Separation of Bioactive Peptides by Membrane Processes: Technologies and Devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although many patents reported bioactive peptides with numerous demonstrated bioactivities and potential applications, there exist some limitations to the production of large quantities to satisfy the growing market demands. Indeed, considering that most functional peptides are present in complex matrices containing a large number of hydrolyzed protein fractions, their separation and purification are required. Some advances have been made in the use of conventional pressure-driven processes for the continuous production and separation of peptides, however, most of these patented technologies are not scalable and demonstrate a low selectivity when separating similar sized biomolecules. To improve the separation efficiency, the use of an external electric field during pressure-driven filtration was proposed and patented. However, whatever the claims, the pressure gradient brings about the accumulation of peptides at the nearby membrane surface and affects the membrane transport selectivity. To overcome these drawbacks, a recent patent proposed the simultaneous fractionation of acidic and basic peptides, using a conventional electrodialysis cell, in which some ion exchange membranes are replaced by ultrafiltration ones. The perspectives in the field of peptide separation will be the development of new membrane materials and new equipments such as microfluidic devices to improve selectivity and yield of production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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 it