Cytotoxicity of surface-functionalized silicon and germanium nanoparticles: the dominant role of surface charges
Bibliographic record
Abstract
Although it is frequently hypothesized that surface (like surface charge) and physical characteristics (like particle size) play important roles in cellular interactions of nanoparticles (NPs), a systematic study probing this issue is missing. Hence, a comparative cytotoxicity study, quantifying nine different cellular endpoints, was performed with a broad series of monodisperse, well characterized silicon (Si) and germanium (Ge) NPs with various surface functionalizations. Human colonic adenocarcinoma Caco-2 and rat alveolar macrophage NR8383 cells were used to clarify the toxicity of this series of NPs. The surface coatings on the NPs appeared to dominate the cytotoxicity: the cationic NPs exhibited cytotoxicity, whereas the carboxylic acid-terminated and hydrophilic PEG- or dextran-terminated NPs did not. Within the cationic Si NPs, smaller Si NPs were more toxic than bigger ones. Manganese-doped (1% Mn) Si NPs did not show any added toxicity, which favors their further development for bioimaging. Iron-doped (1% Fe) Si NPs showed some added toxicity, which may be due to the leaching of Fe(3+) ions from the core. A silica coating seemed to impart toxicity, in line with the reported toxicity of silica. Intracellular mitochondria seem to be the target for the toxic NPs since a dose-, surface charge- and size-dependent imbalance of the mitochondrial membrane potential was observed. Such an imbalance led to a series of other cellular events for cationic NPs, like decreased mitochondrial membrane potential (ΔΨm) and ATP production, induction of ROS generation, increased cytoplasmic Ca(2+) content, production of TNF-α and enhanced caspase-3 activity. Taken together, the results explain the toxicity of Si NPs/Ge NPs largely by their surface characteristics, provide insight into the mode of action underlying the observed cytotoxicity, and give directions on synthesizing biocompatible Si and Ge NPs, as this is crucial for bioimaging and other applications in for example the field of medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".