Aerogels based on Bacterial Nanocellulose and their Applications
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".