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Surface processing for iron-based degradable alloys: A preliminary study on the importance of acid pickling

2021· article· en· W3202387501 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioactive Materials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationFonds de recherche du Québec – Nature et technologiesMinistère de l'Économie, de l’Innovation et des Exportations du Québec
KeywordsPicklingMaterials scienceWettingElectropolishingAlloyMetallurgyChemical engineeringFabricationMetalCharacterization (materials science)NanotechnologyComposite materialChemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

The formation of a heterogeneous oxidized layer, also called scale, on metallic surfaces is widely recognized as a rapid manufacturing event for metals and their alloys. Partial or total removal of the scale represents a mandatory integrated step for the industrial fabrication processes of medical devices. For biodegradable metals, acid pickling has already been reported as a preliminary surface preparation given further processes, such as electropolishing. Unfortunately, biodegradable medical prototypes presented discrepancies concerning acid pickling studies based on samples with less complex geometry (e.g., non-uniform scale removal and rougher surface). Indeed, this translational knowledge lacks a detailed investigation on this process, deep characterization of treated surfaces properties, as well as a comprehensive discussion of the involved mechanisms. In this study, the effects of different acidic media (HCl, HNO3, H3PO4, CH3COOH, H2SO4 and HF), maintained at different temperatures (21 and 60 °C) for various exposition time (15–240 s), on the chemical composition and surface properties of a Fe–13Mn-1.2C biodegradable alloy were investigated. Changes in mass loss, morphology and wettability evidenced the combined effect of temperature and time for all conditions. Pickling in HCl and HF solutions favor mass loss (0.03–0.1 g/cm2) and effectively remove the initial scale.

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)0.001
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 score0.528

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 head0.054
GPT teacher head0.304
Teacher spread0.250 · 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