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Record W2888979662 · doi:10.1016/j.heliyon.2018.e00766

Chiral zirconium quantum dots: A new class of nanocrystals for optical detection of coronavirus

2018· article· en· W2888979662 on OpenAlexafffund
Syed Rahin Ahmed, Seon Woo Kang, Sangjin Oh, Jaebeom Lee, Suresh Neethirajan

Bibliographic record

VenueHeliyon · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Rural AffairsNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Guelph
KeywordsConjugated systemQuantum dotMaterials scienceFluorescenceNanoparticleAnalyteCircular dichroismDetection limitBiosensorNanotechnologyChemistryPolymerCrystallographyChromatographyOptics

Abstract

fetched live from OpenAlex

A synthetic way of chiral zirconium quantum dots (Zr QDs) was presented for the first time using L(+)-ascorbic acid acts as a surface as well as chiral ligands. Different spectroscopic and microscopic analysis was performed for thorough characterization of Zr QDs. As-synthesized QDs exhibited fluorescence and circular dichroism properties, and the peaks were located at 412 nm and 352 nm, respectively. MTT assay was performed to test the cytotoxicity of the synthesized Zr QDs against rat brain glioma C6 cells. Synthesized QDs was further conjugated with anti-infectious bronchitis virus (IBV) antibodies of coronavirus to form an immunolink at the presence of the target analyte and anti-IBV antibody-conjugated magneto-plasmonic nanoparticles (MPNPs). The fluorescence properties of immuno-conjugated QD-MP NPs nanohybrids through separation by an external magnetic field enabled biosensing of coronavirus with a limit of detection of 79.15 EID/50 μL.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.019
GPT teacher head0.305
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), 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

Citations81
Published2018
Admission routes2
Has abstractyes

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