Carbonaceous Materials‐Based Photothermal Process in Water Treatment: From Originals to Frontier Applications
Bibliographic record
Abstract
The photothermal process has attracted considerable attention in water treatment due to its advantages of low energy consumption and high efficiency. In this respect, photothermal materials play a crucial role in the photothermal process. Particularly, carbonaceous materials have emerged as promising candidates for this process because of exceptional photothermal performance. While previous research on carbonaceous materials has primarily focused on photothermal evaporation and sterilization, there is now a growing interest in exploring the potential of photothermal effect-assisted advanced oxidation processes (AOPs). However, the underlying mechanism of the photothermal effect assisted by carbonaceous materials remains unclear. This review aims to provide a comprehensive review of the photothermal process of carbonaceous materials in water treatment. It begins by introducing the photothermal properties of carbonaceous materials, followed by a discussion on strategies for enhancing these properties. Then, the application of carbonaceous materials-based photothermal process for water treatment is summarized. This includes both direct photothermal processes such as photothermal evaporation and sterilization, as well as indirect photothermal processes that assisted AOPs. Meanwhile, various mechanisms assisted by the photothermal effect are summarized. Finally, the challenges and opportunities of using carbonaceous materials-based photothermal processes for water treatment are proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".