Nanocellulose-based functional materials for advanced energy and sensor applications
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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".