A New Perspective to Tribocharging: Could Tribocharging Lead to the Development of a Non-Destructive Approach for Process Monitoring and Quality Control of Powders?
Bibliographic record
Abstract
This study explores a new perspective on triboelectrification that could potentially lead to the development of a non-destructive approach for the rapid characterization of powders. Sieved yellow pea powders at various particle sizes and protein contents were used as a model system for the experimental charge measurements of the triboelectrified powders. A tribocharging model based on the prominent condenser model was combined with a Eulerian-Lagrangian computational fluid dynamics (CFD) model to simulate particle tribocharging in particle-laden flows. Further, an artificial neural network model was developed to predict particle-wall collision numbers based on a database obtained through CFD simulations. The tribocharging and CFD models were coupled with the experimental tribocharging data to estimate the contact potential difference of powders, which is a function of contact surfaces' work functions and depends on the chemical composition of powders. The experimentally measured charge-to-mass ratios were linearly related to the calculated contact potential differences for samples with different protein contents, indicating a potential approach for the chemical characterization of powders.
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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 | 0.001 | 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".