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Record W4323294815 · doi:10.1002/anie.202301879

Atomic‐Level Regulation of Cobalt Single‐Atom Nanozymes: Engineering High‐Efficiency Catalase Mimics

2023· article· en· W4323294815 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.

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsActive siteCatalaseCatalysisChemistryEnzymeArtificial enzymeRational designCobaltNanotechnologyAtom (system on chip)KineticsProtein engineeringMaterials scienceComputer sciencePhysicsBiochemistryInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract Nanozymes aim to mimic the highly evolved active centers of natural enzymes. Despite progress in nanozyme engineering, their catalytic performance is much less favorable compared with natural enzymes. This study shows that precise control over the atomic configuration of the active centers of Co single‐atom nanozymes (SAzymes) enables the rational regulation of their catalase‐like performance guided by theorical calculations. The constructed Co‐N 3 PS SAzyme exhibits an excellent catalase‐like activity and kinetics, exceeding the representative controls of Co‐based SAzymes with different atomic configurations. Moreover, we developed an ordered structure‐oriented coordination design strategy for rationally engineering SAzymes and established a correlation between the structure and enzyme‐like performance. This work demonstrates that precise control over the active centers of SAzymes is an efficient strategy to mimic the highly evolved active sites of natural enzymes.

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 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 score0.021
Threshold uncertainty score0.806

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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