Bibliographic record
Abstract
In this work, mixed Fe/Cu oxides as sorbents for SO<sub>2</sub> and NH<sub>3</sub> removal were investigated. Nanoporous iron oxide mixed with 10, 20 and 30 at.% CuO were prepared by thermal decomposition of the corresponding oxalates at 250 °C for 5 h in air. The mixed Fe/Cu oxalates were obtained from the co-precipitation of iron/copper sulfate and ammonium oxalate during ultrasonication. The physical properties of the oxalate precursors and the resulting mixed Fe/Cu oxides were characterized with SEM, TGA-DSC, FTIR, powder XRD and Mössbauer spectroscopy. The porosity was studied by N<sub>2</sub> adsorption-desorption isotherms and small angle X-ray scattering. Evenly dispersed CuO hindered the crystallization of Fe<sub>2</sub>O<sub>3</sub>, which significantly increased the specific BET surface area from 211 m<sup>2</sup>/g for Fe<sub>2</sub>O<sub>3</sub> to 354 m<sup>2</sup>/g for Fe<sub>0.8</sub>Cu<sub>0.2</sub>O<sub>x</sub>. As a result, SO<sub>2</sub> and NH<sub>3</sub> adsorption on Fe<sub>0.8</sub>Cu<sub>0.2</sub>O<sub>x</sub> were enhanced by about 70% compared to Fe<sub>2</sub>O<sub>3</sub>. Compared to Fe<sub>2</sub>O<sub>3</sub>-impregnated activated carbons, nanoporous Fe<sub>0.8</sub>Cu<sub>0.2</sub>O<sub>x</sub> could capture five times more SO<sub>2</sub> per unit weight, which will be attractive for applications in respirators with lower weight and smaller size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".