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A Review of Recent Developments in Nanocellulose-Based Conductive Hydrogels

2020· review· en· W3107145709 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

VenueCurrent Nanoscience · 2020
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNanocelluloseSelf-healing hydrogelsMaterials scienceNanofiberNanotechnologyCelluloseBiocompatibilityBacterial celluloseChemical engineeringPolymer chemistryEngineering

Abstract

fetched live from OpenAlex

Nanocellulose has attracted much research interest owing to its biocompatibility, low density, environmental sustainability, flexibility, ease of surface modification, excellent mechanical properties and ultrahigh surface areas. Recently, lots of research efforts have focused on nanocellulose- based conductive hydrogels for different practical applications, including electronic devices, energy storage, sensors, composites, tissue engineering and other biomedical applications. A wide variety of conductive hydrogels have been developed from nanocellulose, which can be in the form of cellulose nanofibers (CNF), cellulose nanocrystals (CNC) or bacterial cellulose (BC). This review presents the recent progress in the development of nanocellulose-based conductive hydrogels, their advanced functions, including 3D printability, self-healing capacity and high mechanical performances, as well as applications of the conductive nanocellulose hydrogels.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.174
GPT teacher head0.423
Teacher spread0.250 · 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