Deinking sludge compost stability and maturity assessment using Fourier transform infrared spectroscopy and thermal analysis
Bibliographic record
Abstract
Thermal composting is an important and useful way to transfer raw organic matter into value-added product rich in humic substances. Furthermore, thermal composting is a very promising way to reduce deinking paper sludge pollutions, which are difficult to remove. The objective of this study was to investigate the behaviour of the composting process of deinking paper sludge with poultry manure over 14 months. Two composts were used: C1 (70/30: deinking paper sludge/poultry manure) and C2 (50/50: deinking paper sludge/poultry manure). The compost stability and maturity were assessed via physico–chemical and thermal analyses: thermogravimetric analysis, differential thermal analysis and diffuse reflectance infrared Fourier transform spectroscopy. Diffuse reflectance infrared Fourier transform spectroscopy analysis provided chemical information for the presence of aromatic, phenolic, aliphatic and polysaccharidic structures. Thermogravimetry analyses revealed that both deinking paper sludge composts were rich in humic acids. The ratio E4/E6 (fulvic acids/humic acids) was also carried out to characterise the maturity index of composts. After 14 months, the compost C2 is more stable, mature and rich in humic acids than compost C1. Furthermore, the addition of poultry manure to the deinking paper sludge-based composts enhanced the formation of humic substances. From 0 to 14 months of composting, the concentration of polychlorinated biphenyls decreased from 0.2 to 0.1 mg kg −1 and from 0.6 to 0.2 mg kg −1 for C1 and C2, respectively.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".