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Non-invasive paper-based sensors containing rare-earth-doped nanoparticles for the detection of D-glucose

2024· article· en· W4396508612 on OpenAlex
Gabriel López-Peña, Eva Ortiz-Mansilla, A. Arranz, Nicoleta Bogdan, Miguel Manso‐Silván, Emma Martín Rodríguez

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.

Bibliographic record

VenueColloids and Surfaces B Biointerfaces · 2024
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsConcordia University
FundersAgencia Estatal de InvestigaciónUniversidad Autónoma de MadridComunidad de Madrid
KeywordsRare earthNanoparticleDopingNanotechnologyChemistryMaterials scienceRemote sensingMineralogyGeologyOptoelectronics

Abstract

fetched live from OpenAlex

Today, diabetes mellitus is one of the most common diseases that affects the population on a worldwide scale. Patients suffering from this disease are required to control their blood-glucose levels several times a day through invasive methods such as piercing their fingers. Our NaGdF4: 5% Er3+, 3% Nd3+ nanoparticles demonstrate a remarkable ability to detect D-glucose levels by analysing alterations in their red-to-green ratio, since this sensitivity arises from the interaction between the nanoparticles and the OH groups present in the D-glucose molecules, resulting in discernible changes in the emission of the green and red bands. These luminescent sensors were implemented and tested on paper substrates, offering a portable, low-cost and enzyme-free solution for D-glucose detection in aqueous solutions with a limit of detection of 22 mg/dL. With this, our study contributes to the development of non-invasive D-glucose sensors, holding promising implications for managing diabetes and improving overall patient well-being with possible future applications in D-glucose sensing through tear fluid.

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 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 score0.359
Threshold uncertainty score0.487

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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