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Record W3096603534 · doi:10.1039/d0ra06743a

Synthesis of FeO@SiO <sub>2</sub> –DNA core–shell engineered nanostructures for rapid adsorption of heavy metals in aqueous solutions

2020· article· en· W3096603534 on OpenAlexafffund
David Patiño‐Ruiz, Lars Rehmann, Mehrab Mehrvar, Édgar Quiñones-Bolaños, Adriana Herrera

Bibliographic record

VenueRSC Advances · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsToronto Metropolitan UniversityToronto Public HealthWestern University
FundersWestern UniversityRyerson University
KeywordsAdsorptionAqueous solutionChemical engineeringSuperparamagnetismInorganic chemistryChemistryMetal ions in aqueous solutionNanocompositeMaterials scienceMetalNanotechnologyOrganic chemistryMagnetization

Abstract

fetched live from OpenAlex

-DNA shows higher adsorption efficiency of 69% for Pb(ii) removal compared to As(iii) (51%) and Hg(ii) (41%). The selectivity towards Pb(ii) is attributed to the similar acid nature to ds-DNA, where the ionic strength interaction provides good affinity and stability. The facile synthesis and rapid adsorption suggest a promising nanostructure for the remediation of water sources contaminated with heavy metal ions and can be extended to other complex molecules.

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 score0.018
Threshold uncertainty score0.633

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 head0.015
GPT teacher head0.255
Teacher spread0.239 · 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

Citations23
Published2020
Admission routes2
Has abstractyes

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