Cross flow microfiltration of starch nanocrystal suspensions
Bibliographic record
Abstract
Abstract Starch nanocrystals (SNC), because of their interesting barrier properties, are of great interest for the formulation of biodegradable nanocomposites. Conventional production process by acid hydrolysis results in a very low yield and a heterogeneous suspension which limits the possibility of scaling up. A continuous process that includes a microfiltration step for isolating starch nanocrystals during the hydrolysis step may be a solution to increase SNC production yield. The main objectives of this study were, first, to quantify the transmission of SNC through membranes under different operating conditions and then to identify the preponderant fouling phenomenon by using simple linear fouling models. Filtration tests were run using a lab‐scale microfiltration unit. It was equipped with ceramic membranes with a nominal pore sizes of 1.4 µm and 0.8 µm. Suspensions of nanocrystals at 0.01, 0.02 and 0.04 % (w/w) were filtered in a concentration mode under different transmembrane pressures (50, 100, and 150 kPa). Particle size analysis by Dynamic Light Scattering (DLS) showed that microfiltration is an effective tool for suspension fractionation. Mean diameter of SNC particles was reduced to less than 300 nm in permeate. The transmission through the membrane depended on operating conditions and reached 37 %. The permeate flow rate decreased with time to reach a steady value. The decrease rate was higher when the feed concentration increased. Constant pressure filtration models were used to identify the preponderant fouling phenomenon during filtration and highlighted that the decrease in permeate flow was mainly due to a cake formation on membrane surface.
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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 | 0.001 |
| 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 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".