Bibliographic record
Abstract
Abstract The use of nanoparticles to make nanocomposite membranes for water treatment spans a range of development with the majority of materials existing at lab scale, some having been piloted, and a select few at commercial scale. As such, the use of nanomaterials in this field is still in the early stages. There is a large parameter space to explore, which includes the many types of polymers used in membranes, the wide range of membrane types used in water separations, the great variety of nanomaterials and their properties, and the multitudinous ways in which nanomaterials can be added to polymers. Determining which polymer nanoparticle combination will deliver substantially improvement over the bare polymer is challenging, but attempting to predict how nanomaterials will impact the desired membrane and flux characteristics when incorporated into a polymer and formed into a membrane is even more difficult due to the complex thermodynamics and kinetics that occur between polymers and nanoparticles during membrane synthesis. Further, optimizing the interactions of nanoparticles with polymer systems is not trivial, but is critical to translating the nanomaterial properties to the polymer matrices in order to produce enhanced membranes. These challenges are opportunities to produce nanocomposite membranes with enhanced practical applications.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".