Removal of Cd2+ from aqueous solution using graphene oxide modified activate carbon derived from peanut shell
Bibliographic record
Abstract
Graphene oxide (GO) was prepared by a modified Hummers method using peanut shells and natural graphite, and graphene oxide modified peanut shells activated carbon composites (GO-AC) were synthesized by co-pyrolysis. The optimal preparation conditions of AC were screened by response surface methodology (RSM) to optimize the preparation process. The results showed that the surface of GO-AC had more micropores and larger specific surface area, increased the surface adsorption sites and had more oxygen-containing functional groups. The adsorption process was mainly based on chemisorption, and the adsorption capacity was 3.45 and 1.30 times higher than that of BC (45.16 mg/g) and AC (119.21 mg/g), respectively. After six adsorption-desorption cycle tests, the adsorption amount of Cd2+ by GO-AC was still as high as 89.26 mg/g, with a percentage increase of 93.5% and 365% compared to BC (19.18 mg/g) and AC (46.13 mg/g), respectively, with good reusability. The research can provide a useful reference for the high value-added conversion of waste biomass, and GO-AC loading modified with significant adsorption of Cd2+ has good potential for application as a novel and low-cost adsorbent. Keywords: graphene oxide-modified biochar, response surface optimization, adsorption, heavy metal DOI: 10.25165/j.ijabe.20231605.8046 Citation: Du Y L, Wang H, Ji J T, Jin X, Song Y, Zhang H, et al. Removal of Cd2+ from aqueous solution using graphene oxide modified activate carbon derived from peanut shell. Int J Agric & Biol Eng, 2023; 16(5): 226–235.
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) | 0.000 | 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 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".