Nanocellulose-based functional materials for advanced energy and sensor applications
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.
Bibliographic record
Abstract
Advanced energy and sensor devices with novel applications (e.g., mobile equipment, electric vehicles, and medical-healthcare systems) are one of the important foundations of modern intelligent life. However, there are still some scientific issues that seriously hinder the further development of devices, including unsustainability, high material cost, complex fabrication process, safety issues, and unsatisfactory performance. Nanocellulose has aroused tremendous attention in recent decades, because of its abundant resources, renewability, degradability, low-cost, and unique physical/chemical properties. These merits make nanocellulose as matrix materials to fabricate advanced functional composites for use in energy-related fields extremely competitive. Here, we comprehensively discuss the recent progress of nanocellulose for emerging energy storage/harvesting and sensor applications. The preparation methodologies of nanocellulose combined with conductive materials are firstly highlighted, including carbon materials, conductive polymers, metal/metal oxide nanoparticles, metal-organic frameworks (MOFs), and covalent organic frameworks (COFs). We then focus on the nanocellulose-based advanced materials for the application in the areas of supercapacitors, lithium-ion batteries, solar cells, triboelectric nanogenerators, moisture-enabled electric generators, and sensors. Lastly, the future research directions of nanocellulose-based functional materials in energy-related devices are presented.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it