Adsorption of Phosphate Dianions by Hybrid Inorganic–Biopolymer Polyelectrolyte Complexes: Experimental and Computational Studies
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Bibliographic record
Abstract
Hybrid inorganic–biopolymer polyelectrolyte complexes (HIB-PECs) consisting of chitosan, alginate, and an aluminum-based cross-linking agent were prepared at variable chitosan and alginate mass ratios. IR spectral results indicate that chitosan and alginate undergo PEC formation according to the spectral shifts of the −NH3+ and COO– groups for chitosan and alginate, respectively. Evidence of Al–O–R (where R = alkyl group) and Al–OH bonds are supported by XPS, PXRD, and NMR spectra. Computational studies provide additional support that aluminum undergoes cross-linking with the carboxylate groups of alginate of the PEC. SEM and pHpzc results provide support of variable product morphology and surface charge for the HIB-PECs over the pristine biopolymers. Equilibrium adsorption studies of the hydrogen phosphate dianion at pH 8.5 reveal that chitosan with alginate at a 1:1 (w/w) ratio afforded an HIB-PEC with an optimal adsorption capacity (Qm = 152 ± 44 mg/g). The Sips isotherm model accounts for the adsorption data, where the uptake parameters indicate a heterogeneous adsorbent surface with physisorption uptake properties. Increased temperatures led to reduced uptake capacity of the HIB-PECs, where competitor anion species had no apparent effects. Computational studies revealed that the −OH sites of the aluminum complex in the HIB-PECs serve as the preferred active site for phosphate anion uptake. Regeneration of the HIB-PECs over four adsorption–desorption cycles did not show any attenuation of the adsorption capacity. This study presents a first example of the application of HIB-PECs for the adsorption of phosphate species in aqueous solution, where the computational and experimental studies provided complementary support that the −OH sites of the aluminum complex serve as the key active adsorption site for the phosphate anion species.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it