Facet-dependent adsorption and transformation of ionic gold on Cu2O particles induced by vacancy defects in aqueous environments
Bibliographic record
Abstract
Metal-oxide-based nanoparticles (MONPs) can interact with ionic gold (AuCl 4 − ) in the surrounding environment to form gold nanoparticles, thereby influencing the environmental fate and toxicity of AuCl 4 − . However, the mechanisms by which intrinsic properties of MONPs, e.g., exposed facets and oxygen vacancy defects (OVDs), affect the immobilization and transformation of AuCl 4 − remain poorly understood. Herein, the adsorption, reduction, and transformation of AuCl 4 − on three types of facet-exposed Cu 2 O nanoparticles containing OVDs, a widely used class of engineered MONPs, were systematically investigated in various aquatic environments. AuCl 4 − adsorption was found to be facet-dependent under varying environmentally relevant hydrochemical conditions, following the order: (111) facet > (100) & (111) facet > (100) facet. OVDs, the key factor driving these differences, could facilitate the dechlorination of AuCl 4 − on different exposed facets of Cu 2 O, thereby promoting the formation of gold nanoparticles. In particular, the variation in surface hydroxyl density and dechlorination energy barriers induced by OVDs are responsible for the selective adsorption behavior of Cu 2 O exposed facets. Moreover, Cu 2 O exhibited the facet-dependent dissolution behavior during the adsorption process, potentially posing risks to ecosystems and human health. These findings provide valuable insights into the geochemical cycling of AuCl 4 − , which may inform strategies for secondary resource recovery and the safe design of nanomaterials aimed at the immobilization of ionic gold in aqueous environment. • Facet-dependent adsorption, reduction, and transformation of AuCl 4 − on Cu 2 O are dominated by oxygen vacancy defects. • Copper and hydroxyl sites promote AuCl 4 − dechlorination. • Cu 2 O exhibits facet-dependent dissolution during AuCl 4 − adsorption.
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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".