Experimental Approaches to Adsorption-Desorption Dynamism of Human Serum Albumin (HSA) onto Crosslinked N,N′-Diethylaminoethyl (DEAE) Dextran Microbeads
Bibliographic record
Abstract
Crosslinked N,N′-Diethylaminoethyl (DEAE) groups containing dextran microbeads have been used in human serum albumin (HSA) adsorption-desorption studies. For the HSA adsorption onto positively charged hydrophilic DEAE dextran microbeads, the adsorption kinetic was slightly decreased by the changing concentration of the protein solution. Adsorption kinetics and equilibrium isotherms for the adsorption of HSA on crosslinked DEAE dextran have been determined experimentally. Modeling of the adsorption processes on DEAE dextran microbeads were realized by applying different adsorption isotherms. Among the several isotherm equations, Langmuir and Freundlich adsorption isotherms were investigated depending on the two temperatures. These were only slightly dependent on the initial concentration of HSA but were considerably affected by the pH of the medium. The HSA adsorption capacity factor and the adsorption equilibrium constant were obtained and mathematical modeling of adsorption, adsorption rate constants and maximum adsorption were determined. Besides the adsorption mechanism, optimum ionic strength and optimum pH also were investigated. Desorption studies and desorption ratio of the system were determined for optimum medium conditions. It was been proved both experimentally and theoretically that human HSA is adsorbed by electrostatic attraction, ion-exchange, hydrophobic interaction and/or hydrogen bonding.
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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 | 0.000 |
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".