Regioselective oxidation of recycled cellulosic fibres for enhancement in the mechanical strength of resulting papers
Bibliographic record
Abstract
Abstract The worldwide recycling rate for paper is about 60% and there are ongoing efforts to enhance the recycling rates to 80%. The low strength of recycled paper primarily due to the contaminants in it fetches lower value to the recycled paper in the market and also limits its recyclability. The ways to circumvent the problem of contamination of paper have been addressed by adding additives. However, the use of additives increases the cost of the final recycled paper. Oxidation has been explored as an alternative to enhance the tensile strength of paper in this work as it can be used in existing paper recycling facilities without any modifications. Pulp fibres were collected from an active paper recycling mill and subjected to oxidation at various oxidation levels from 0.25 up to 1.5 mmol/g. It was found that the tensile strength of the paper increased by up to 89% with an increase in oxidation level up to 1 mmol/g and later decreased with a further increase in the oxidation level up to 1.5 mmol/g. The brightness of the paper increased by up to 3% with an increase in oxidation level up to 0.85 mmol/g and then a decrease with further enhancement in the oxidation level. The oxidized samples were also studied with infrared spectroscopy, x‐ray diffraction, electron microscopy, and optical properties to study the behaviour of the resulting material. A ring crush test and corrugating medium test were also performed on the oxidized pulp samples to establish the feasibility of the material for packaging applications.
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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 | 0.000 |
| 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 | 0.001 |
| 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".