MétaCan
Menu
Back to cohort

A Review of Recent Developments in Nanocellulose-Based Conductive Hydrogels

2020· review· en· W3107145709 on OpenAlexaff
Iman Yousefi, Wen Zhong

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCurrent NanoscienceSame topicAdvanced Cellulose Research StudiesFrench-language works237,207