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Record W2888484826 · doi:10.1039/c8na00012c

Electrochemically assisted flexible lanthanide upconversion luminescence sensing of heavy metal contamination with high sensitivity and selectivity

2018· article· en· W2888484826 on OpenAlexfundno aff
Yuen-Ting Wong, Sin‐Yi Pang, Ming‐Kiu Tsang, Yan Liu, Haitao Huang, S. F. Yu, Jianhua Hao

Bibliographic record

VenueNanoscale Advances · 2018
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsnot available
FundersHong Kong Polytechnic UniversityBC Cancer AgencyResearch Grants Council, University Grants Committee
KeywordsLanthanideLuminescencePhoton upconversionContaminationMetalSelectivityMaterials scienceSensitivity (control systems)Inorganic chemistryChemistryOptoelectronicsMetallurgyIonOrganic chemistry

Abstract

fetched live from OpenAlex

ions can be effectively accumulated through oxidation, yielding a remarkable improvement in the selectivity and sensitivity. A more outstanding detection limit of the sensor was achieved at 82 ppb under the E-chem assistance, with 300-fold enhancement compared to the detection without the E-chem effect. This sensing approach can be an alternative to molecular quenchers and open up new possibilities for simple, rapid and portable sensing of metal ions.

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 score0.017
Threshold uncertainty score0.776

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.230
Teacher spread0.222 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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