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Record W4303628485 · doi:10.3390/cryst12101414

Orientation Selection of Supported Au Nanoparticles on (111)- and (001)-Terminated SrTiO3 Substrates

2022· article· en· W4303628485 on OpenAlex
Wangwang Kuang, Guo‐zhen Zhu

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

VenueCrystals · 2022
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials scienceNanoparticleSurface energyOrientation (vector space)Substrate (aquarium)Particle (ecology)FabricationSurface (topology)NanotechnologyComposite materialGeometry

Abstract

fetched live from OpenAlex

Orientation-dependent performance has been demonstrated in different materials consisting of nanoparticles on substrates. The fabrication of desirably oriented nanoparticles requires knowledge of orientation selection rules. Based on the Wulff–Kaishew theory, our analysis shows that the energy-favorable orientation(s), is influenced by the surface energy of particles, in addition to the dominant factor, i.e., the energy difference between particle/substrate interfacial energy and surface energy of the substrate. To verify this, a model system of dewetted Au nanoparticles on SrTiO3 is studied. The {111}-terminated SrTiO3 supports only {111}-orientated Au particles, with the lowest interfacial energy. On the other hand, {100}-terminated SrTiO3 supports multiple Au particles, with {111}-, {100}-, {110}- orientations, as a possible result of close surface energy contributions. The above orientations can be additionally manipulated by changing the heat treatment temperature. Our results provide fundamental insights into fabricating supported nanoparticles for practical applications.

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 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 score0.011
Threshold uncertainty score1.000

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)0.0010.000

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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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