Intercalation of Copper Phthalocyanine Within Bulk Graphite as a New Strategy Toward the Synthesis of CuO-Based CO Oxidation Catalysts
Bibliographic record
Abstract
Graphite is a widely available natural form of carbon with peculiar chemical and surface properties. It is essentially hydrophobic and consists in very stable stacks of graphene layers held together by highly delocalized π-π interactions. Its use in chemistry and in particular for catalytic applications requires modification of its structure to increase its surface area. This is commonly achieved by harsh oxidation methods which also modifies the chemical composition of graphite and enables subsequent deposition of catalytic phases via common impregnation/reduction methods. Here we show that copper phthalocyanine (CuPc) can be incorporated into unmodified bulk graphite by the straight-forward sonication of a dimethylformamide solution containing CuPc and graphite flakes. Immobilization of the CuPc complex in the graphitic matrix is shown to rely on π-π interactions between the Pc ligand and graphenic surfaces. This strong CuPc-graphene interaction facilitates oxidation of the graphitic matrix upon oxidation of the immobilized complex, as shown by thermogravimetric analysis in air. Nevertheless, a soft oxidation treatment can be designed to produce CuO nanoparticles (NPs) without degrading the dispersing graphitic matrix. These well-dispersed CuO NPs are shown (1) to decrease the degree of stacking of graphite in the solid-state by intercalation in-between graphitic stacks, (2) to be more easily reducible than bulk CuO, and (3) to be catalytically active for the oxidation of carbon monoxide. The higher mass-specific CO oxidation rates observed, as compared with CuO/alumina benchmarks, highlight the beneficial role of the carbon support and the relevance of this new strategy toward the design of copper oxide catalysts from copper phthalocyanine metal complexes.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".