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Record W4401059520 · doi:10.1002/smll.202403583

Aerogels based on Bacterial Nanocellulose and their Applications

2024· review· en· W4401059520 on OpenAlexaff
Mahyar Panahi‐Sarmad, Niloofar Alikarami, Tianyu Guo, Mehri Haji, Feng Jiang, Orlando J. Rojas

Bibliographic record

VenueSmall · 2024
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNanocelluloseMaterials scienceNanotechnologyBacterial cellulosePolymer scienceChemical engineeringCelluloseEngineering

Abstract

fetched live from OpenAlex

Microbial cellulose stands out for its exceptional characteristics in the form of biofilms formed by highly interlocked fibrils, namely, bacterial nanocellulose (BNC). Concurrently, bio-based aerogels are finding uses in innovative materials owing to their lightweight, high surface area, physical, mechanical, and thermal properties. In particular, bio-based aerogels based on BNC offer significant opportunities as alternatives to synthetic or mineral counterparts. BNC aerogels are proposed for diverse applications, ranging from sensors to medical devices, as well as thermal and electroactive systems. Due to the fibrous nanostructure of BNC and the micro-porosity of BNC aerogels, these materials enable the creation of tailored and specialized designs. Herein, a comprehensive review of BNC-based aerogels, their attributes, hierarchical, and multiscale features are provided. Their potential across various disciplines is highlighted, emphasizing their biocompatibility and suitability for physical and chemical modification. BNC aerogels are shown as feasible options to advance material science and foster sustainable solutions through biotechnology.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.065
GPT teacher head0.338
Teacher spread0.272 · 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 teacher head, not a consensus.

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

Citations40
Published2024
Admission routes1
Has abstractyes

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