Elaboration of Superhydrophobic Nanoporous Ceramic Membranes - Application to Desalination
Bibliographic record
Abstract
Ceramic membranes are usually elaborated with metal oxides like alumina, titania or zirconia. These compounds have hydroxyl groups at their surface which give them a hydrophilic behaviour. In the field of membranes, it can be interesting to use membranes owning a hydrophobic behaviour. It is the case in the membrane distillation process. This process is based on a difference of temperature between the feed side and the permeate one. A very convenient method to change this behaviour is the grafting of particular molecules like fluoromolecules. The grafting was performed by immersion of the nanoporous membranes in a solution of the fluorinated compound in chloroform. Immersion time and fluoroalkylsilane concentration are the 2 principal parameters to be controlled. The characterisation was conduced by TGA, IR and Raman spectroscopy. The hydrophobic character was determined by measuring the water permeability and the water contact angle. Contact angles measured on flat samples are in the range 150 - 160°. The hybrid ceramics are super hydrophobic. Solutions of NaCl and seawater were filtered through the new membranes. The rejection of salts is total whatever their concentration.
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".