Carbon Nanofilaments Functionalized with Iron Oxide Nanoparticles for in-Depth Hydrogen Sulfide Adsorption
Bibliographic record
Abstract
The purification of hydrogen prior of its use in various applications, such as fuel cells, is of paramount importance. Although there are many commercial ways to obtain hydrogen sulfide, the need to reach very low concentration values, at the ppm or even at the ppb level, is the main motivation behind this work. This work examines the production and utilization of a new, low H 2 S breakthrough and high capacity adsorbent, made of iron nanoparticles embedded in carbon nanofilaments. It is produced by a 2-step functionalization methodology: acid pretreatment and iron wet impregnation. This novel adsorbent was characterized by scanning transmission electron microscope, X-ray absorption near edge structure, Brunauer Emmet and Teller calculations, and thermogravimetric analysis, and the adsorption efficiency was measured for different iron-loadings, temperatures, and H 2 S breakthrough values. Operating conditions and metal-loading that allow a decrease of H 2 S concentration from 500 ppm to below 1.5 ppm are reported. It has also been found that acid treatment influences metal dispersion and, due to the nanometric nature of adsorbents, the process is not controlled by mass diffusion phenomena.
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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".