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Record W2781609059 · doi:10.1039/c7ra10694g

Controlled synthesis of Cu nanoparticle arrays with surface enhanced Raman scattering effect performance

2018· article· en· W2781609059 on OpenAlexaff
Qianqian Ding, Lifeng Hang, Liang Ma

Bibliographic record

VenueRSC Advances · 2018
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsMD Precision (Canada)
Fundersnot available
KeywordsRaman scatteringNanoshellMaterials scienceMonolayerNanomaterialsNanoparticleSubstrate (aquarium)Deposition (geology)NanotechnologyRaman spectroscopyChemical engineeringOptoelectronicsOptics

Abstract

fetched live from OpenAlex

Herein, we report the synthesis of a 350 nm Cu nanoparticle array with different period combinations by a method based on a monolayer and have further investigated its surface-enhanced Raman scattering (SERS) properties experimentally. The SERS properties of the 350 nm Cu nanoparticle array were investigated, and the influence of the thickness of the Cu nanoshell was studied. The results demonstrated that the 18 min ion-sputtering deposition can improve the SERS activity in addition to good stability. This study can provide an optimized method for some inexpensive nanomaterials as highly active SERS substrates and a good solution to the interference caused by substrate impurity.

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 score0.601

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 head0.005
GPT teacher head0.231
Teacher spread0.225 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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