).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.383

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 head0.021
GPT teacher head0.252
Teacher spread0.232 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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