Interfacial Charge Transfer between CdTe Quantum Dots and Gram Negative Vs Gram Positive Bacteria
Bibliographic record
Abstract
Oxidative toxicity of semiconductor and metal nanomaterials to cells has been well established. However, it may result from many different mechanisms, some requiring direct cell contact and others resulting from the diffusion of reactive species in solution. Published results are contradictory due to differences in particle preparation, bacterial strain, and experimental conditions. It has been recently found that C(60) nanoparticles can cause direct oxidative damage to bacterial proteins and membranes, including causing a loss of cell membrane potential (depolarization). However, this did not correlate with toxicity. In this study we perform a similar analysis using fluorescent CdTe quantum dots, adapting our tools to make use of the particles' fluorescence. We find that two Gram positive strains show direct electron transfer to CdTe, resulting in changes in CdTe fluorescence lifetimes. These two strains also show changes in membrane potential upon nanoparticle binding. Two Gram negative strains do not show these effects-nevertheless, they are over 10-fold more sensitive to CdTe than the Gram positives. We find subtoxic levels of Cd(2+) release from the particles upon irradiation of the particles, but significant production of hydroxyl radicals, suggesting that the latter is a major source of toxicity. These results help establish mechanisms of toxicity and also provide caveats for use of certain reporter dyes with fluorescent nanoparticles which will be of use to anyone performing these assays. The findings also suggest future avenues of inquiry into electron transfer processes between nanomaterials and bacteria.
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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 | 0.001 |
| 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) | 0.001 | 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".