Carboxyalkylated Lignin as a Sustainable Dispersant for Coal Water Slurry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coal water slurry (CWS) has been considered a cleaner and sustainable alternative to coal. However, the challenging suspension of coal particles in CWS has created a major obstacle to its use in industry. This study presents a novel approach to enhance the stability and rheological properties of coal water slurry (CWS) through the utilization of carboxyalkylated lignin (CL) as a dispersant. The generated CL samples had high water solubility of around 9 g/L and a charge density of around 2 mmol/g. All CLs were able to stabilize the coal suspension, and their performance decreased due to the increase in the alkyl chain length of carboxyalkylated lignin. Carboxymethylated lignin (CL-1) improved the stability of the coal suspensions with the lowest instability index of less than 0.6. The addition of CLs reduced the contact angle of the coal surface from 45.3° to 34.6°, and the increase in the alkyl chain length hampered its effect on contact angle changes. The zeta potential measurements confirmed that the adsorption of CL enhanced the electrostatic repulsion between coal particles in suspensions, and the zeta potential decreased with the increased alkyl chain length of CLs due to increased steric hindrance. The rheology results indicated that CLs demonstrated shear thinning behavior. This innovative method showcases the affinity of carboxyalkylated lignin to improve the performance of CWS, offering an environmentally friendly alternative for producing a cleaner product, i.e., sustainable coal water slurry, with improved suspension stability.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it