Bibliographic record
Abstract
Porous, high surface area activated carbon (AC) can be used to remove certain irritating\nand toxic gases from contaminated air streams. Impregnating AC with carefully\nselected chemicals can improve ACs adsorption capacity for certain gases and provide\nadsorption capacity for gases that un-impregnated AC cannot fi lter. Impregnated activated carbons (IACs) and ACs can be used as the active component in respirators.\nComparative studies of di fferent commercially available AC samples and of IAC\nsamples, prepared from a wide variety of di fferent chemicals, were performed. The gas\nadsorption capacity of the samples was tested using sulfur dioxide (SO2), ammonia\n(NH3), hydrogen cyanide (HCN) and cyclohexane (C6H12) challenge gases and compared to results obtained from a commercially available broad spectrum respirator\ncarbon. The samples were characterized using wide angle x-ray di raction (XRD),\nsmall angle x-ray scattering (SAXS), nitrogen adsorption isotherms, thermal gravimetric\nanalysis (TGA) and scanning electron microscopy (SEM).\nHighlights of this work include the discovery of a IAC sample prepared from\nzinc nitrate (Zn(NO3)2) and nitric acid (HNO3) that, after heating at 180 C under\nargon, had overall dry gas adsorption capacity that was greater than the commercially\navailable sample. The importance of pore size on the C6H12 adsorption capacity of\nAC was demonstrated using SAXS and nitrogen adsorption data. A relationship\nbetween decreased humid C6H12 capacity and pre-adsorbed water was shown using\nSAXS, TGA and gravimetric studies.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".