Bibliographic record
Abstract
Olivine structured LiFePO4 (LFP) has attracted significant attention as a promising cathode material for LIBs. An advantage of using LFP compared to other cathode chemistries (i.e., NMC, LCO & LNO) is that iron is naturally abundant, making LFP low in cost, environmental impact, and toxicity.1,2 Though LFP has high thermal stability and excellent cycle life, it suffers from poor electronic conductivity and low Li+ ion diffusion which have been improved by carbon coating and reducing particle sizes to nanometres (<1 µm), respectively.3 To improve the energy density of LFP electrodes we have developed ultra-dense LFP electrodes with densities in excess of 2.7 g/cm3 to be achieved with high loadings (15 mg/cm3) and with electrode formulations containing 90% LFP active material. This corresponds to an electrode porosity of only 14% and, as shown in Figure 1(a), represents a volumetric energy density increase of about 27% compared to conventional LFP. Moreover, as shown in Figure 1(b), ultra-high density LFP electrodes were found to have higher coulombic efficiency than conventional calendered LFP electrodes with lower densities. Voltage polarization was also found to be reduced. Lastly, Figure 1(c) shows an approximate 4 times reduction in charge transfer resistance for an ultra-dense LFP electrode with respect to a conventional calendered LFP electrode. The ability to make highly dense LFP electrodes could have profound impacts, allowing for Li-ion cells to be made with low costs and low environmental impact LFP, while achieving volumetric energy densities approaching that of Li-ion cells employing layered oxide cathodes. References X. Ren et al., J. Electrochem. Soc., 167, 130523 (2020). L. Wen, X. Hu, H. Luo, F. Li, and H. Cheng, Particuology, 22, 24–29 (2015). L.-X. Yuan et al., Energy Environ. Sci., 4, 269–284 (2011). Figure 1
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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 | 0.000 | 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".