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Record W2995438734 · doi:10.1002/admi.201970155

Nanopaper: A Nanocellulose‐Paper‐Based SERS Multiwell Plate with High Sensitivity and High Signal Homogeneity (Adv. Mater. Interfaces 24/2019)

2019· article· en· W2995438734 on OpenAlexaff
Longyan Chen, Binbin Ying, Pengfei Song, Xinyu Liu

Bibliographic record

VenueAdvanced Materials Interfaces · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsUniversity of TorontoUniversity of New BrunswickMcGill UniversityNSCAD University
Fundersnot available
KeywordsMaterials scienceNanocelluloseAdsorptionHomogeneity (statistics)NanotechnologyIonic bondingChemical engineeringNanoparticleCelluloseOrganic chemistryChemistryComputer science

Abstract

fetched live from OpenAlex

The unique carboxylic group-rich, ultrasmooth surface of nancellulose paper (nanopaper) enables the growth of densely packed and uniformly sized silver nanoparticles through a simple dip-rinse successive ionic layer adsorption and reaction (SILAR) process. A low-cost nanopaper SERS multiwell plate is fabricated by taking advantage of the SILAR process, which provides an ultra-high enhancement factor and excellent signal homogeneity. The picomolar-level detection of small dye molecules is achieved. More details can be found in article number 1901346 by Xinyu Liu, and co-workers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.218
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes1
Has abstractyes

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