Comparative performance of functional adsorbent materials for sustainable metal ion recovery
Bibliographic record
Abstract
Heavy metal contamination in water systems poses serious environmental and health risks, necessitating the development of efficient and sustainable treatment technologies. This study explores the adsorption performance of six adsorbent materials for heavy metal removal from aqueous solutions, focusing on two Ti 3 C 2 T x MXenes synthesized through LiF/HCl and NH 4 HF 2 /citric acid etching, commercial activated carbon, α-MnO 2 , and two biomass-derived activated carbons. The materials were characterized using XRD, FTIR, SEM, EDX, and BET analyses, revealing key differences in morphology, surface chemistry, and elemental composition. Adsorption experiments targeting Cr 6+ , Pb 2+ , Zn 2+ , and five other heavy metal ions demonstrated that Ti 3 C 2 T x - NH 4 HF 2 exhibited the highest adsorption capacities due to its delaminated structure and oxygen-rich surface. While other materials like α-MnO 2 and biosourced activated carbons with much higher specific surface area showed moderate to limited performance, the findings reiterate the critical role of surface functionality over plain surface area. The results also highlight how equilibrium-driven experiments at realistic conditions offer a more conservative and reliable assessment compared to previously reported methods. This work supports the potential of functionalized MXenes as promising materials for efficient adsorption of various heavy metal cations in wastewater treatment.
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 | 0.001 | 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 | 0.000 |
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".