Mechanism study of metal ion adsorption on porous hydroxyapatite: experiments and modeling
Bibliographic record
Abstract
In this study, low-cost biomaterial hydroxyapatite (N-Hap) prepared from bovine bones was tested for its potential to sorb copper Cu2+ and Fe3+ from aqueous solution and was compared with commercial hydroxyapatite (C-Hap). The structural characterization of N-Hap and C-Hap were carried out by Fourier transform infrared (FTIR) analysis, textural analysis (BET), morphology, and elemental analysis via scanning electron microscopy (SEM-EDX). The results obtained show that SEM images confirmed the formation of porous N-Hap with various morphologies and the average particle size ranges from 50 to 100 nm and the CaP ratio is 1.657. The presence of functional groups on N-Hap surface was confirmed by FTIR analysis. The specific surface areas of N-Hap and C-Hap are found to be 46.87 and 40.98 m2/g, respectively. Adsorption of two metals Cu2+ and Fe3+ was fast, with equilibrium attained within 30 min. Copper ions exhibited the greatest adsorption on both adsorbents because of their size and pH conditions. Metallic ion removal efficiency was favored at a slightly acidic solution pH and low temperature. The equilibrium and kinetic data were found to fit well the Langmuir model and the pseudo second order model with intraparticle diffusion. Thermodynamic parameters (ΔHads < 0 and ΔGads < 0) involved the exothermic, spontaneous, and physical adsorption process. The adsorption interaction of Cu2+ and Fe3+ with Hap surface was investigated by theoretical density functional theory (DFT) calculations. Therefore, the porous hydroxyapatite N-Hap displays potential as a new biocompatible adsorbent, and its use seems to be an interesting solution for the treatment of industrial wastewater.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".