Ultrasound assisted wet stirred media mill of high concentration LiFePO<sub>4</sub> and catalysts
Bibliographic record
Abstract
Abstract Wet media mills grind solids to the nanometric size and the performance of the mills depends on solids loading, particle morphology, surfactant concentration, and material characteristics. As the particle size decreases, they tend to form clusters that reduce the grinding efficiency. Ultrasound deagglomerates these clusters thereby increasing efficiency but, surprisingly, it can operate at higher solids concentrations. We processed suspensions of (LFP) with a yttria stabilized zirconia media with a size from 0.3 mm to 0.4 mm, and surfactant‐to‐LFP mass ratio 0.008. The combined method ground the particle size from 35 m down to 0.2 m in 90 min with a throughput of 0.68 /h. We also tested the improved wet media milling on two catalysts: ( and vanadyl pyrophosphate (VPP) precursor) to confirm the trends. We adopt a model for the steady state and repeatable micronizing process with ultrasonic assistance. According to TEM imaging the catalyst primary particles (20 nm) are much smaller than the agglomerated ones measured by laser diffraction (470 nm). VPP precursor slurry is normally an unstable suspension that is hard to mill. It formed agglomerates with recrystallized silica and VPP flocculation. The ultrasound‐assisted wet milling technique produced nanometer scale particles (180 nm), which is otherwise impossible. We developed and updated the steady state micronizing process with ultrasonic assistance using a simplified population balance model. The model accounts for of the variance in the experimental data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".