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Record W4402687814 · doi:10.1021/acs.analchem.4c04242

Superhydrophobic Paper Strips with Embedded Agarose-Anthocyanin Mini-Discs for Point-of-Need Quantitative pH Measurements

2024· article· en· W4402687814 on OpenAlexafffund
Michelle Leung, Lingling Zhang, Xiaochun Li, Hua‐Zhong Yu

Bibliographic record

VenueAnalytical Chemistry · 2024
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryAgaroseAnthocyaninSTRIPSQuantitative analysis (chemistry)ChromatographyNanotechnologyFood scienceComposite material

Abstract

fetched live from OpenAlex

Commercial pH paper is a quick and simple tool for measuring a solution's acidity/basicity, but it only provides qualitative or semi-quantitative results, and the synthetic indicator dyes within can be toxic or carcinogenic. Although pH meters enable more accurate and quantitative analysis, they are less convenient to operate and are tedious to calibrate. This presents a need for an alternative pH testing method for applications where it is not easy or possible to use a pH meter, yet quantitative results are desired. We report herein the fabrication of a pH test strip made from superhydrophobic paper and agarose-anthocyanin film discs. In the proposed method, test strips are dipped into samples and then imaged with a portable scanner (or a smartphone). The color of the film is extracted with ImageJ software (or a mobile app), using the RGB color system. By generating a calibration curve relating the film color to the sample pH using standard buffer solutions, we are able to quantify the pH of beverages and other liquids with an accuracy and precision comparable to that of a pH meter. The test strips offer the same convenience as conventional pH paper, with the added capabilities of quantitation and multiplexed testing, which presents a practical tool for point-of-need pH analysis.

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 score0.368
Threshold uncertainty score0.799

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 head0.026
GPT teacher head0.259
Teacher spread0.233 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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