Real-Time Observation of Carbon Oxidation by Driven Motion of Catalytic Ceria Nanoparticles within Low Pressure Oxygen
Bibliographic record
Abstract
Carbon particulate matter (PM) is an undesirable aerosol pollutant formed from combustors such as power plants, refineries, and engines. The most common and effective method of mitigating PM emission is the capture of particulates using a filter, before particles are released into the atmosphere. In order to develop and improve advanced filtering materials, a better understanding is required of their chemical and mechanical behavior. We report on a novel phenomenon on the mobility and oxidation behavior of catalytic iron doped ceria nanoparticles in contact with mobile carbon black nanoparticles. The process is recorded by real time imaging within an environmental transmission electron microscope. In contrast to observations in previous studies, the separated ceria nanoparticles are found to actively move on the substrate and consume the connecting carbon particles one-by-one. The velocity of particle motion is correlated to the reaction temperature and oxygen pressure, both determining the reaction rate. Modeling using the Density Functional Theory suggests this motion is driven by the chemical bonding between the surface oxygen of the catalyst and the graphite layers of carbon black, initiated through the Van der Waals force between two types of nanoparticles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".