Co-valorization of shrimp and tropical wood waste to high-value composites: Fabrication, characterization, and herbicide adsorption studies
Bibliographic record
Abstract
The current work underscores the potential of using copious waste streams to fabricate high value composite adsorbents which are then used in environmental remediation. The study investigates the fabrication of nitrogen-enriched adsorbents by the co-valorization of shrimp hydrochar, shrimp chitin, and shrimp shells with a waste wood feedstock, greenheart, by a facile, phosphoric acid activation process. These materials were characterized and subsequently deployed to remove 2,4-dichlorophenoxy acetic acid from model solutions at 50 ppm concentration at pH 7. Shrimp shell and shrimp hydrochar composites were typically mesoporous but shrimp chitin composites were microporous. Specific surface area ranged from 1224 m 2 /g to 1974 m 2 /g. Surface nitrogen peaked at 2.94 at% with amine, amide and imide function predominating. The largest specific surface area and greatest nitrogen content of composites was more than 56 % and 5 times greater than the pristine greenheart adsorbent. Nitrogen functionality was uniformly distributed on the composite surface implying that there was homogeneous combination of the co-valorized feedstocks. The shrimp-chitin-greenheart composite was most efficient at removing 2,4-D with a maximum adsorption capacity of 101 mg/g. Maximum adsorption capacities of composites were most strongly correlated with amine groups (0.86), total nitrogen (0.88), total surface nitrogen density (0.90) and specific surface area (0.87), demonstrating that both surface area and nitrogen functionality played a pivotal role in the adsorption. The Freundlich isotherm model best described the adsorption process, implying the heterogeneous nature of adsorption sites. Adsorption was spontaneous and entropically favored and adsorption enthalpies ranged from −12 kJ/mol to −17 kJ/mol indicating that physisorption interactions dominated the adsorption process. These composites, with demonstrated efficacy in removing 2,4-D, are promising environmental remediation materials.
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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) | 0.001 | 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".