Sustainability Assessment of Nanoscale Zerovalent Iron Production Methods
Bibliographic record
Abstract
Nanoscale zerovalent iron (nZVI) is the main nanomaterial used in remediation processes. The aim of this study was to evaluate the sustainability of the nZVI production methods. For this, nine nZVI production methods were selected for analysis. Four kinds of life cycle analysis were performed: life cycle assessment (LCA), life cycle cost (LCC), social life cycle assessment (S-LCA), and life cycle sustainability assessment (LCSA). The LCA was performed in the SimaPro® program using the Impact 2002+ method. The LCC was also performed in SimaPro by developing a cost analysis method. For the social analysis, equations were used to calculate the social life cycle score. For the LCSA, the results of the life cycle analyses were normalized, and a weighting factor was defined on the basis of multi-criteria analysis methods. The sustainability score was calculated on the basis of a linear additive model. Scenario and sensitivity analyses were performed, and Monte Carlo simulation was used to quantify the uncertainty of the results. The system limits the stages of raw material extraction, transportation, and nZVI production. The functional unit was 1.00 kg of nZVI produced. The green synthesis method was found to be the most sustainable method, classified as highly sustainable, whereas the microemulsion method was found to be the least sustainable method, classified as unsustainable. The scenario analysis showed that overall the Swiss and Canadian scenarios have the highest sustainability index scores, whereas the Indian scenario has the lowest. In addition, the results show low sensitivity to weighting factor variation. In general, this study contributed to the state-of-the-art LCSA application on nanomaterials used in remediation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".