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Record W4229066152 · doi:10.14447/jnmes.v25i1.a05

Sodium Nitrate As a Corrosion Inhibitor of Carbon Steel in Various Concentrations of Hydrochloric Acid Solution

2022· article· en· W4229066152 on OpenAlexvenueno aff
Saad A. Jafar, Ahmad A. Aabid, Ghassan Hassan Abdul Razzaq, Jasim I. Humadi

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
FundersTikrit University
KeywordsHydrochloric acidCorrosionNitrateSodium nitrateCarbon steelCarbon fibersSodiumChemistryMetallurgyNuclear chemistryInorganic chemistryMaterials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

In the chemical and petroleum industries, metals are used in over 90% of construction units. Iron and steel are widely used metals in the fabrication and manufacturing of petroleum field operating platforms. The exposures of metals to the effect of bases or acids in the various industries can be severe to the metals specifications and thus result in suddenly failure of materials in service. So that, the need to investigate the protection of metals when exposed to various environments, as this is a very important factor in the selection of material that determines the service life of it. In the present study, the effect of various concentrations ranging from (25 to 100 mg/l) of sodium nitrate was studied in inhibiting the corrosion of carbon steel placed in different concentrations of hydrochloric acid solution (1 M, 2 M, and 3 M), the results showed that the concentration of (75 mg/l) of sodium nitrate achieved the best inhibition of the acid used in all the concentrations used, as it reduced the corrosion rate by 48.91% for a concentration of 1 M, 39.38% for a concentration of 2 M, and 35.33% for a concentration of 3 M.

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 score0.004
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.221
Teacher spread0.210 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of New Materials for Electrochemical SystemsSame topicConcrete Corrosion and DurabilityFrench-language works237,207