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Record W2999508154 · doi:10.1039/c9nr10822j

Light-activated nanozymes: catalytic mechanisms and applications

2020· review· en· W2999508154 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 · 2020
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsRegional Municipality of WaterlooNational Institute for NanotechnologyUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanomaterialsNanotechnologyEnvironmental remediationBiosensorCatalysisChemistryMaterials scienceBiologyContaminationBiochemistryEcology

Abstract

fetched live from OpenAlex

Recently, nanozymes have attracted enormous interest for their high stability, low cost and various enzyme-like activities. In nature, many biochemical reactions require light. Recently, introducing light to nanozymes has also been reported, especially for photosensitized oxygen activation. Compared to normal nanozymes, light-activated nanozymes possess several advantages including light-regulated activity, using molecular oxygen as a green oxidant, and often higher activity can be achieved. Herein, we summarize light-activated nanozymes, starting from their photophysical processes and identification of reactive oxygen species (ROS). Although the types of light-activated nanozymes are still quite limited and cannot yet mimic the same reactions as natural photo-related enzymes, they have widened the range of nanozymes. A few specific applications are highlighted, including sensing, chemical synthesis, degradation of organic pollutants, and cleavage and repair of DNA. 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)0.000
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 score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.299
Teacher spread0.275 · 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