Effect of a heat pretreatment on the structure and properties of carbon supports for carbon membranes
Bibliographic record
Abstract
Abstract A heat pretreatment was developed to modify the microstructure and properties of the phenolic resin‐based carbon supports for the preparation of carbon membranes. The pore size distribution, porosity, surface functional groups, microstructure, and morphology of the supports were characterized by bubble pressure method, boiling method, infrared spectroscopy, X‐ray diffraction, and scanning electron microscopy, respectively. Furthermore, the optimum preparation conditions of the as‐prepared supports were validated by the fabrication of supported carbon membranes. The results show that the heat pretreatment at 280 °C is helpful for the supports to tolerate the high temperature of subsequent pyrolysis and improve the adhesion to surface carbon membrane layers. When supported carbon membranes were prepared by the heat pretreated supports, spin‐coating of 4 times, and pyrolysis at 650 °C, the ideal selectivities of H2/N2 and O2/N2 can reach 52.8 and 8.0, respectively.
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 | 0.001 |
| 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".