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Record W3033798253 · doi:10.1002/bio.3893

Nanozyme‐based luminescence detection

2020· review· en· W3033798253 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLuminescence · 2020
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLuminescenceLuminescent MeasurementsChemistryComputer scienceMaterials scienceOptoelectronics

Abstract

fetched live from OpenAlex

Nanozymes refer to nanomaterial-based enzyme mimics. Most nanozyme assays use chromogenic molecules as substrates that can produce an absorbance change in the visible region upon reaction. For certain applications, such as imaging, fluorescence and luminescence signals are required. Fluorescence detection is also advantageous for sensor development due to its low background signal and high sensitivity. A few substrates, such as Amplex Red and luminol, allow luminescence generation with many oxidative nanozymes. In addition, these substrates can work at neutral pH, useful for intracellular applications. In this review, we summarize luminescence signalling-based nanozyme systems. We begin with the properties of commonly used substrates. A few specific nanozymes are highlighted, including metal nanoparticles, metal oxide nanomaterials, carbon-based nanomaterials, and metal-organic frameworks. Finally, a few future research opportunities are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.037
GPT teacher head0.318
Teacher spread0.281 · 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