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Record W4281725414 · doi:10.1021/acs.jpcc.2c02337

Electrochemical Surface-Enhanced Raman Spectroscopy (EC-SERS) and Computational Study of Atrazine: Toward Point-of-Need Detection of Prevalent Herbicides

2022· article· en· W4281725414 on OpenAlexafffund
Najwan Albarghouthi, Maddison Eisnor, Cory C. Pye, Christa L. Brosseau

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2022
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsSaint Mary's University
FundersResearch Nova ScotiaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsAtrazineAdsorptionMoleculeRaman spectroscopyMoietyPerpendicularElectrochemistryMaterials scienceSurface-enhanced Raman spectroscopyNanotechnologyRaman scatteringChemistryElectrodeOpticsPhysical chemistryStereochemistryOrganic chemistryPhysicsPesticide

Abstract

fetched live from OpenAlex

This paper presents an electrochemical SERS (EC-SERS) and computational study of the common herbicide atrazine. Herein, we highlight that the atrazine molecule shows a clear potential-dependent SERS signal that manifests as two different adsorption orientations at the nanostructured silver surface. This finding is supported by computational work and indicates that the initial adsorption orientation is perpendicular to the surface with the C–Cl moiety pointed away from the surface, and upon stepping to negative applied voltages, the atrazine molecule rotates such that the C–Cl and isopropyl moieties are orientated more planar to the surface, while the molecule remains oriented perpendicular to the surface. To the best of our knowledge, this paper represents the first EC-SERS study of atrazine and paves the way for a rapid point-of-need detection tool for atrazine monitoring in the environment wherein an enhanced signal can be detected at negative applied voltages.

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 score0.002
Threshold uncertainty score0.224

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)0.0000.000

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 head0.010
GPT teacher head0.246
Teacher spread0.237 · 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

Citations29
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207