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Record W2518989689 · doi:10.1149/ma2016-02/12/1259

High Resolution Studies of Dealloyed Layers

2016· article· en· W2518989689 on OpenAlexaff
Ayman A. El‐Zoka, Doug D. Perovic, Brian Langelier

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsTernary operationMaterials scienceDeposition (geology)NanoporousBrittlenessNanotechnologyUnderpotential depositionBinary numberMetallurgyChemical engineeringChemistryComputer scienceGeologyElectrochemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

The nanoscale morphology of dealloyed materials has been studied for many years, from Pickering and Swann [1], through A.J. Forty [2], to recent TEM and atom-probe studies [3, 4]. Modern instruments have the ability to reveal key features of dealloying at unprecedented resolution, as well as having facilities for heating and environment control. Our particular interest is the dealloying behaviour of ternary alloys, such as AgAuPt, following the recent work of Vega [5]. Another area of interest is stress corrosion cracking of such materials, which features film-induced brittle events [6, 7]. Progress will be reported in several areas, including – High-resolution ATEM studies of dealloyed binary and ternary alloys , to observe and account for the distributions of Ag, Au and Pt for various dealloying conditions (binary AgPt is included in this part of the study). In situ heating studies of dealloyed binary and ternary alloys , to gain more insight into the observations of Vega [8] regarding oxygen-induced surface segregation of Pt, and other ways to manipulate the surface composition of the ligaments within the nanoporous material. Conventional underpotential deposition and novel “sub”-potential deposition of bulk Cu , as shown by Lee et al. for deposition of Cu into dealloyed CuPt [9]. Pore-filling of dealloyed materials by electrodeposition , often believed to be impossible, but actually a relatively easy method, provided the conditions are controlled very precisely and one is only dealing with a surface layer. Naturally this method is more challenging for ternary than binary alloys, owing to the smaller pore size. Copper is the initial metal of choice for pore filling. Atom-probe tomography studies are in progress, as favourable sites within the sample can be chosen for tip fabrication, even when pore filling is uneven. Initial results show good promise as a way to determine definitively the elemental distributions. References [1] H. W. Pickering and P. R. Swann. Electron metallography of chemical attack upon some alloys susceptible to stress corrosion cracking, Corrosion , 1963, 19 , 373t. [2] A. J. Forty. Corrosion micro-morphology of noble-metal alloys and depletion gilding, Nature , 1979, 282 , 597. [3] B. Pfeiffer, T. Erichsen, E. Epler, C. A. Volkert, P. Trompenaars, and C. Nowak. Characterization of nanoporous materials with atom probe tomography, Microscopy and Microanalysis, 2015, 21 , 557-563. [4] T. Fujita, P. Guan, K. McKenna, X. Lang, A. Hirata, L. Zhang, T. Tokunaga, S. Arai, Y. Yamamoto, N. Tanaka, Y. Ishikawa, N. Asao, Y. Yamamoto, J. Erlebacher and M. Chen. Atomic origins of the high catalytic activity of nanoporous gold, Nature Materials , 2012, 11 , 775–780. [5] A. A. Vega and R. C. Newman. Nanoporous metals fabricated through electrochemical dealloying of Ag-Au-Pt with systematic variation of Au:Pt ratio. Journal of the Electrochemical Society , 2014, 161 , C1-C10. [6] Andrew Barnes, N.A. Senior and R. C. Newman. Film-induced cleavage of Ag-Au alloys. Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science , 2009, 40 , 58-68. [7] S. Sun, X. Chen, N. Badwe and K. Sieradzki. Potential-dependent dynamic fracture of nanoporous gold. Nature Materials , 2015, 14 , 894-898. [8] A. A. Vega and R. C. Newman. Beneficial effects of adsorbate-induced surface segregation of Pt in nanoporous metals fabricated by dealloying of Ag-Au-Pt alloys. Journal of the Electrochemical Society , 2014, 161 , C11-C19. [9] L. Lee, D. He, A.G. Carcea and R.C. Newman. Exploring the reactivity and nanoscale morphology of de-alloyed layers. Corrosion Science, 2007, 49 , 72–80.

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)0.000
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 score0.036
Threshold uncertainty score0.259

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 head0.024
GPT teacher head0.244
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

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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