Shape-Dependent Surface Reactivity and Antimicrobial Activity of Nano-Cupric Oxide
Bibliographic record
Abstract
Shape of engineered nanomaterials (ENMs) can be used as a design handle to achieve controlled manipulation of physicochemical properties. This tailored material property approach necessitates the establishment of relationships between specific ENM properties that result from such manipulations (e.g., surface area, reactivity, or charge) and the observed trend in behavior, from both a functional performance and hazard perspective. In this study, these structure-property-function (SPF) and structure-property-hazard (SPH) relationships are established for nano-cupric oxide (n-CuO) as a function of shape, including nanospheres and nanosheets. In addition to comparing these shapes at the nanoscale, bulk CuO is studied to compare across length scales. The results from comprehensive material characterization revealed correlations between CuO surface reactivity and bacterial toxicity with CuO nanosheets having the highest surface reactivity, electrochemical activity, and antimicrobial activity. While less active than the nanosheets, CuO nanoparticles (sphere-like shape) demonstrated enhanced reactivity compared to the bulk CuO. This is in agreement with previous studies investigating differences across length-scales. To elucidate the underlying mechanisms of action to further explain the shape-dependent behavior, kinetic models applied to the toxicity data. In addition to revealing different CuO material kinetics, trends in observed response cannot be explained by surface area alone. The compiled results contribute to further elucidate pathways toward controlled design of ENMs.
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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 | 0.003 |
| 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".