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Record W3016980942 · doi:10.1021/acsami.0c03405

An Ultrasensitive Fluorescent Paper-Based CO<sub>2</sub> Sensor

2020· article· en· W3016980942 on OpenAlex
Hui Wang, Sergei I. Vagin, Bernhard Rieger, A. Meldrum

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Materials & Interfaces · 2020
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsFluorescenceMaterials sciencePhotobleachingCarbon dioxide sensorCarbonic acidChromophoreFilter (signal processing)OptoelectronicsNanotechnologyComputer sciencePhotochemistryOpticsCarbon dioxideChemistry

Abstract

fetched live from OpenAlex

We demonstrate a versatile and easily fabricated paper-based CO2 sensor. The sensor consists of a specially designed fluorescent color-shift chromophore infused into standard filter paper. The emission color of the resulting fluorescent paper changes upon exposure to CO2 due to the formation of carbonic acid, which underlies the sensing mechanism. By using a ratiometric method, the undesirable effects of photobleaching can be eliminated, leading to a stable and repeatable sensor performance. These multiuse sensors have a response time on the order of 1 min and feature low detection limits for a paper-based CO2 gas sensor, suggesting possible low-cost applications in smart buildings or other facilities in which CO2 levels are required to be continuously monitored.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.003
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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