Photothermal Catalytic Polyester Upcycling over Cobalt Single‐Site Catalyst
Bibliographic record
Abstract
Abstract Photothermal catalytic conversion of waste plastics into fuels and/or feedstocks using renewable solar energy can achieve solar‐to‐chemical conversion, resource sustainability, and environmental remediation simultaneously. However, the construction of photothermal catalysts with strong light absorption and high catalytic activity remains a great challenge. In this work, integrated cobalt single‐site catalysts (Co SSCs), coupled with strong photothermal conversion, high catalytic activity, and stability, are employed to catalyze the glycolysis of polyesters. The unique coordination‐unsaturated CoO5 single‐site can coordinate with the carbonyl groups in polyester, thus boosting the nucleophilic addition elimination processes. As a result, the space‐time yield of Co SSCs is an order of magnitude higher than that of general catalysts. In addition, the polyethylene terephthalate (PET) conversion and bis(2‐hydroxyethyl) terephthalate yield in photothermal catalysis are 5.4 and 6.6 times higher than those of thermal catalysis under the same conditions, which are contributed by the localized heating effect. Technical economic analysis shows that the recycling of 105 tons of waste PET by photothermal catalysis consumes 146.4 GW·h electrical energy and misses 7.44 × 104 tons of CO2 emission. Therefore, a high‐efficient photothermal catalytic plastic recycling system is of great significance for waste plastic valorization.
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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".