Heavy metal adsorption from phosphoric acid 29% P <sub>2</sub> O <sub>5</sub> with Amberlite IRC200 Na resin
Bibliographic record
Abstract
Phosphoric acid plays a major role in our day-to-day life since it is used among other applications to produce fertilizers. Nevertheless, its production from phosphate rock often involves the presence of heavy metals. This study investigates the adsorption of cadmium, zinc, nickel, chromium, arsenic, and vanadium from phosphoric acid on a strongly acidic cation exchange resin, Amberlite IRC200 Na. Amberlite IRC200 Na is an easily regenerated resin with a high selectivity toward divalent cations (Cd, Zn, and Ni) removing 94% of them, while the formation of anionic or neutral complexes with phosphoric acid hinders the adsorption of Cr (70%), As and V (below 20%) on the resin. To model the resin adsorption in a continuous flow, the kinetics and isotherm have been studied. The fast adsorption of metals follows a pseudo-second order kinetic, where the rate is equivalent to the square of the number of remaining adsorption sites. The adsorption isotherm follows a Langmuir equilibrium model, which indicates that the ion exchange can be approximated to a homogeneous adsorption. The kinetic and isotherm results show that the resin is not selective between divalent ions.
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) | 0.001 | 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) | 0.002 | 0.001 |
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".