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Record W2312293328 · doi:10.1021/am401205w

Synthesis of Au-Decorated Tripod-Shaped Te Hybrids for Applications in the Ultrasensitive Detection of Arsenic

2013· article· en· W2312293328 on OpenAlexaff
Demeng Wang, Yuewu Zhao, Huile Jin, Jin-Xia Zhuang, Weimin Zhang, Shun Wang, Jichang Wang

Bibliographic record

VenueACS Applied Materials & Interfaces · 2013
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Windsor
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsTripod (photography)Materials scienceHydrothermal circulationSelectivityDetection limitElectrochemistryMicrostructureMetalIonMetal ions in aqueous solutionHydrothermal synthesisSemiconductorAntimonyChemical engineeringNanotechnologyPhysical chemistryOptoelectronicsElectrodeMetallurgyCatalysisOpticsChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Novel Au-decorated Te hybrids with a tripod-shaped planar microstructure were prepared through a two-step hydrothermal process: the synthesis of Te single crystals and the subsequent self-sacrificial reaction of Te template with HAuCl4. Based on the influences of reaction temperature and solvent compositions on the as-obtained microstructures, a plausible mechanism was proposed to account for the formation of the tripod-shaped Te and Au/Te crystals. The as-prepared Au/Te hybrids have the sensitivity of 6.35 μA/ppb in the electrochemical detection of As(III), which represents the highest sensitivity reported in literature. The Au/Te sensor also has a low detection limit of 0.0026 ppb and could work in complex mixtures containing As(III), Cu(II) and other heavy metal ions, exhibiting excellent selectivity on As(III) and Cu(II) ions. The enhanced electrocatalytic property may be attributed to the synergetic interactions between the noble metal and semiconductor and the presence of a large number of active sites on the hybrids surface.

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 score0.483

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 head0.009
GPT teacher head0.229
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.

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

Citations67
Published2013
Admission routes1
Has abstractyes

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