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Record W4415398603 · doi:10.1021/acs.langmuir.5c03334

Spectroscopic Insights into Controlling Sidewall Faceting of TiO <sub>2</sub> Nanowires via Au–Ag Bimetallic Seeds

2025· article· en· W4415398603 on OpenAlexafffund
Zhina Razaghi, Minghui Lin, Ning Chen, Kai Cui, Guo‐zhen Zhu

Bibliographic record

VenueLangmuir · 2025
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsNational Research Council CanadaNational Institute for NanotechnologyCanadian Light Source (Canada)University of SaskatchewanUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersMitacsCanada Research ChairsUniversity of Manitoba
KeywordsFacetingNanowireNucleationBimetallic stripMorphology (biology)Electron energy loss spectroscopySpectroscopyNanostructureSurface energy

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Precisely controlling the morphology and crystallographic features of single-crystalline, defect-free nanowires is key to advancing their applications. This study demonstrates that the local compositional heterogeneity within Au–Ag bimetallic seeds─specifically the presence of Ag-rich domains at a particular corner of the seed–nanowire interface─drives a transition from ⟨110⟩-oriented bead-like TiO 2 nanowires to ⟨111⟩-oriented prismatic nanowires during vapor-phase growth. The strong affinity between Ag and oxygen-containing growth species, as identified through X-ray absorption spectroscopy and electron energy loss spectroscopy, facilitates site-specific nucleation and lateral growth across the hexagonal seed–nanowire interface for prismatic nanowires. These findings underscore the critical role of localized seed composition at the seed–nanowire interface in governing nucleation behavior and enabling controlled nanowire morphology through seed engineering.

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 score0.007
Threshold uncertainty score0.685

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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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