Biomass-Derived Hard Carbon Anodes Processed with Deep Eutectic Solvents for High-Performance Sodium-Ion Batteries
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Sodium-ion batteries (SIBs) are increasingly acknowledged as a promising alternative for large-scale energy storage applications, attributable to the abundant natural availability, widespread geographical distribution, and economic viability of sodium resources. Hard carbon is recognized as one of the leading options for anode materials in sodium-ion batteries (SIBs). It presents several advantages, including a high reversible specific capacity and the availability of abundant precursor sources. The synthesis of hard carbon anodes frequently necessitates the utilization of acids or alkalis, which presents considerable environmental challenges and results in substantial costs associated with waste liquid treatment. Consequently, there is an imperative to establish a cost-effective and environmentally sustainable modification method for hard carbon materials. Herein, we develop a universal reciprocal biomass processing method to prepare a series of high-performance hard carbon anodes derived from biomass. The utilization of biomass-based deep eutectic solvents (DES) leads to the disruption of intramolecular and intermolecular hydrogen bonds within cellulose in biomass feedstock, resulting in increased disorder and expanded interlayer spacings of hard carbon during the pyrolysis process. The optimal hard carbon anode derived from macadamia nut shells (MNSs) exhibits enhanced sodium-ion transport and storage capabilities treated by DES, featuring a high reversible capacity of 297.07 mAh g –1 at 20 mA g –1 and good rate performance. Other biomass resources, such as bamboo, coconut, and pine, highlight the versatility of the proposed reciprocal biomass processing method for synthesizing high-performance hard carbon anodes. This study presents a universal and green method to process biomass resources for synthesizing high-performance hard carbon anode materials.
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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) | 0.001 | 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".