A Simple and Green Procedure to Prepare Efficient Manganese Oxide Nanopowder for the Low Temperature Removal of Formaldehyde
Bibliographic record
Abstract
Abstract Activated reactive synthesis, as a top‐down synthesis approach, is proposed for the production of a nanocrystalline, high surface area, manganese (IV) oxide starting from commercial micrometric α‐MnO2. The developed approach consists of two‐steps: 1) high energy ball milling (HEBM) produces a nano‐sized material, 2) low energy ball milling (LEBM) improves textural properties. During the HEBM step, elementary crystals are observed to evolve from several hundred elongated cylindrical particles to short length cylinders and pseudo spherical particles. Despite fractioning of the elementary crystals, surface area remains low over the HEBM derived solid. The LEBM step, achieves crystal shape modification, giving rise to only pseudo‐spherical nanometric particles and significantly increasing the surface area to approximately 60–80 m2 g−1. Catalytic activity of α‐MnO2 powder is affected by the grinding process. Activity is similar to the fresh commercial MnO2 after HEBM, and increases upon LEBM reaching a maximum for LEBM time of 1 h. Thereafter, activity remains constant despite the further increase in surface area upon prolonged LEBM. The presence of an optimal activity after limited LEBM time is paralleled with the increase of the iron contamination occurring after prolonged LEBM time which impacts manganese reducibility and results in less reactive surface.
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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".