Synthesis and metal ion uptake studies of chelating polyurethane resin containing donor atoms: Experimental optimization and temperature studies
Bibliographic record
Abstract
Abstract Novel polyurethane (PU) bearing metal binding sites was synthesized by poly‐condensation. Synthesized polyurethane was characterized by using Fourier transformation IR spectrometry (FTIR), nuclear magnetic resonance (1H‐NMR), and thermo‐gravimetric analysis (TGA) techniques. The structural morphology of polyurethane was analyzed with scanning electron microscopy (SEM) and the polymer was used as an adsorbent for metal extraction using batch adsorption studies in aqueous media. Upon observation, it was found that maximum adsorption was obtained at pH = 6 with the adsorbent dose of 20 mg/L at 60 min of contact time for 100 mg/L of Pb(II) and Cu(II) as initial metal ion concentration. In comparison with 2‐parameter and 3‐parameter non‐linear isotherm modelling, Redlich‐Peterson adsorption model (3‐P) fits well supporting Langmuir (2‐P) compared with other sorption isotherms. From the Langmuir isotherm, maximum monolayer adsorption capacity of 496 mg/g and 481.8 mg/g for Pb(II) and Cu(II) ions were obtained, respectively. From the pseudo‐second order equation, the R2 values of Pb(II) and Cu(II) were found to be 0.9984 and 0.9958. Based on tan, the exothermic nature of adsorption is evidenced. PU was found to be stable after 5 cycles with 0.1 N H2SO4, suggesting that the synthesized polyurethane resin was chemically stable and could act as a potential adsorbent for heavy metal extractions in the aqueous media.
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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".