Extraction of s-block metals by nano-baskets of calix[4]crown-3
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Three kinds of nano-baskets including 1,2-alternate and cone conformers of di-ionizable p-tert-butylcalix[4]arene-1,2-crown-3 and the cone conformer of p-tert-butylcalix[4]arene-1,2-thiacrown-3 were synthesized and the competitive solvent extractions of alkali and alkaline earth metal cations were studied. The novelty of this study is including three binding units of the calixarene’s bowl, the crown ether’s ring, and electron-donor ionizable moieties in a unique scaffold, in which their sizes were selected based upon their complexation ability to show equal binding tendency towards the cations. The objective of this work is to assess the extraction efficiency, selectivity, and pH 1/2 of such complexes. The results of solvent extraction experiments indicated that these compounds were effective extractants of alkali and alkaline earth metal cations. Their selectivities were greatly influenced by the acidity of the solution and the conformations of the calixcrown. One conformer was highly selective to Na + and the other to Ba 2+ in acidic and basic solutions, respectively.
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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) | 0.000 | 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".