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Record W3199472792 · doi:10.1002/adem.202100477

3‐Dimensional Printing of Hydrogel‐Based Nanocomposites: A Comprehensive Review on the Technology Description, Properties, and Applications

2021· review· en· W3199472792 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

VenueAdvanced Engineering Materials · 2021
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiofabricationSelf-healing hydrogelsMaterials scienceNanotechnologyNanocompositeExtracellular matrix3D printingTissue engineeringBiocompatible materialBiomedical engineeringComposite materialChemistryEngineering

Abstract

fetched live from OpenAlex

Increasing demand for customized implants and tissue scaffolds requires advanced biomaterials and fabricating processes for fabricating three‐dimensional (3D) structures that resemble the complexity of the extracellular matrix (ECM). Lately, biofabrication approaches such as cell‐laden (soft) hydrogel 3D printing (3DP) have been of increasing interest in the development of 3D functional environments similar to natural tissues and organs. Hydrogels that resemble biological ECMs can provide mechanical support and signaling cues to cells to control their behavior. Although the capability of hydrogels to produce artificial ECMs can regulate cellular behavior, one of the major drawbacks of working with hydrogels is their inferior mechanical properties. Therefore, keeping and enhancing the mechanical integrity of fabricated scaffolds has become an essential matter for 3D hydrogel structures. Herein, 3D‐printed hydrogel‐based nanocomposites (NCs) are evaluated systematically in terms of introducing novel techniques for 3DP of hydrogel‐based materials, properties, and biomedical applications.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.050
GPT teacher head0.289
Teacher spread0.239 · 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