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Record W7117294733 · doi:10.1021/acs.nanolett.5c04784

Hexoctahedron in a Nanoframe: Tailoring Stationary and Open Nanogaps for Single-Particle Surface-Enhanced Raman Scattering

2025· article· en· W7117294733 on OpenAlexaff
Hayoon Jung, Jung Young Jung, Sunghee Kang, Yushin Kim, Dae Han Wi, Heon Chul Kim, Yonghyeon Kim, Jong Wook Hong, Sang Woo Han

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of Korea
KeywordsRaman scatteringPlasmonNanostructureSubstrate (aquarium)Raman spectroscopyNanocrystalFabricationMoleculeScattering

Abstract

fetched live from OpenAlex

Plasmonic nanostructures with sharp features and nanogaps have been of immense interest in surface-enhanced Raman scattering (SERS) applications due to their strong hot spots generated at such morphological features. Here, we report a synthetic route to produce Au hexoctahedron-in-a-nanoframe structures (Au HIAFs), in which the high-curvature vertices of the Au hexoctahedral nanocrystal core fit tightly into the frame corners without the aid of an anchoring linker, realizing stationary, open nanogaps at sharp features within a single nanostructure. The prepared Au HIAFs can effectively exploit both sharp feature- and gap-enhanced electromagnetic fields, thereby exhibiting prominent SERS performance at the single-particle level. Due to the finely controlled, linker-free nanogaps of the Au HIAFs, the SERS detection of a series of analyte molecules could also be possible with an identical single Au HIAF through the reversible adsorption and desorption of analytes, highlighting their potential as a practical, reusable substrate for SERS-related 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.

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 score0.006
Threshold uncertainty score0.486

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 head0.277
Teacher spread0.253 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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