(Invited) John B. Goodenough's Centenarian : Success Story of LiFePO<sub>4</sub> (LFP) As Cathode Material for Rechargeable Lithium Batteries
Bibliographic record
Abstract
Lithium-ion and solid-state battery (SSB) are now playing a central role in consumer electronic, energy storage and electric vehicles thanks to their excellent cycle life and high energy density. One of the key components that have paved the way for this success story in the past 27 years is LiFePO4 (LFP) which has served as a lithium-ion host structure for the cathode electrode. Today only LFP is used in both commercial Li-ion and SSB batteries, due their safety, low cost (cobalt free), fast charge and discharge, and over 20 years of calendar life. In this presentation, we will show the progress of the physical chemistry of the olivine compounds since the pioneering work of Prof. John B. Goodenough. This major improvement has positioned LiFePO4 as the active cathode element of a new generation of Li-ion batteries from cell to pack, hence making a breakthrough in the technology of energy storage and electric transportation. This achievement is the fruit of about 27 years of intensive research in the electrochemical community during which chemists, electrochemists, physicists, and engineers added their efforts to understand the properties of the material, to overcome the obstacles that were met on the way, and finally to reach the state of the art enabling its ubiquitous use in technology today and in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".