Thermophysical Properties of a Slurry of Distilled Water and Microencapsulated Phase-Change Materials
Bibliographic record
Abstract
This paper presents experimental measurements of some effective thermophysical properties of slurries consisting of microencapsulated phase change materials (MCPCMs) suspended in distilled water. The related apparatus and procedures are also presented and discussed. The MCPCMs considered here consist of a core of phase-change material (PCM), in this case a substance akin to octadecane, surrounded by a solid shell. The effective density of the slurries was measured using hydrometers. The effective thermal conductivity of the slurries was measured using an in-house designed apparatus. The effective kinematic viscosity of the slurries was measured using a series of glass capillary viscometers. A differential scanning calorimeter (DSC) was used to obtain the effective specific heat, melting and freezing temperatures of the core PCMs, and the latent heat of the slurries. Slurry concentrations between 0% (pure distilled water) and 20% by mass of the MCPCMs were considered in this investigation, at temperatures ranging from 5°C to 65°C. Where possible, the results have been compared to predictions obtained using available analytical expressions with properties of the constitutive materials as inputs.
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 | 0.001 |
| 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) | 0.001 | 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".