Porosity and packing features of nano- and micro-particles of carbon and silica adsorbents
Bibliographic record
Abstract
Comparative characterization of the particulate morphology and texture of various silicas (fumed silicas, silica gels, ordered mesoporous silicas) and carbons (chars and activated carbons, AC) is of interest from both theoretical and practical points of view since it allows one better understanding of advantages and disadvantages of various adsorbents upon their interactions with different adsorbates, co-adsorbates, and solutes in various dispersion media. Complete characterization needs application of a certain set of methods that is analyzed in the present paper. It is shown that the main difference in the textural characteristics of silica and carbon adsorbents is due to the absence (silicas) or presence (carbons) of nanopores in nanoparticles (NP). A great contribution of these pores in strongly activated carbons provides the specific surface area values greater by an order of magnitude than that of fumed silicas. Despite a high activation degree of AC, contribution of closed pores or pores inaccessible for nitrogen molecules remains relatively large in contrast to fumed silica A–300 composed of nonporous nanoparticles synthesized in the flame at higher temperature (~80% of melting temperature, Tm, for amorphous silica) than carbon activation temperature (~25%of Tm for carbons). Therefore, the pores inaccessible for nitrogen molecules in fumed silica could be attributed to narrow voids around contact area between neighboring NP in their aggregates, but for AC, there are both closed pores and open nanopores inaccessible for nitrogen molecules. For complete characterization of the morphology and texture of various adsorbents, such methods as transmission and scanning electron microscopies, probe (nitrogen, argon) adsorption, smallangle X-ray scattering (SAXS)and X-ray diffraction (XRD)could be used with appropriate software to analyze the data. The latter is especially important for the analyses of indirect data (e.g., adsorption, SAXS, XRD) characterizing the materials.
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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 | 0.001 | 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".