Triboelectrostatic Separation for Dry Fractionation of Pulse Flour
Bibliographic record
Abstract
Plant-based protein ingredients are necessary for the food industry to develop new nutritious food products for the growing population. The advent of wet fractionation has commercialized plant protein extraction, but it is unsustainable as it uses ample heat, water, and chemicals. One of the viable options for sustainable protein extraction is tribo-electrostatic separation (TES). This technique is based on tribocharging particles by rubbing or frictional contact and separating the charged particles of different polarities in a high-intensity static electric field. This study elucidates the impact of tribocharging on the separation of yellow pea and faba bean flour under the influence of an electric field in a bench-scale setup. The effect of protein content, moisture content, residence time, and tribocharger wall material was investigated using vibratory, vortex flow, and solid particle dispensing (SPD) methods for the tribocharging experiments. The effect of airflow rate, electric field strength, and wall material was studied for separating tribocharged flour particles in the separator setup. The non-conducting wall materials induced a high positive charge for the flour particles. Lower moisture content increased the chargeability of the flour particles but reducing it below 6% has less impact on chargeability. The SPD (1100 nC/g) method had better chargeability than vibratory (19 nC/g) and vortex flow (7.2 nC/g) tribocharging methods. Separation results using SPD indicates that plate voltage and wall material play a significant role in protein enrichment. However, the airflow rate in the turbulent regime does not benefit separation. Low electric field strength resulted in high protein purification with low protein yield. A maximum protein content of 58% was obtained after separating the protein-starch mixture having an initial protein content of 28.4%. The results indicate that TES has the potential to be utilized by food industries to make sustainable plant protein ingredients for manufacturing new food products for consumers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 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".