Removal of micro- and nano-plastics from aqueous matrices using modified biochar – A review of synthesis, applications, interaction, and regeneration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Modified biochars are used for efficient removal of micro- and nano-plastics from water matrices. • Synthesis techniques for biochar consist chemical functionalization and nanoparticle integration. • The interaction mechanisms between modified biochar and microplastics are critical water treatment. • Critical evaluation of challenges in biochar regeneration is also crucial for sustainable reuse. The widespread contamination of aquatic environments by micro- and nano-plastics has become a global environmental concern that is demanding effective remediation strategies. The main objective of this paper is to find the application of modified biochar in micro- and nano-plastic removal from aqueous environments. In this paper, the synthesis of modified biochar, encompassing various modification techniques such as chemical functionalization, physical activation, and nanoparticle incorporation, is systematically explored. The intricate interaction mechanisms between modified biochar and microplastics are dissected by considering physical adsorption, chemical interactions, electrostatic forces, and hydrophobic interactions. The regeneration of biochar for repeated use is critically evaluated by emphasizing the challenges associated with structural changes and the loss of functional groups during regeneration processes. This review paper integrates findings from recent studies and identifies research gaps by offering insights into the optimization of biochar-based materials for sustainable and efficient removal of microplastics from aqueous matrices. It also guides future research endeavors and technological advancements in plastic pollution mitigation using sustainable materials.
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.
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) | 0.001 | 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 it