Valorization of decationized newsprint to levulinic acid
Bibliographic record
Abstract
As of today, most chemical products are fossil-based. The environmental concerns of fossil resources due to their constant misuse have led to the exploration of bio-based alternatives. Biomass comprising industrial and municipal wastes, agricultural residues, forest residues, and natural herbaceous plants can favorably replace fossil fuel to produce chemicals. In this study, softwood and hardwood pulps were used to synthesize levulinic acid. Prior to a dilute acid hydrolysis step, the wood pulps were decationized overnight with 0.2 M HCl. The effects of the major reaction conditions including reaction temperature, time, and HCl concentration on the yield of levulinic acid was studied via a central composite design. Levulinic acid yields from softwood and hardwood pulps reached 50.30 and 68.85 mol%, respectively, at optimum reaction conditions. When newsprints were tested using the optimized parameters for softwood and hardwood conversion, levulinic acid yields of 66.25 and 79.65 mol% were obtained, respectively. A kinetic model was developed to predict the yields of glucose, hydroxymethylfurfural, and levulinic acid from the HCl-pretreated newsprint. The analysis of the kinetic parameters and the results of the response surface methodology experiments provided optimized conditions for levulinic acid production.
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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) | 0.001 | 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) | 0.001 | 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".