NanoParticle Flow Reactor (NanoPFR): a tested model for simulating carbon nanoparticle formation in flow reactors
Bibliographic record
Abstract
Abstract Flow reactors are widely used to study the formation of various nanoparticles, such as carbon black, soot, nickel, titania, and silica. Such reactors provide well-controlled conditions, making them a favored laboratory tool to investigate the details of particle formation. Here we present NanoParticle Flow Reactor (NanoPFR), a detailed model to simulate nanoparticle synthesis in flow reactors. The model predicts the agglomerate fractal-like morphology and size distribution with a 2-variable sectional population balance model coupled with gas-phase chemistry. The particle formation processes employed in the code are tested using detailed discrete element modeling simulations and then used to predict carbon black formation from ethylene pyrolysis experiments from the literature. The code is a robust flow reactor predictive tool with a strong foundation that can serve as a basis for further development, including the simulation of other nanoparticles formation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".