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Record W4409896775 · doi:10.3390/pr13051334

Influence of Chromium Content in Alloys on Corrosion in Saline Water Saturated with Supercritical CO2

2025· article· en· W4409896775 on OpenAlexafffund
Haofei Sun, Minkang Liu, Yimin Zeng, Jing Liu

Bibliographic record

VenueProcesses · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNatural Resources CanadaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercritical fluidChromiumCorrosionMaterials scienceMetallurgySalineChemistryOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Amid growing global efforts toward carbon capture, utilization, and storage (CCUS), this study investigates the influence of chromium (Cr) content in candidate construction alloys on their corrosion modes and kinetics in supercritical CO2 (s-CO2)-saturated saline water at 8 MPa and 50 °C. The results indicate that alloys with a Cr concentration of over approximately 9 wt.%, including P91, 316L, and Alloy 800, exhibit a satisfactory corrosion performance in this environment. During exposure to s-CO2-saturated saline water, a non-protective FeCO3 layer forms on all tested alloys. For alloys containing more than 2 wt.% Cr, an inner Cr-enriched layer concurrently grows and acts as a barrier to resist environmental attack. The integrity of the inner and outer corrosion layers becomes more compact and uniform on alloys with at least 9 wt.% Cr. Pitting is unlikely to occur on candidate alloys used for s-CO2 storage or enhanced oil recovery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2025
Admission routes2
Has abstractyes

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