3D-printed grid LiFePO₄@ACB cathode for Li-ion batteries with high energy and areal density
Bibliographic record
Abstract
Despite great efforts that have been devoted to high-performance lithium-ion batteries, conventional electrode fabrication methods still face the challenge of low areal capacity and limited energy density required for electric vehicle applications. In this work, LiFePO4@ACB (acetylene carbon black), denoted as ACB cathodes, were designed with a porous structure utilizing direct ink writing 3D-printing (3D) technology for enhanced areal specific capacity and energy density in lithium-ion batteries (LIBs). The 3D-printed composite cathodes consist of closely packed and well-aligned LiFePO4@ACB filaments. ACB particles are wrapped on the outer surface of olivine LiFePO4, which contributes to the formation of hierarchical and abundant open pores. The 3D-printed LiFePO4@ACB cathodes exhibited a higher capacity, enhanced cycling life, and high areal specific capacity compared to those of conventional ink-cast thick LiFePO4@ACB cathodes. Grid-patterned 3D-printed LiFePO4@ACB (12 layers) exhibited an enhanced areal specific capacity of 6.7795 mAh cm−2 and high specific energy density of 634.372 Wh kg−1 at a specific power density of 59.95 W kg−1, due to its short ion transport pathways and enhanced mechanical strength. This work demonstrates that the direct ink writing strategy enables the fabrication of grid-patterned electrodes with high areal and energy densities, offering significant potential for the future development of high-performance lithium-ion batteries.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".