Research on extraction of Cr( <scp>III</scp> ) by <scp>D2EHPA</scp> / <i>n</i> ‐octanol/sulphonated kerosene
Bibliographic record
Abstract
Abstract Chromium is an important industrial raw material. As far as China is concerned, chromium is in great demand, which is dependent on imports and large emissions. So recovery of chromium has important economic and environmental protection value. The extraction and back extraction of Cr(III) from solution by the extractant diisooctyl phosphate (di‐2‐ethylhexylphosphoric acid [D2EHPA]) with n ‐octanol as assistant and sulphonated kerosene as diluent was studied. The effects of saponification rate, phase ratio, temperature, condensation of extract, aging of extractant, and pH of aqueous phase on extraction equilibrium were discussed, and the formulation of extractant was optimized. The coordination number of the extraction reaction under specific conditions was discussed by the saturation capacity method. The extraction reaction kinetics were mathematically characterized by binary linear regression. The advantages and disadvantages of back extraction with H 2 SO 4 or NaOH were compared. At last, a process to realize the recycling of extractant was obtained.
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".