Fast adsorption of methylene blue and crystal violet from aqueous solution by sustainable biosorbent (pine needle of <scp> <i>Pinus nigra</i> </scp> Arn.): Kinetics, equilibrium, and thermodynamics study
Bibliographic record
Abstract
Abstract Methylene blue (MB) and crystal violet (CV) dyes, which are toxic to the ecosystem, were removed by Pinus nigra Arn. tree ( Pn A.) waste needle powders (Ptwnd), which is a natural, easily available, and cheap adsorbent. The physicochemical composition of Ptwnd was carried out by Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) and energy dispersive X‐ray (EDX), thermogravimetry–differential thermal analysis (TGA‐DTA), UV–vis spectroscopy, X‐ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area, and point of zero charge (pH pzc ). In adsorption studies, the effects of pH, adsorbent amount, time, initial dye concentration, and temperature were determined. The results were tested by kinetics (pseudo first order [PFO], pseudo second order [PSO], Elovich, and intra‐particle diffusion [I‐PD]) and isotherm (Freundlich, Langmuir, Temkin, Dubinin–Radushkevich [D‐R]) models and tested with 5 different error functions. Accordingly, the average pore diameter and pH pzc value were measured as 68.87 Å and 6.13, respectively. Also, the mass loss of 4.6%–28.7% and 24.3% at three temperatures was 121.2–533.5°C and 766.2°C, respectively. The adsorption mechanism was endothermic, and the removal efficiencies exceeded 99% in the first 10 min. Also, the most suitable models were determined to be Langmuir and PSO for both dyes, respectively. Maximum adsorption capacity ( q max ) calculated as 95.767 (for MB) and 151.657 (for CV) mg/g respectively. In this study, very promising results were achieved in the removal of two different dyes from water with the biosorbent obtained from pine needles, which we think will contribute to the sustainability of the forest ecosystem.
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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) | 0.000 | 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".