Adsorption optimization of acyclovir on prepared activated carbon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Canadian pine cones (pine cone type I and pine cone type II) and deoiled canola meal were utilized as precursors for preparation of powdered activated carbons (PACs) using thermo‐chemical process. Physicochemical properties of the biomass and obtained adsorbents were evaluated. Prepared PACs were investigated for the adsorptive removal of acyclovir (ACV) from aqueous solutions. Pine cone type I PAC was found to have well developed pores and surface area. It was found to be suitable for removal of ACV from aqueous solution. Response surface methodology was used for the optimization of ACV adsorption on PAC prepared from pine cone type I and the development of predictive model. A second‐order polynomial equation fitted data well which indicated that the process variables such as temperature, pH and especially adsorbent dose had large impact on the ACV adsorption process. The optimum conditions for ACV removal were found to be 39 °C, pH 8 and 2 g/L of pine cone type I PAC dose. At these conditions, the removal of ACV was 90.3 %, which was close to the maximum predicted value of 92.5 %.
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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) | 0.000 | 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 it