Experimental methods in chemical engineering: specific surface area and pore size distribution measurements—BET, BJH, and DFT
Bibliographic record
Abstract
Abstract Gas physisorption is an experimental technique based on equilibrium Van der Waals interactions between gas molecules and solid particles, that quantifies the specific surface area (SSA), pore size distribution (PSD), and pore volume of solids and powders. The performance of catalysts, absorbents, chromatography column materials, and polymer resins depends on these morphological properties. Here we introduce the basic principles and procedures of physical adsorption, especially nitrogen physisorption, as a guide to students and researchers unfamiliar with the field. The Brunauer‐Emmett‐Teller theory (BET) is a common approach to estimate SSA that extends the Langmuir monolayer molecular adsorption model to multilayer layers. It relies on an equilibrium adsorption isotherm, measured at the normal boiling point of the adsorbate, eg, 77 K or 87 K for N 2 and Ar, respectively. Web of Science indexed 45 400 articles in 2016 and 2017 that mentioned N 2 adsorption porosimetry—BET and BJH (Barrett‐Joyner‐Halenda) keywords. The VOSViewer bibliometric tool grouped these articles into four research clusters: adsorption, activated carbon in aqueous solutions for removal of heavy metal ions; synthesis of nanoparticles and composites; catalysts performance in oxidation and reduction processes; and photocatalytic degradation with TiO 2 . According to the literature, the accuracy of the density function theory (DFT) method is higher than with the BJH theory and it is more reliable.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".