A New Method of Gel-Coating Polyethyleneimine (PEI) on Organic Resin Beads. High Capacity and Fast Kinetics of PEI Gel-Coated on Polystyrene
Bibliographic record
Abstract
A new process has been developed for gel-coating a high-capacity weak-base resin, polyethyleneimine (PEI), on an inert organic support, polystyrene (PS), used as macroporous spherical beads. The process produces a thin and firm coating of PEI, cross-linked with glutaraldehyde and chemically bonded to the PS surface through carboxylic acid groups, created a priori by exposure to a chromic acid solution in glacial acetic acid for 3 h at the reflux temperature. Designated as OPS···[PEI.XG], the spherical gel-coated beads afford nearly full attainment of the theoretical proton capacity of the coated resin and exhibit significantly faster attainment of equilibrium sorption, compared to a conventional bead-form ion exchanger. Ascorbic acid and UO 2 SO 4 have been used as test sorbates for comparing the performance of OPS···[PEI.XG] with that of a commercial weak-base resin poly(4-vinylpyridine) (PVP). In an ascorbic acid substrate solution, the gel-coat layer of OPS···[PEI.XG] exhibits a sorption capacity of 12 mmol/g of (dry) resin, as compared to 2 mmol/g of (dry) PVP resin beads, while for UO 2 2+ the corresponding capacities are 4.5 and 1.1 mmol/g of dry resin, respectively. Large differences are also observed in sorption rates of gel-coated resin beads, in which the sorption is largely confined to the surface gel layer, and conventional resin beads, in which sorption occurs in the whole matrix.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".