A novel low-cost plant-based adsorbent from Red Oak (Quercus rubra) Acorns for wastewater treatment: Kinetic study on removal of dye from aqueous solution
Bibliographic record
Abstract
Abstract Oak species are a successful plant group that have colonized the world's largest areas of forest. Oak trees are also prevalent in urban green spaces in the United States and Canada. As a result, these trees produced an abundance of acorns each year. In urban areas, these acorns are frequently discarded as solid waste. Alternative uses for this forest/plant waste are highly desirable because they will not only be valorized but will also contribute to the reduction of solid waste. The purpose of this work was to manufacture low-cost activated carbon using Red Oak (Quercus rubra) acorns and utilize it to remove methyl blue colors in aqueous solutions. The results of experiments indicated that prepared carbons were effective at removing pollutants from water. The pH, starting dye concentration, temperature, duration of the adsorption process, and shaking rate all had an effect on the adsorption process. The basic pH system was found to have the most favourable conditions for dye removal after a 3-hour contact time. The starting concentration of adsorbate has a detrimental influence on the removal rate, while the other factors also may have effect. A kinetic analysis revealed for the first 2 hours, the dye adsorption was better characterized by a pseudo-second order kinetic model with an equilibrium concentration (qe) of 0.9756 mg/g and an equilibrium rate constant (k2) of 16.81 g/mg min. Given that Red Oak acorns are largely regarded as solid waste due to their low monitory value and their widespread availability, the resulting carbons are anticipated to be economically feasible for the treatment of wastewater. The study's various findings indicate that this novel material is an excellent biosorbent for dye removal from contaminated waters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".