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Record W4390635926 · doi:10.1016/j.omx.2024.100290

Gum Arabic-stabilized upconverting nanoparticles for printing applications

2024· article· en· W4390635926 on OpenAlex

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

VenueOptical Materials X · 2024
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsOptech (Canada)University of Ottawa
Fundersnot available
KeywordsInkwellNanotechnologyBiocompatible materialMaterials scienceNanoparticleLuminescenceThermal stabilityGum arabicArabicChemical engineeringProcess engineeringChemistryComposite materialEngineeringOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Upconverting nanoparticles (UCNPs) have been proposed for a variety of applications ranging from biomedical probes to luminescent sensors and security tags. Yet, bringing UCNPs into real-life, technologically relevant products requires implementation into industry-friendly processes. The need for stable dispersions, clean films or dry powders challenges users who look for a way to use UCNPs. In this work, an ink formulation was developed that offers a straightforward way to print UCNPs on glass and metallic substrates. The use of Gum Arabic as biocompatible emulsifier allowed to implement the NaGdF4:Er,Yb/NaGdF4 core/shell UCNPs into water-based ink formulations without the need of complex surface chemistry. The formulation, based on water, glycerin, and propanediol, exhibited good stability and applicability for printing with a commercial aerosol jet printer. Bright upconversion emission was retained upon printing, and the obtained UCNP films were used in proof-of-concept luminescent thermal sensing.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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