Ultrasensitive Physical, Bio, and Chemical Sensors Derived from 1‐, 2‐, and 3‐D Nanocellulosic Materials
Bibliographic record
Abstract
Sensors are of increasing interest since they can be applied to daily life in different areas from various industrial sectors. As a natural nanomaterial, nanocellulose plays a vital role in the development of novel sensors, particularly in the context of constructing multidimensional architectures. This review summarizes the utilization of nanocellulose including cellulose nanofibers, cellulose nanocrystals, and bacterial cellulose for sensor design, mainly focusing on the influence of nanocellulose on the sensing performance of these sensors. Special attention is paid to nanocellulose in different forms (1D, 2D, and 3D) to highlight the impact of nanocellulose constructed structures. The aim is to provide a critical review on the most recent progress (especially after 2017) related to nanocellulose-containing sensors, since there are significantly increasing research activities in this area. Moreover, the outlook for the development of nanocellulose-containing sensors is also provided at the end of this work.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".