Magnetite (Fe3O4) and nickel ferrite (NiFe2O4) zeta potential measurements at high temperature: Part II – Results, study of the influence of temperature, boron concentration and lithium concentration on the zeta potential
Bibliographic record
Abstract
Predicting the deposition of typical corrosion products (CPs) particles formed in the primary system of pressurized water reactors (PWRs) is important for system integrity and the radioprotection of nuclear workers. Such corrosion products foul heat transfer surfaces, promote localized corrosion and, when made radioactive in the reactor core, transport throughout the coolant system and give rise to radiation fields around components. Accurately predicting CPs’ propensity to transport and deposit around the system entails knowing their zeta potentials, quantities that until now have been unavailable. The zeta potentials of magnetite and nickel ferrite particles between 20 °C and 240 °C have been measured in the chemical conditions representative of an operating cycle of the primary system of PWRs. The measurements were performed via the streaming potential method as described in a previous paper – Part I. The measured values increased with temperature but decreased with increasing concentrations of boron and lithium. They are suitable for predicting radiation field growth around components of a typical PWR system.
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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".