Model fitting of sorption kinetics data: Misapplications overlooked and their rectifications
Bibliographic record
Abstract
When the model fitting of sorption kinetics data was performed using linearized pseudosecond‐order rate equations based on constant Q e corresponding to equilibrium sorption, the instantaneous driving force for sorption was underestimated, resulting in an erroneous overestimation of the rate constant. To resolve the issue, a rectification of the model fitting was proposed by accounting for the concentration dependence of Q e in the model equation based on the fact that Q e in the equation represents the sorption capacity at that instant as sorption proceeded with time. The rectified approach was validated with experimental data for various sorption systems reported in the literature. It was shown that the rectification yielded true sorption rate constant that characterizes the relationship between sorption rate and solute concentration, thereby resolving the issues associated with the original approach where the specific rate constant was found to depend on solute concentration and sorption time. © 2017 American Institute of Chemical Engineers AIChE J , 64: 1793–1805, 2018
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 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 | 0.001 | 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) | 0.000 | 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".