Cd(II) and Zn(II) adsorption on lignite‐derived humic substances and cattle manure biochar
Bibliographic record
Abstract
Abstract Excessive industrial release of trace elements may pose a great risk to the environment. Conventional remediation treatments have considerable limitations, making less expensive new technologies an important research area. Batch experiments were conducted at room temperature to investigate the use of two lignite‐derived humic products (nano‐humus and humic powder) and a cattle manure biochar as adsorbents in the removal of cadmium and zinc from laboratory synthesized water. Nano‐humus was most effective in adsorbing Cd(II), wherein 89% adsorption was rapidly achieved in 15 min. The adsorbed amount of Cd(II) and Zn(II) both increased with initial metal ion concentrations from 25 to 125 mg L −1 . Despite being produced from the same sources, nano‐humus and humic powder showed different properties and adsorption behaviors. The adsorption mechanism of nano‐humus followed the Freundlich isotherm model and pseudo‐second‐order kinetic model, indicating multilayer chemisorption. Humic powder followed the pseudo‐second‐order kinetic model, although it had a low isotherm model fit, implying chemisorption‐dominated adsorption. Cattle manure biochar followed the Freundlich isotherm and pseudo‐first‐order kinetics model, suggesting diffusion‐dominated multilayer adsorption. Of the three adsorbents tested, nano‐humus had greatest potential as an effective and inexpensive material for metal remediation.
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 | 0.000 | 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) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".