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Record W2513174922 · doi:10.5006/2193

Materials Selection for Use in Concentrated Hydrochloric Acid

2016· article· en· W2513174922 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCORROSION · 2016
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsWestern University
Fundersnot available
KeywordsHydrochloric acidSelection (genetic algorithm)MetallurgyChemistryMaterials scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Hydrochloric acid (HCl) is an important mineral acid with many uses, including the pickling of steel, acid treatment of oil wells, and chemical cleaning and processing. This acid is extremely corrosive and its aggressiveness can change drastically depending on its concentration, the temperature, and contamination by oxidizing impurities. One of the most commonly encountered oxidizing impurities is the ferric ion. In general, stainless steels cannot tolerate aggressive HCl solutions, hence the need to use corrosion resistant nickel-based alloys. A part of this study focused on the role of alloying elements on the corrosion performance of commercial nickel-based alloys UNS N10276, UNS N06022, UNS N06200, UNS N07022, UNS N10362, UNS N10675, UNS N06059, and UNS N06625, in HCl solutions, with and without the presence of oxidizing impurities (ferric ions). Aggressive HCl solutions can also be used to simulate the critical crevice solution. Therefore, another aspect of this research is to investigate the role of alloying elements in nickel-based alloys on the inhibition of crevice corrosion. In the present study, various standard corrosion test methodologies, conservative electrochemical techniques, and a range of surface analytical tools have been utilized.

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.

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 score0.015
Threshold uncertainty score0.345

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 head0.025
GPT teacher head0.247
Teacher spread0.223 · 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