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NANOPARTÍCULAS DE ORO FUNCIONALIZADAS CON L-CISTEÍNA PARA DETECCIÓN DE ARSÉNICO EN AGUA

2022· article· es· W4281766949 on OpenAlexaff
Edgar González, Yesid A. Acuña, Ana M. Quiroz

Bibliographic record

VenueIngeniería Investigación y Desarrollo · 2022
Typearticle
Languagees
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsCentennial College
Fundersnot available
KeywordsPhysicsHumanitiesMolecular biologyChemistryArtBiology

Abstract

fetched live from OpenAlex

En este artículo se reportan resultados experimentales de interacciones del aminoácido L-cisteína (Cis) con nanopartículas de oro (AuNPs) y nanopartículas de oro funcionalizadas con Cis (AuNPs+Cis) cuando se incorpora en la solución iones de As3+. En los procesos de agregación se evalúa el papel de la concentración de la Cis en la funcionalización, además del tamaño de las nanopartículas cuando se incorpora a la solución el agente iónico de interés. Los resultados muestran una sensible dependencia en la agregación de las nanosondas AuNPs+Cis en función de su tamaño, con un registro de mayor sensibilidad para nanopartículas de mayor diámetro. Se observa que la concentración de la Cis en el proceso defuncionalización es condición necesaria para la programación de la estabilidad de las nanosondas, aspecto esencial paraconfigurar un modo estable para detección del agente iónico. De manera que, estos resultados permiten establecer criteriosútiles para el diseño de sensores colorimétricos para detección de metales pesados en aguas contaminadas.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.305
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.013
GPT teacher head0.232
Teacher spread0.220 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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