Cu–Al–Zn/Magnetite Nanoparticle Composite Catalysts for Steam Reforming of MeOH by Magnetic Induction
Bibliographic record
Abstract
We explore the use of a mechanical mixture comprising a commercial Cu–Al–Zn catalyst (CZA, HiFuel W220) and 160 nm magnetite nanoparticles as a susceptor for methanol steam reforming powered by induction heating. By the generation of heat locally near catalytic sites, this approach reduces thermal transfer limitations typically encountered in externally heated systems. The optimized mixture achieved 46% methanol conversion at 150 °C under induction heating compared to the 225 °C required for similar performance using conventional oven heating. The stability and performance of the mixture were assessed under both inert and reactive conditions. A solid-state reaction between the catalyst and magnetite was observed during inert gas heating, but this was effectively suppressed under the reducing methanol reforming stream. Durability tests showed stable activity with no significant copper sintering or performance loss over a period of 3 h. Magnetite-mediated localized heating enabled an efficient reaction while maintaining a low overall bed temperature. This work demonstrates the potential of catalyst–susceptor integration to enhance thermal efficiency in heterogeneous catalysis. The findings highlight design strategies for applying induction heating to catalytic systems, offering improved energy utilization and operational control for gas-phase reactions.
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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".