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Record W4405531938 · doi:10.1016/j.talanta.2024.127390

Microscale quantitation of heavy metals by back-scattering interferometry in conjunction with photothermal effect using a single laser beam

2024· article· en· W4405531938 on OpenAlexaff
Xiaoliang Zhang, Xiaohong Chen, Xiaofang Shi, Xiaochun Li, Hua‐Zhong Yu

Bibliographic record

VenueTalanta · 2024
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsChemistryMicroscale chemistryPhotothermal therapyInterferometryLaserLaser beamsOpticsBeam (structure)

Abstract

fetched live from OpenAlex

A microanalytical technique based on the photothermal effect in conjunction with back-scattering interferometry (BSI) using a single laser beam was developed for quantitative detection of heavy metals. After the chromogenic reaction of an analyte in a capillary tube, the photothermal effect induced by irradiation with the same laser beam leads to a change of the refractive index of the solution, which can be "quantified" using the BSI technique. For prove-of-concept, Cu(II) was chosen as the trial analyte, for which the solution changes to purplish through reacting with the chromogenic reagent; a single laser beam of 532 nm was adapted for both inducing the photothermal effect and realizing BSI detection. With as little as 1.0 μL solution, a limit of detection (LOD) of 0.10 mg/L for Cu(II) was achieved. In addition, the versatility of the technique was demonstrated by detecting other two heavy metal ions, Fe(II) and Cr(VI), with limits of detection of 0.06 mg/L and 0.04 mg/L, respectively. The demonstrated detection sensitivity, application versatility, and instrumentation simplicity of this new technique promises it as a practical tool for environmental monitoring and beyond.

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 score0.011
Threshold uncertainty score0.476

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 head0.225
Teacher spread0.216 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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