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Record W2912110014 · doi:10.3390/met9020117

Acetic Acid Etching of Mg-xGd Alloys

2019· article· en· W2912110014 on OpenAlex
Marcjanna Maria Gawlik, Björn Wiese, Alexander Welle, Jorge González, Valérie Desharnais, Jochen Harmuth, Thomas Ebel, Regine Willumeit‐Römer

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

VenueMetals · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsEtching (microfabrication)MicrostructureMaterials scienceImpurityDegradation (telecommunications)MetallurgyAcetic acidIsotropic etchingReactive-ion etchingSecondary ion mass spectrometryFabricationSurface-area-to-volume ratioVolume (thermodynamics)CorrosionChemical engineeringComposite materialChemistryMass spectrometryChromatography

Abstract

fetched live from OpenAlex

Mg-xGd alloys show potential to be used for degradable implants. As rare earth containing alloys, they are also of special interest for wrought products. All applications from medical to engineering uses require a low and controlled degradation or corrosion rate without pitting. Impurities from fabrication or machining, like Fe inclusions, encourage pitting, which inhibits uniform material degradation. This work investigates a suitable etching method to remove surface contamination and to understand the influence of etching on surface morphology. Acetic acid (HAc) etching as chemical surface treatment has been used to remove contamination from the surface. Extruded Mg-xGd (x = 2, 5 and 10) discs were etched with 250 g/L HAc solution in a volume of 5 mL or 10 mL for different times. The microstructure in the near surface region was characterized. Surface characterization was done by SEM, EDS, interferometry, and ToF-SIMS (time-of-flight secondary ion mass spectrometry) analysis. Different etching kinetics were observed due to microstructure and the volume of etching solution. Gd rich particles and higher etching temperatures due to smaller etchant volumes promote the formation of pits. Removal of 2–9 µm of material from the surface was sufficient to remove surface Fe contamination and to result in a plain surface morphology.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.034
Threshold uncertainty score0.998

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)0.0050.002

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 head0.015
GPT teacher head0.234
Teacher spread0.220 · 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