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Record W3158231859 · doi:10.1016/j.carbpol.2021.118114

Direct ink writing of aloe vera/cellulose nanofibrils bio-hydrogels

2021· article· en· W3158231859 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

VenueCarbohydrate Polymers · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of British Columbia
FundersAalto-YliopistoAcademy of Finland
KeywordsSelf-healing hydrogelsMaterials scienceCellulosePorosityShrinkageAloe vera3d printedComposite materialChemical engineeringNanotechnologyPolymer chemistryBiomedical engineering

Abstract

fetched live from OpenAlex

Direct-ink-writing (DIW) of hydrogels has become an attractive research area due to its capability to fabricate intricate, complex, and highly customizable structures at ambient conditions for various applications, including biomedical purposes. In the current study, cellulose nanofibrils reinforced aloe vera bio-hydrogels were utilized to develop 3D geometries through the DIW technique. The hydrogels revealed excellent viscoelastic properties enabled extruding thin filaments through a nozzle with a diameter of 630 μm. Accordingly, the lattice structures were printed precisely with a suitable resolution. The 3D-printed structures demonstrated significant wet stability due to the high aspect ratio of the nano- and microfibrils cellulose, reinforced the hydrogels, and protected the shape from extensive shrinkage upon drying. Furthermore, all printed samples had a porosity higher than 80% and a high-water uptake capacity of up to 46 g/g. Altogether, these fully bio-based, porous, and wet stable 3D structures might have an opportunity in biomedical fields.

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), Insufficient payload (model declined to judge)
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 score0.020
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.243
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