Biochars from Lignin-rich Residue of Furfural Manufacturing Process for Heavy Metal Ions Remediation
Bibliographic record
Abstract
The pentose/furfural industrial manufacturing process uses corn cob residue as a raw material, where such a process yields significant amount of lignin-rich residue (LCR) at the end, which is commonly disposed by burning. In this study, the conversion of LCR to biochars (BCs), and their subsequent applications for heavy metal ion removal, were investigated. The BCs were prepared through hydrothermal carbonization and post-activation, using either ZnCl2 or H3PO4 treatment. The as-prepared activated BCs were characterized using N2 adsorption–desorption isotherms, XRD, FT-IR, SEM and TEM, and their performance in removing heavy metal ions (Pb2+, Cu2+, Cd2+) from aqueous solutions was assessed. The ZnCl2-activated BCs (BC-ZnCl2) exhibit a higher adsorption capacity than the H3PO4-activated BCs (BC-H3PO4), mainly due to the differences in their chemical/physical characteristics. The related adsorption kinetics and isotherms were analyzed.
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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".