Kinetic Uptake Studies of Powdered Materials in Solution
Bibliographic record
Abstract
Challenges exist for the study of time dependent sorption processes for heterogeneous systems, especially in the case of dispersed nanomaterials in solvents or solutions because they are not well suited to conventional batch kinetic experiments. In this study, a comparison of batch versus a one-pot setup in two variable configurations was evaluated for the study of uptake kinetics in heterogeneous (solid/solution) systems: (i) conventional batch method; (ii) one-pot system with dispersed adsorbent in solution with a semi-permeable barrier (filter paper or dialysis tubing) for in situ sampling; and (iii) one-pot system with an adsorbent confined in a semi-permeable barrier (dialysis tubing or filter paper barrier) with ex situ sampling. The sorbent systems evaluated herein include several cyclodextrin-based polyurethane materials with two types of phenolic dyes: p-nitrophenol and phenolphthalein. The one-pot kinetics method with in situ (Method ii) or ex situ (Method iii) sampling described herein offers significant advantages for the study of heterogeneous sorption kinetics of highly dispersed sorbent materials with particles sizes across a range of dimensions from the micron to nanometer scale. The method described herein will contribute positively to the development of advanced studies for heterogeneous sorption processes where an assessment of the relative uptake properties is required at different experimental conditions. The results of this study will be advantageous for the study of nanomaterials with significant benefits over batch kinetic studies for a wide range of heterogeneous sorption processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".