Evaluation of the SO<sub>2</sub> and NH<sub>3</sub> Gas Adsorption Properties of CuO/ZnO/Mn<sub>3</sub>O<sub>4</sub> and CuO/ZnO/NiO Ternary Impregnated Activated Carbon Using Combinatorial Materials Science Methods
Bibliographic record
Abstract
Impregnated activated carbons (IAC) are widely used materials for the removal of toxic gases in personal respiratory protection applications. The combinatorial method has been employed to prepare IACs containing different types of metal oxides in various proportions and evaluate their adsorption performance for low molecular weight gases, such as SO(2) and NH(3), under dry conditions. Among the metal oxides used for the study, Mn(3)O(4) was found to have the highest capacity for retaining SO(2) gas under dry conditions. NiO and ZnO were found to have similar NH(3) adsorption capacities which are higher than the NH(3) capacities observed for the other metal oxide impregnants used in the study. Although Cu- or Zn-based impregnants and their combinations have been extensively studied and used as gas adsorbents, neither Mn(3)O(4) nor NiO have been incorporated in the formulations used. In this study, ternary libraries of IACs with various combinations of CuO/ZnO/Mn(3)O(4) and CuO/ZnO/NiO were studied and evaluated for their adsorption of SO(2) and NH(3) gases. Combinations of CuO, ZnO, and Mn(3)O(4) were found to have the potential to be multigas adsorbents compared to formulations that contain NiO.
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".