Selective adsorption of water from aqueous butanol solution using canola-meal-based biosorbents
Bibliographic record
Abstract
In the present paper, the capabilities of canola meal (CM)-based biosorbents for the selective water removal from aqueous solution of butanol were investigated for purifying butanol. The raw canola meal (RCM) after protein extraction was pretreated using 5% (v/v) sulfuric acid to enhance the water adsorption characteristics of CM. This pretreated canola meal (PCM) was used as an adsorbent along with the RCM adsorbent for selective water removal. Biosorbents were characterized by Fourier transform infrared, carbon hydrogen nitrogen sulfur (CHNS), Brunauer, Emmett, and Teller surface area, and X-ray diffraction. The surface area and micropore volume were increased in PCM. In addition, crystallinity index and CHNS content in PCM were also increased. Both the adsorbents were able to selectively adsorb water from the aqueous solutions of butanol. PCM demonstrated a higher water uptake and a higher final butanol concentration than RCM. The adsorption diffusion model better fits the kinetic data of water adsorption by PCM in a butanol solution containing 95.3 wt% water. The adsorption isotherm was also investigated. The mean free energy per molecule of adsorbate () based on Dubinin–Radushkevich theory indicated that water adsorption is favorable and water or butanol adsorption was physical in nature.
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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 | 0.000 |
| 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 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".