Biosorption of Trivalent Chromium on the Brown Seaweed Biomass
Bibliographic record
Abstract
Biosorption has attracted attention as a cost-effective means for the treatment of metal-bearing wastewater. However, the mechanism of metal binding is not clearly understood, and consequently, modeling of the biosorption performance is still raising debates. In this study, the biosorption of trivalent chromium was investigated with protonated brown alga Ecklonia biomass as a model system. Titration of the biomass revealed that it contains at least three types of functional groups. The Fourier transform infrared spectrometry showed that the carboxyl group was the chromium-binding site within the pH range (pH 1-5) used in this study, where chromium does not precipitate. The pK value and the number of carboxyl groups were estimated to be 4.6 +/- 0.1 and 2.2 +/- 0.1 mmol/g, respectively. The equilibrium sorption isotherms determined at different solution pH indicated that the uptake of chromium increased significantly with increasing pH. A model for the description of chromium biosorption was developed incorporating the hydrolysis reactions that chromium undergoes in the aquatic phase. The model was able to predict the equilibrium sorption experimental data at different pH values and chromium concentrations. In addition, the speciation of the binding site as a function of the solution pH was predicted using the model in order to visualize the distribution of chromium ionic species on the binding site.
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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".