Modified Lignocellulosic Waste for the Amelioration of Water Quality: Adsorptive Removal of Congo Red and Nitrate Using Modified Poplar Sawdust
Bibliographic record
Abstract
Since the synthetic dye Congo red and nitrate are notorious contributors to water pollution due to their persistent and potentially toxic nature, it is necessary to develop new efficient methods to remove them from water bodies. Native lignocellulosic materials as biosorbents are mostly inferior, i.e., the adsorption capacities of native materials are lower. Therefore, attempts have been made to improve the adsorption capacities of such materials by physical and/or chemical methods, including the production of biochar. In this study, adsorptive removal was investigated using a novel biosorbent (mPWS) obtained by modifying poplar (waste) sawdust through quaternisation. The characterisation of mPWS included SEM/EDX, FTIR, and MIP analysis. The adsorption of CR and nitrate onto mPWS was studied in a batch system, as a function of contact time (1–240 min), biosorbent concentration (1–8 g·dm−3), and initial adsorbate concentration (25–200 mg·dm−3). In all experiments, a high removal of both adsorbates, from 60 to over 90%, was achieved. Langmuir and Freundlich adsorption isotherm models were used in order to describe equilibrium adsorption data, while pseudo-first-order and pseudo-second-order kinetic models, and the intraparticle diffusion model, were used to describe possible adsorption mechanisms. The Langmuir model fit the adsorption data of CR well, while the nitrate adsorption process was better interpreted with the Freundlich isotherm model. The kinetics data for both CR and nitrate agreed with the pseudo-second-order kinetics model, while analysis using the intraparticle diffusion model indicated two rate-limiting steps during the adsorption process. Based on the results, it can be concluded that the tested novel biosorbent can be effectively used for the removal of CR and nitrate from water (with its adsorption capacities being 70.3 mg·g−1 and 43.6 mg·g−1, respectively).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".