Engineered hydrochar from polyester waste: Synthesis, optimization, and environmental impact
Bibliographic record
Abstract
Abstract Polyester has been revalorized to generate new products. Among various alternatives, thermal treatments for producing carbon‐based materials are highlights. Within these methods, hydrothermal carbonization (HTC) stands out for its environmental advantages, as it yields a carbon‐based material known as hydrochar. However, limited studies have explored how HTC process changes textural, chemical, thermal, and surface properties of the hydrochar. Therefore, hydrochar production using polyester as feedstock under varying process conditions, including polyester ratios, FeSO 4 addition, carbonization temperature, and drying time, was performed. The results indicated that HTC modifies the properties of polyester. Hydrochars synthesized at 200°C with 120 g L −1 polyester and FeSO 4 exhibited superior mesoporous structures, enhancing thermal stability, and reduced mass loss during thermal decomposition, attributed to the presence of FeSO 4 . A life cycle assessment (LCA) using the Eco‐Indicator 99 method revealed that processes without FeSO 4 had lower environmental impacts, primarily due to the high energy demand associated with the iron salt. Nonetheless, processing conditions using 120 g L −1 of polyester at 200°C were identified as the most sustainable, offering minimal environmental trade‐offs despite a reduced yield. In contrast, the process using 120 g L −1 polyester and FeSO 4 at 190°C optimized yield with acceptable environmental compromises, making it more suitable for yield‐driven applications. The process using 60 g L −1 polyester, without FeSO 4 at 200°C, presented a balanced approach between sustainability and efficiency. These findings highlight the potential of polyester waste in hydrochar production and underscore the importance of optimizing process parameters to balance environmental impact and material performance.
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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 | 0.001 |
| 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".