Deposition of magnetite particles from flowing suspensions under flow‐boiling and single‐phase forced‐convective heat transfer
Bibliographic record
Abstract
Abstract The deposition rate of colloidal magnetite particles was measured under both single‐phase forced‐convective and flow‐boiling conditions. All measurements were made at alkaline pH where both the heat transfer surface and the surface of the magnetite particles appear to be negatively charged. For single‐phase forced convection, the deposition rate constant is lower than the mass transfer coefficient for colloidal particles, and the difference is attributed to the force of repulsion between the negatively charged surfaces of the particle and substrate. The deposition rate measured under flow‐boiling conditions is lower than that reported for the deposition of colloidal particles at neutral pH. The difference is, again, attributed to the force of repulsion between the particle and substrate. Particle removal rates were significantly lower than deposition rates; analysis using the theory of turbulent bursts suggests a removal efficiency of only 10 −9 % for each turbulent burst. The low removal efficiency is consistent with the particle diameter being significantly smaller than the thickness of the laminar sublayer in these tests.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".