Carboxyalkylated lignin derived coal wastewater slurry
Bibliographic record
Abstract
Preparing coal water slurry from coal gasification wastewater as gasification/combustion feedstock offers a promising strategy for wastewater management and resource recovery. The primary challenge lies in optimizing slurry formulation to accommodate wastewater as the aqueous medium. This study demonstrated that carboxyalkylated lignin derivatives, i.e., biomass-based aromatic polymers, were effective dispersants for generating coal gasification wastewater slurry (CGWS). The alkyl chain lengths of carboxyalkylated lignin derivatives significantly influenced their molecular characteristics and, consequently, the physicochemical properties of the resulting slurry. Fundamentally, the dispersion performance of lignin derivatives and sodium methylene dinaphthalene sulfonate (NNO), as a commonly used dispersant for the slurry, was different. While NNO promoted dispersion via increasing electrostatic repulsion, the lignin derivatives promoted the slurry formulation primarily via grafting molecular structure. Among the tested lignin derivatives, CPr (carboxybutylated lignin) required the lowest dosage (0.20 wt%) and increased the solid concentration of the slurry from 50.42 wt% (NNO-CGWS) to 52.81 wt% (CPr-CGWS) at an apparent viscosity of 1000 mPa·s, while reducing its pseudo-plasticity. Additionally, lignin derivatives increased the calorific value of CGWS by 0.191 kcal/g using CPr at a dosage of 0.20 wt%. This work establishes carboxyalkylated lignin, particularly CPr, as a highly effective and eco-friendly dispersant for preparing value-added CGWS from industrial wastewater.
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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".