Green Chemistry for the Transformation of Chlorinated Wastes: Catalytic Hydrodechlorination on Pd-Ni and Pd-Fe Bimetallic Catalysts Supported on SiO2
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Bibliographic record
Abstract
Monometallic catalysts based on Fe, Ni and Pd, as well as bimetallic catalysts based on Fe-Pd and based on Ni-Pd supported on silica, were synthesized using a sol–gel cogelation process. These catalysts were tested in chlorobenzene hydrodechlorination at low conversion to consider a differential reactor. In all samples, the cogelation method allowed very small metallic nanoparticles of 2–3 nm to be dispersed inside the silica matrix. Nevertheless, the presence of some large particles of pure Pd was noted. The catalysts had specific surface areas between 100 and 400 m2/g. In view of the catalytic results obtained, the Pd-Ni catalysts are less active than the monometallic Pd catalyst (<6% of conversion) except for catalysts with a low proportion of Ni (9% of conversion) and for reaction temperatures above 240 °C. In this series of catalysts, increasing the Ni content increases the activity but leads to an amplification of the catalyst deactivation phenomenon compared to Pd alone. On the other hand, Pd-Fe catalysts are more active with a double conversion value compared to a Pd monometallic catalyst (13% vs. 6%). The difference in the results obtained for each of the catalysts in the Pd-Fe series could be explained by the greater presence of the Fe-Pd alloy in the catalyst. Fe would have a cooperative effect when associated with Pd. Although Fe is inactive alone for chlorobenzene hydrodechlorination, when Fe is coupled to another metal from the group VIIIb, such as Pd, it allows the phenomenon of Pd poisoning by HCl to be reduced.
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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 | 0.000 | 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) | 0.000 | 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 it