Microwave-assisted synthesis of carbon-based nanomaterials from biobased resources for water treatment applications: emerging trends and prospects
Bibliographic record
Abstract
Carbon-based nanomaterials have drawn significant interest as desirable nanomaterials and composites for the adsorptive removal of various classes of pollutants from water owing to their versatile physicochemical properties. The underlying sorption mechanisms serve as the bedrock for the development of carbonaceous adsorbents for various target pollutants. Microwave-assisted synthesis can be regarded as a recent and well-advanced technique for the development of carbon-based nanomaterials, and the use of biobased materials/wastes/residues conforms with the concept of green and sustainable chemistry. For advancements in carbon-based functional nanomaterials and their industrial/field applications, it is essential to fully comprehend the sorption performance and the selective/non-selective interaction processes between the contaminants and sorbents. In this regard, research on the development of carbon-based nanomaterials for the adsorption of chemical contaminants, both organic and inorganic, in water has made considerable strides as discussed in this review. However, there are still several fundamental hurdles associated with microwave-assisted chemical synthesis and commercial/industrial scale-up applications in nano-remediation. The challenges, benefits, and prospects for further research and development of carbon-based nanomaterials/nanocomposites for the purification of water are also discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".