MétaCan
Menu
Back to cohort
Record W4415703722 · doi:10.1021/acs.langmuir.5c04431

Nanozyme-Linked Immunosorbent Assays: A Kinetic Perspective

2025· article· en· W4415703722 on OpenAlexafffund
Vasily G. Panferov, Nicholas D’Abruzzo, Nadezhda A. Byzova, Juewen Liu

Bibliographic record

VenueLangmuir · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersMinistry of Science and Higher Education of the Russian FederationUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsHorseradish peroxidaseCatalysisStability (learning theory)Kinetic energySensitivity (control systems)Range (aeronautics)Perspective (graphical)

Abstract

fetched live from OpenAlex

Nanozymes with peroxidase-like (POD) activity are increasingly utilized as functional replacements for horseradish peroxidase in various assays. In particular, their application in enzyme-linked immunosorbent assays (ELISA) has led to the development of nanozyme-linked immunosorbent assays (NLISA). NLISA follow the well-established ELISA procedure and have been reported for a wide range of nanozymes and analytes. However, most developments overlook the fundamental differences between enzyme and nanozyme catalysis, often resulting in nonoptimal protocols in a kinetically limited regime. Herein, using core@shell Au@Pt and Au@Pd POD-like nanozymes, we demonstrate significant differences in the Michaelis-Menten constant depending on the shell thickness. Furthermore, for the first time, we report the unusually high stability of POD-like activity at ultralow pH values (down to minus 0.56). This unique feature enabled us to propose new strategies for terminating the catalytic reaction. In summary, we show that consideration of the distinct catalytic properties of nanozymes enables the development of NLISA protocols with up to an order of magnitude higher sensitivity and minimized background.

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)0.000
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 score0.050
Threshold uncertainty score0.819

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.283
Teacher spread0.274 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueLangmuirSame topicAdvanced Nanomaterials in CatalysisFrench-language works237,207