A Prospect for Environmental Remediation of Perchlorate via Cost-Effective Pinus Leaves and Dandelion Flower Powder-based Layer Double Hydride (LDH) Sorbents
Bibliographic record
Abstract
In this study, pine leaves powder (PiP) and dandelion flower powder (DFP) were repurposed to synthesize layered double hydroxides (LDHs) to form a base for sorbents used in perchlorate remediation from wastewater. The effects of the adsorbent dose, pH, thermodynamics, and coexisting ions were evaluated in batch experiments. The results revealed that 0.1 g adsorbent (PiP-LDH and DFP-LDH) removed 97% and 93% of perchlorate contaminants, respectively. In this study, the correlation coefficient of pseudo-second-order was higher than pseudo-first-order for all the LDHs. The kinetic and isotherm studies showed the best uptake of perchlorate in the short time was by PiP-LDH, followed by DFP-LDH (20 min and 40 min, respectively). The calculated and experimental values of adsorption at the equilibrium state also concurred with the pseudo-second-order model. The prepared LDHs were mesoporous. The surface area of PiP-LDH provided more adsorption sites, rendering it more suitable for perchlorate adsorption compared with the other two LDHs. The model suggests Physico-chemical interactions behind the sorption of perchlorate by LDHs. The adsorption was more influenced by anions i.e, PO43− > SO42− > NO3 than monovalent anions due to the increase in the charge radius values. The prepared LDHs could be of great benefit to the environmental remediation of wastewater bodies.
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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".