Matching the Functionality of Single‐Cell Algal Oils with Different Molecular Compositions
Bibliographic record
Abstract
Abstract The fatty acid composition, triglyceride composition, melting/crystallization profiles, crystallization kinetics, X‐ray diffraction patterns, microstructure and mechanical properties of a pair of algal oils were studied to elucidate structural reasons for the similarity in melting and mechanical properties. Oil A is a predominantly saturated fat, rich in capric, myristic and palmitic acids, composed mostly of trisaturated triglycerides while Oil B contains predominantly palmitic and oleic acids in triglycerides such as POP/OPP and OOP/OPO. The DSC thermogram of Oil A shows similar peak melting temperatures to that of Oil B with Oil B exhibiting a few additional peaks. Both oils exhibit identical SFC‐temperature profiles. Polarized light microscopy revealed a needle‐like morphology for both Oil A and Oil B, with an average length of approximately 3.5–4.0 μm. The similar morphology of the crystals was attributed to a similar polymorphic form (β’) present in both. The fractal dimensions for the distribution of crystalline material within the fat crystal networks of both oils were also similar. The identical melting and mechanical properties of Oil A and Oil B were thus be attributed, respectively, to the presence of different triglycerides (in approximately equal proportions) with similar melting points and the assembly of these triglycerides into crystals of identical shape and size, which are in turn assembled into a network with identical crystal mass distributions. This work suggests that the mechanical and thermal properties of oils with vastly different molecular compositions can be matched by targetting specific TAG combinations which yield similar melting behavior, microstructure and mechanical response.
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) | 0.000 | 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".