Simulating Polydisperse Polymer Adsorption onto Porous Substrates
Bibliographic record
Abstract
Simulated adsorption isotherms that describe the irreversible binding of cationic polydisperse polymers onto anionic porous wood pulp fibers were used to relate the physicochemical properties of the polymers and fibers to five attributes of both simulated and experimental isotherms. The analysis is complicated because the lower molecular weight fractions of the adsorbing polymer access more fiber surface area compared to the larger chains. A key assumption is that Γ = λ·ssa = CP· D, where Γ (mg/g) is the amount of adsorbed polymer, λ (mg/m 2 ) is the coverage, ssa (m 2 /g) is the accessible specific surface area, CP is the cumulative polymer chain length probability, and D is the corresponding polymer dose. The polymers are assumed to have a log–normal chain length distribution characterized by a mean, n m, and a coefficient of variation, cv. The final polymer property is the Mark–Houwink exponent β. The fiber’s accessible specific surface area, ssa, was assumed to be a power-law function of the adsorbing polymer chain length. This power law is described by three properties: the slope, the ssa of the exterior fiber surfaces, and the corresponding polymer chain length. Simulated isotherms exhibited the general features of published isotherms. The simulations indicated the links between five isotherm attributes and six simulated isotherm physical properties. Comparisons with published adsorption data supported this approach.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".