Impact of biochar physical properties on adsorption mechanisms for removal of aromatic aqueous contaminants in water
Bibliographic record
Abstract
A potential application of biochars produced by pyrolysis of lignocellulosic feedstocks is as sustainable adsorbent materials for the removal of organic contaminants in water, such as pharmaceuticals and dyes. To understand the impact of biochar's physical characteristics on the adsorption mechanisms of organic aromatic contaminants in water, Douglas fir and red cedar derived biochars were produced and treated under different conditions. CO 2 was found to produce biochar with greater oxygen-containing functional groups and higher aromaticity compared to those produced under N 2, while the pyrolysis temperature showed greater adsorption for organic contaminants when pyrolyzed at higher temperatures, attributed to greater aromaticity and surface area. The effect of different oxygen-containing groups, mainly on π - π interactions was explored by characterizing the biochar surface after three different post-pyrolysis treatments (HNO 3 , H 2 O 2 , and KOH) and comparing the effects on the adsorption mechanisms for test compounds (ibuprofen, acetaminophen, methyl orange, and methylene blue). The results showed that increasing aromaticity and electron-donating groups, such as hydroxyl functional groups, increased π - π electron donor-acceptor (EDA) interactions with compounds containing electron-withdrawing groups. The presence of electron-withdrawing carbonyl groups resulted in an increase in adsorption towards compounds with electron-donating groups.
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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) | 0.000 | 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) | 0.001 | 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".