Dephosphorization Kinetics of Bloated Metal Droplets Reacting with Basic Slag Containing TiO<sub>2</sub>
Bibliographic record
Abstract
Although the dephosphorization kinetics of bloated metal droplets reacting with oxidizing slag have been studied in detail in the authors’ laboratory, the mechanism of reaction for slags in a basicity range typical of steelmaking, has been sparsely reported. The current study employed a high temperature furnace equipped with X-ray fluoroscopy to observe the bloating behavior of droplets and tracked dephosphorization kinetics by quenching and analyzing droplets after different reaction times. The mechanism of reaction between bloated metal droplets and slag was studied at 1923 K for slags with basicity range C/S=2.56. The rate and extent of dephosphorization was found to be greater in CMS slags compared to CAS slags due to the faster mass transport and a larger thermodynamic driving force. The kinetic analysis showed that the reaction proceeded in two distinct stages, a fast initial stage followed by a slower stage. The km during the first stage of dephosphorization was at least 8 times higher than that during the second stage. This is proposed to be due to a higher internal CO generation rate during the initial stage which increases the rate of surface renewal. The effect of TiO2 on dephosphorization kinetics was also investigated in terms of thermodynamic driving force.
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) | 0.001 | 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".