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Record W2800699816 · doi:10.1149/2.0741807jes

Effect of Iron Oxidizing Bacteria Biofilm on Corrosion Inhibition of Imidazoline Derivative in CO<sub>2</sub>-Containing Oilfield Produced Water with Organic Carbon Source Starvation

2018· article· en· W2800699816 on OpenAlexaff
Hongwei Liu, Y. Frank Cheng, Dake Xu, Hongfang Liu

Bibliographic record

VenueJournal of The Electrochemical Society · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Calgary
FundersHuazhong University of Science and TechnologyNational Natural Science Foundation of China
KeywordsBiofilmCorrosionOxidizing agentCarbon steelChemistryIron bacteriaCarbon fibersBacteriaMetallurgyEnvironmental chemistryNuclear chemistryMaterials scienceOrganic chemistryBiologyComposite material

Abstract

fetched live from OpenAlex

In this work, corrosion of a Q235 carbon steel in CO2-containing oilfield produced water in the presence of iron oxidizing bacteria (IOB) with organic carbon source starvation and the inhibiting performance of imidazoline derivative were investigated by weight loss, electrochemical measurements and surface analysis techniques. Results show that iron oxidizing bacteria (IOB) could survive well in the aqueous environments with organic carbon source starvation. Corrosion of the steel occurred under the IOB biofilm. Both the planktonic and sessile IOB cell counts after 14 days of testing were related to the biofilm incubation time. The corrosion rate of the steel covered with 7-day IOB biofilm was lowest suggesting that this biofilm had the weakest corrosivity in the test solution. The inhibition performance of the inhibitor was affected by the IOB biofilm formed on steel surface. There was a higher inhibition efficiency of the inhibitor, when the steel was covered with a 2-day IOB biofilm due to less IOB biofilm formed on the steel.

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)0.001
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 score0.003
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.227
Teacher spread0.221 · 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

Citations24
Published2018
Admission routes1
Has abstractyes

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