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Record W2913838502 · doi:10.1002/cjce.23463

Molecular and electronic structure elucidation of Fe<sup>2+</sup>/Fe<sup>3+</sup> complexed chelators used in iron sulphide scale removal in oil and gas wells

2019· article· en· W2913838502 on OpenAlexvenueno aff
Abdulmujeeb T. Onawole, Ibnelwaleed A. Hussein, Abdullah S. Sultan, Safwat Abdel‐Azeim, Mohamed Mahmoud, Mohammed Saad

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsChelationChemistryCarboxylateMoleculeBinding energyInorganic chemistryIonStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT Quantum chemical calculations based on DFT are employed to study the electronic structure and binding affinity of chelators used in the removal of iron sulphide scales. Three chelating agents, EDTA, HEDTA, and DTPA, are considered in this work. The complexes showed a coordination number of 5, 6, and 7 for Fe 2+ and Fe 3+ ions with HEDTA, EDTA, and DTPA, respectively. However, regarding EDTA, Fe 3+ could coordinate with an additional water molecule and form a seven‐coordinate complex. The calculated binding energies agreed with the experimental stability constants of the chelators in the order DTPA &gt; EDTA &gt; HEDTA for both Fe 2+ /Fe 3+ complexes. The binding free energies showed a spontaneous reaction with Fe 3+ having a stronger binding affinity than Fe 2+ due to electrostatic forces. This investigation provides insights regarding how chelators that are applied in iron sulphide scale removal may be designed by increasing the number of nitrogen atoms to above the number of carboxylate groups.

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 score0.122
Threshold uncertainty score0.689

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 integrity0.0000.001
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 head0.003
GPT teacher head0.172
Teacher spread0.169 · 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

Citations34
Published2019
Admission routes1
Has abstractyes

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