Phytoextraction as a tool for green chemistry
Bibliographic record
Abstract
Abstract The unique chemical and physical properties of metals mean that they are extensively utilized by industry in a huge variety of applications, including electronics, materials, industrial catalysts and chemicals. The increased consumer demand from a growing population worldwide with rising aspirations for a better life has resulted in concerns over the security of supply and accessibility of these valuable elements. As such, there is a growing need to develop alternative methods to recover them from waste repositories, current or historic, both for hazard avoidance and potentially, as a new source of metals for industry. Phytoextraction (the use of plants for the recovery of metals from waste repositories) is a green and novel technique for metal recovery, which, if done with the goal of resource supply rather than hazard mitigation, is termed “phytomining”. The ability for plants to form metallic nanoparticles as a consequence of phytoextraction could make the recovered metal ideally suited for utilization in green chemical technologies, such as catalysis. This review focuses on a multidisciplinary approach to elemental sustainability and highlights important aspects of metal lifecycle analysis, metal waste sources (including mine tailings), phytoextraction and potential green chemical applications that may result from the integration of these approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".