Enhancement of the Stability of Biosorbents for Metal-Ion Adsorption
Bibliographic record
Abstract
Biosorbents have demonstrated great potential in the treatment of metal-containing wastewater. However, one of the bottleneck issues of using biosorbents is that amounts of organic carbon release from biosorbents into water. This seriously limits the application of biosorption technology in treating wastewater. In this work, a novel methodology was developed to greatly reduce the organic carbon release and enhance the stability of biosorbents by using barley straw as the model biosorbent material and nickel as the model metal ion. The raw barley straw was first made into cylindrical pellets, which were coated with a sodium alginate (SA) and poly(vinyl alcohol) (PVA) membrane. The coating conditions including the ratio of SA to PVA, glutaraldehyde (GA) dose, concentration of CaCl 2 solution, and cross-linking time were optimized by L 9 (3 4 ) orthogonal array design. The pellets coated at the optimal conditions (1:1 mass ratio of SA to PVA, 1.0 mL of GA, 8% CaCl 2, and 20 min of cross-linking time) were then applied for nickel adsorption. The effects of the solution pH and ionic strength on the adsorption equilibrium and desorption of adsorbed nickel ions were investigated. Scanning electron microscopy and synchrotron X-ray fluorescence spectroscopy were used to locate the adsorption sites on the coated pellets. The results demonstrated that organic carbon release of the coated pellets was significantly reduced to 3.8–9.7 mg/g of dry barley straw pellets in the nickel adsorption process, while that of the raw barley straw particles was 44 mg/g. The nickel uptake increased to 25.6 mg/g, higher than that of the raw barley straw particles.
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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) | 0.001 | 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 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".