Advancements in biosorbent green technologies for cadmium removal: A bibliometric analysis and review (2019–2024)
Bibliographic record
Abstract
Abstract This paper provides an in‐depth exploration of recent developments in biosorbent technologies for the removal of cadmium from aqueous solutions. Through a comprehensive bibliometric analysis, we identify key trends and advancements across four primary methodologies: physical, chemical, biological, and nanomaterial‐based approaches. Physical methods, utilizing natural materials like green algae and ion‐imprinted bagasse, offer cost‐effective and eco‐friendly solutions with moderate removal efficiencies (up to 111 mg/g). Chemical methods, including iminodiacetic acid (IDAA) functionalized loofah sponge and Salean polysaccharide, achieve high removal efficiencies (up to 170.1 mg/g) but involve complex preparation processes. Biological methods leverage the natural biosorption capacities of microorganisms and plants, with Bifidobacterium longum and chitosan achieving up to 99.11% removal efficiency. These methods are sustainable and support regeneration and reuse. Nanomaterial‐based approaches exhibit the highest cadmium removal efficiencies, with electro spun chitosan/phosphorylated nanocellulose reaching 232.55 mg/g, benefiting from their high surface area and functionalization potential. The review underscores the significant progress made in optimizing biosorbent technologies for cadmium removal, highlighting the strengths and limitations of each method. Nanomaterial‐based approaches emerge as the most effective, while chemical and biological methods offer high performance and sustainability, respectively.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.041 | 0.052 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".