Lentil protein concentrate + pectin gels dried with SC-CO2: Influence of protein-polysaccharide interactions on the characteristics of aerogels
Bibliographic record
Abstract
This study investigated the interactions of lentil protein concentrate (LPC) and pectin hydrogels and their influence on the physico-chemical characteristics of aerogels. First, emulsion gels were formed using high-intensity ultrasound (HIUS) and the impact of HIUS nominal power, and concentrations of LPC and pectin on the gelation were evaluated. Then, the emulsion gels were dried using supercritical CO 2 (SC-CO 2 ) and the density, surface area, crystallinity index, microstructure and oil and water absorption capacities of the aerogels formed were evaluated. Overall, there was no significant effect of HIUS power on the viscoelastic behavior of the emulsion gels. The emulsion gels exhibited shear-thinning behavior and had thermo-reversible property. The FT-IR spectra of the aerogels showed predominant β-sheets, responsible for the non-covalent bond formation. The aerogels had semi-crystalline structure, densities of 0.0009–0.003 g/mm 3 and surface area of 2.4–7.6 m 2 /g. The LPC-pectin interactions can be explored to form tailor-made aerogels for bioactive delivery.
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.
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 | 0.000 |
| 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 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".