Sequestration of Sulfate Anions from Groundwater by Biopolymer-Metal Composite Materials
Bibliographic record
Abstract
Binary (Chitosan-Cu(II), CCu) and Ternary (Chitosan-Alginate-Cu(II), CACu) composite materials were synthesized at variable composition: CCu (1:1), CACu1 (1:1:1), CACu2 (1:2:1) and CACu3 (2:1:1). Characterization was carried out via spectroscopic (FTIR, solids C-13 NMR, XPS and Raman), thermal (differential scanning calorimetry (DSC) and TGA), XRD, point of zero charge and solvent swelling techniques. The materials’ characterization confirmed the successful preparation of the polymer-based composites, along with their variable physico-chemical and adsorption properties. Sulfate anion (sodium sulfate) adsorption from aqueous solution was demonstrated using C and CACu1 at pH 6.8 and 295 K, where the monolayer adsorption capacity (Qm) values were 288.1 and 371.4 mg/g, respectively, where the Sips isotherm model provided the “best-fit” for the adsorption data. Single-point sorption study on three types of groundwater samples (wells 1, 2 and 3) with variable sulfate concentration and matrix composition in the presence of composite materials reveal that CACu3 exhibited greater uptake of sulfate (Qe = 81.5 mg/g; 11.5% removal) from Well-1 and CACu2 showed the lowest sulfate uptake (Qe of 15.7 mg/g; 0.865% removal) from Well-3. Generally, for all groundwater samples, the binary composite material (CCu) exhibited attenuated sorption and removal efficiency relative to the ternary composite materials (CACu).
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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".