Ultrasound assisted wet media milling synthesis of nanofiber-cage LiFePO4/C
Bibliographic record
Abstract
To meet the objectives of the Intergovernmental Panel on Climate Change nations are adopting policies to encourage consumers to purchase electric vehicles. Electrification of the automobile industry reduces greenhouse gases but active metals for the cathode—LiCoO2 and LiNiO2—are toxic and represent an environmental challenge at the end of their lifetime. LiFePO4 (LFP) is an attractive alternative that is non-toxic, thermally stable, and durable but with a moderate theoretical capacity and a low electrical conductivity. Commercial technologies to synthesize LFP are energy-intensive, produce waste that incurs cost, and involve multiple process steps. Here we synthesize LFP precursor with lignin and cellulose in a sonicated grinding chamber of a wet media mill. This approach represents a paradigm shift that introduces mechanochemistry as a motive force to react iron oxalate and lithium hydrogen phosphate at ambient temperature. Ultrasound-assisted wet media milling increases carbon dispersion and reduces the particle size simultaneously. The ultrasound is generated by a 20 kHz,500 W automatic tuning ultrasound probe. The maximum discharge rate of the LFP synthesized this way was achieved with cellulose as a carbon source, after 9 h milling, at 70% ultrasound amplitude. After 2.5 h of milling, the particle size remained constant but the crystal size continued to drop and reached 29 nm. Glucose created plate-like particles, and cellulose and lignin produced spindle-shaped particles. Long mill times and high ultrasound amplitude generate smoother particle surfaces and the powder densifies after a spray drying step.
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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) | 0.002 | 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".