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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".