Development and characterization of pine bark with enhanced capacity for uptaking Cr(III) from aqueous solutions
Bibliographic record
Abstract
Abstract This work aims for the development and characterization of a new biosorbent with enhanced capacity for recovery of Cr(III) from aqueous solutions. The adsorbent was developed by chemical modification of pine bark (Pinus pinaster) (PB). Initially, several chemical agents (HCl, H2SO4, HNO3, H3PO4, citric acid, acetic acid, NaOH) were tested, but the best results were obtained with NaOH. The adsorbent developed was characterized regarding chemical composition and thermal behaviour before and after chemical modification. Scanning electron microscopy coupled with energy dispersive spectroscopy (SEM‐EDS) showed the morphology and the most relevant elements on the surface before and after adsorption of Cr(III). Fourier transform infrared spectroscopy (FTIR) revealed the important role of carboxylate groups in Cr(III) uptake. The adsorption process was studied under different conditions, namely for testing the particle size (in the ranges from 0.088 to 0.149 and 0.250 to 0.595 mm). The equilibrium isotherms showed that Cr(III) adsorption is strongly dependent on particle size. According to the Langmuir model, the maximum adsorption capacity of Cr(III) increased from 17.15 to 31.40 mg/g as the particle range decreased. The experimental Cr(III) removal efficiencies as a function of the adsorbent dosage were well predicted by the mass balance equation for the batch adsorption process coupled with the equilibrium isotherm.
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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".