One-Step Acid-Induced Confined Conversion of Highly Oriented and Well-Defined Graphitized Cellulose Nanocrystals: Potential Advanced Energy Materials
Bibliographic record
Abstract
Graphitic carbon nanomaterials are highly attractive for numerous applications due to their unique mechanical, electronic, thermal, and optical properties. Although there are many methods to produce graphite such as extraction from natural graphite mines or synthesis of graphite from carbonaceous compounds, the existing problems include quite complex and unsafe preparation processes, large energy consumption, and high cost, which fail to satisfy the requirements of sustainable development for the environment and economy. Here, we report an efficient and controllable strategy to prepare highly oriented and ordered graphitized cellulose nanocrystal (GCNC) with well-defined sizes and shapes at low temperature and atmospheric pressure by hydrolyzing the disordered regions of microcrystalline cellulose with sulfuric acid and further dehydrating and carbonizing its surface. The as-synthesized GCNC has a high degree of graphitization along with a yield of ∼24%, and the high-quality graphitic carbon layers enable the GCNC to own electrical conductivity and excellent electrochemical performance. Moreover, the cellulose characteristics are well retained, which endows the GCNC with excellent solution dispersibility. Our study provides a new avenue to synthesize graphitic carbon and redefines conductive nanocellulose, with anticipation that holds promising application prospects in the fields of hybrid composites, energy storage, and electronics.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".