The Effect of Modifying Canadian Goldenrod (Solidago canadensis) Biomass with Ammonia and Epichlorohydrin on the Sorption Efficiency of Anionic Dyes from Water Solutions
Bibliographic record
Abstract
This study examined the effect of modifying Canadian goldenrod (Solidago canadensis) biomass on its sorption capacity of Reactive Black 5 (RB5) and Reactive Yellow 84 anionic dyes. The scope of the research included the characteristics of sorbents (FTIR, elementary analysis, pHPZC), the effect of pH on dye sorption efficiency, sorption kinetics, and the maximum sorption capacity (describing the data with Langmuir 1 and 2 and Freundlich models). FTIR analyses showed the appearance of amine functional groups in the materials modified with ammonia water, which is indicative of the sorbent amination process. The amination efficiency was higher in the case of materials pre-activated with epichlorohydrin, which was confirmed by elemental analysis and pHPZC values. The sorption efficiency of RB5 and RY84 on the tested sorbents was the highest in the pH range of 2–3. The sorption capacity of the goldenrod biomass pre-activated with epichlorohydrin and then aminated with ammonia water was 71.30 mg/g and 59.29 mg/g in the case of RB5 and RY84, respectively, and was higher by 2970% and 2510%, respectively, compared to the unmodified biomass. Amination of biomass pre-activated with epichlorohydrin can increase its sorption capacity, even by several dozen times.
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 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) | 0.001 | 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".