Synergizing date palm seeds-derived oxidized activated carbon: Sustainable innovation for enhanced water retention, efficient wastewater treatment, and synthetic dye removal
Bibliographic record
Abstract
Herein, this study explores the efficacy of oxidized activated carbon from date palm seeds (OACDS) as a multifaceted solution for sustainable wastewater treatment and water retention improvement.Through oxidation synthesis, OACDS demonstrates exceptional capability in adsorbing contaminants from water sources, offering a promising eco-friendly option for environmental remediation.Batch adsorption experiments highlight the rapid and efficient removal of Rh-6 G dyes, achieving an impressive 88.06% adsorption rate under optimized conditions of pH 6 and a 70-minute contact time.Moreover, OACDS exhibits remarkable adsorption capacities, reaching 91.3% at a 60 mg adsorbent dose and 94.23% at 55 • C. Kinetic and adsorption analyses align with PSO and Langmuir models, indicating chemisorption and mono-layered adsorption of Rh-6 G on OACDS.Thermodynamic evaluation suggests the process's spontaneity and endothermic nature.The regeneration experiment revealed a notable 10.9% decrease in the adsorption capacity of OACDS towards Rh-6 G after five cycles.These findings substantiate the reusability and cost-effectiveness of OACDS, underscoring its potential economic advantages.Beyond wastewater treatment, OACDS showcases notable water retention properties, promising for agricultural applications.Its integration into clay and sandy soil enhances water retention capacities, with the clay soil-OACDS mixture displaying a peak of 16.8 mL.The material's rough and porous surface positively impacts water retention in crops, benefiting agricultural yields.Comprehensive characterization analyses using SEM, FTIR, and XRD support OACDS's effectiveness in adsorption, highlighting its amorphous structure and suitability for environmental applications.This study positions OACDS as a comprehensive solution addressing wastewater treatment and water conservation challenges, encouraging further exploration of agricultural byproducts for sustainable environmental solutions.
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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".