Continuous fixed‐bed column adsorption of nickel (II) using recyclable three‐dimensional cellulose nanocrystals‐hydrogel: Bed depth service time, Thomas, Adams–Bohart, and Yoon–Nelson modelling
Bibliographic record
Abstract
Abstract Heavy metal ions have detrimental impacts on both the environment and human health. Therefore, it is necessary to develop simple, economical water treatment adsorbents that employ easily modifiable, organic, biodegradable polymers like cellulose nanocrystals. This work used Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), X‐ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) to characterize the cellulose nanocrystal hydrogel prepared to remove Ni 2+ . The hydrogel was established to have two stable degradation points, ranging from 70 to 120°C to 250 to 380°C. Additionally, the principal functionalized groups observed in the hydrogel's molecular structure were CH, OH, and CO, which were uniform distribution and finger‐like structures as seen by SEM. It consisted of crystalline and amorphous structures, as shown by XRD patterns, making it a viable option for water filtration. BET showed that the surface area of the hydrogel increased upon modification. The column study involves optimization of pH, flow rate, concentration, and bed depth. According to experimental data, the effects of breakthrough parameters including pH (4, 5, 6) influent concentration (50, 75, and 100 mg/L), feed flow rate (5, 10, and 15 mL/min), and bed height (10, 15, and 20 cm). With an adsorption capacity of 58.65 mg/g, a flow rate of 10 mL/min, a bed depth of 20 cm, an influent concentration of 75 mg/L and a pH of 5 was found. The column experimental data fitted better to the Thomas, Yoon–Nelson, and bed depth service time modelling ( R 2 > 0.99) than the Adams–Bohart model with R 2 > 0.90. The adsorbent is economical and environmentally friendly due to its excellent regeneration capacity.
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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".