Environmental, energy and economic assessment of four recyclable nano-sized semiconductor photocatalysts to remove 2,4-dichlorophenol
Bibliographic record
Abstract
As environmental concerns continue to grow, life cycle assessment \LCA) has become a vital tool for assessing the sustainability of different technologies. Photocatalysts are particularly important for water treatment due to their effectiveness in removing organic pollutants. This study investigated the sustainability of four types of photocatalysts in the removal of 2,4-DCP from aqueous solutions using LCA and evaluated their environmental impacts, energy consumption, and economic costs. The ecological consequences of these photocatalysts were evaluated using SimaPro software, which encompasses 18 environmental impact categories and Cumulative Energy Demand (CED). The results of this study indicated that, in most environmental indicators, the rGH photocatalyst had the highest environmental impact, except for terrestrial biotoxicity (TE) and human non-carcinogenic toxicity (HNCT). In contrast, the 10 % rGH-Fe 3 O 4 @SnO 2 /Ag photocatalyst composition demonstrated the lowest environmental impact across all evaluated indicators. Moreover, the analysis of the four photocatalytic processes revealed a significant impact on human health. Among these, the 10 % rGH-Fe 3 O 4 @SnO 2 /Ag photocatalyst demonstrated the highest efficiency, achieving 92.64 %. In terms of energy consumption, the 10 % rGH-Fe 3 O 4 @SnO 2 /Ag required the lowest energy, with a cumulative energy demand (CED) of 0.27 GJ, while rGH had the highest CED at 1.29 GJ. Regarding cost, the Fe 3 O 4 @SnO 2 /Ag option was found to be the most economical, costing $768.47, whereas rGH was the most expensive at $7524.57. In conclusion, the use of the 10 % rGH-Fe 3 O 4 @SnO 2 /Ag photocatalyst is a more effective, cost-efficient, and environmentally friendly method for the removal of 2,4-DCP from aqueous solutions compared to other investigated methods.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".