Enhancing soot oxidation using microtextured surfaces
Bibliographic record
Abstract
Biomass combustion provides energy needs for millions of people worldwide. However, soot accumulation on the combustors' walls significantly reduces heat transfer efficiency. Herein, we demonstrate how microtextured surfaces minimize soot accumulation by enhancing soot oxidation. We investigate soot layers from the combustion of paraffin wax as a model for wood-based soot, and find that randomly microtextured glass obtained by sandblasting shows a 71% reduction in the time taken to oxidize 90% of surface soot coverage when compared to smooth glass at 530 °C. We also study grooved microtextures fabricated via laser ablation and find that grooves with widths between 15 and 50 µm enhance soot oxidation, while the expedited advantage is lost when the groove width is 85 µm. X-ray photoelectron spectroscopy validates the superior extent of soot removal on microtextures down to a sub-nanometer length-scale. The high density of sharp features such as peaks and edges on microtextures, and the conformality of the soot layer to the microtextures contribute to increased soot oxidation. We also demonstrate enhanced soot oxidation on microtextured stainless steel, the principal material of construction in biomass combustors. Microtextured surfaces that facilitate soot oxidation upon contact could significantly improve performance and longevity in various combustion applications.
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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".