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Record W1873814962 · doi:10.1039/c5nr04176g

Accelerating peroxidase mimicking nanozymes using DNA

2015· article· en· W1873814962 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

VenueNanoscale · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDNAPeroxidaseHydrogen peroxideChemistryNanoparticleSubstrate (aquarium)DNA oxidationPolymerCombinatorial chemistryOxideIron oxide nanoparticlesPhotochemistryIron oxideInorganic chemistryBiophysicsNanotechnologyDNA damageMaterials scienceOrganic chemistryBiochemistryEnzyme

Abstract

fetched live from OpenAlex

DNA-capped iron oxide nanoparticles are nearly 10-fold more active as a peroxidase mimic for TMB oxidation than naked nanoparticles. To understand the mechanism, the effect of DNA length and sequence is systematically studied, and other types of polymers are also compared. This rate enhancement is more obvious with longer DNA and, in particular, poly-cytosine. Among the various polymer coatings tested, DNA offers the highest rate enhancement. A similar acceleration is also observed for nanoceria. On the other hand, when the positively charged TMB substrate is replaced by the negatively charged ABTS, DNA inhibits oxidation. Therefore, the negatively charged phosphate backbone and bases of DNA can increase TMB binding by the iron oxide nanoparticles, thus facilitating the oxidation reaction in the presence of hydrogen peroxide.

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)0.001
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 score0.046
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.078
GPT teacher head0.310
Teacher spread0.231 · 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