Perspective and Roadmap of Energy-Efficient Desalination Integrated with Nanomaterials
Bibliographic record
Abstract
Desalination is known to be one of the most sustainable solutions for water treatment to provide fresh water for many water-stressed communities and industrial sectors. As the integration of nanotechnology with desalination processes is most likely to dominate the future research attention and desalination market, this manuscript presents state-of-the-art review on the enabling of cutting edge desalination technology integrated with nanomaterials. The technological needs and future perspective, which include the challenges and opportunities of nano-enabled desalination processes are critically reviewed in this contribution. Recent developments and findings on the state-of-the-art nano-enabled desalination processes are discussed. Key issues such as scale-up, economic competitiveness, potential environmental impacts and energy consumption are also reviewed. This minireview aims to provide directions and guidelines to the desalination research community regarding the future outlook and roadmap of the application of nanotechnology in desalination processes at the bench scale and commercialization level.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".