Thermodynamic analysis of the effect of palm stearin on fully hydrogenated soybean oil coatings in granular micronutrient premixes
Bibliographic record
Abstract
This study examined the phase behaviour and temperature-dependent viscosity of palm stearin and fully hydrogenated soybean oil (FHSBO) mixtures to understand the stability of FHSBO coatings on micronutrient premixes. The Hildebrand equation showed that palm stearin/FHSBO mixtures form liquid solutions, while DSC data indicated partial immiscibility in the solid phase. The melting temperatures and enthalpies of fusion are composition dependent, ranging from 67 °C and 138 J/g for 100 % FHSBO to 13 °C and 71 J/g for 100 % palm stearin. The Arrhenius model provided an excellent fit for the viscosities of the binary mixtures above their melting points, with activation energies of 26–27 kJ/mol and pre-exponential factors of 131–207 MPa.s. At the processing conditions of bouillon cubes, a binary fat coating can form on premix granules and contain approximately 70 % palm stearin at equilibrium. At processing temperatures of 30–60 °C, the premix fat coating contains 68–84 % liquid. • Palm stearin and fully hydrogenated soybean oil (FHSBO) form ideal liquid solutions. • Viscosities of palm stearin and FHSBO mixtures conform to the Arrhenius model. • FHSBO coatings must remain in a solid state to resist deformation and removal during processing.
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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 | 0.000 |
| 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".