In Silico Characterization of Nanomaterials
Bibliographic record
Abstract
Nanomaterials (NMs) and nanoparticles (NPs) lie at the core of many technological applications in medicine and pharmacology, as well as in the food, agriculture, electronics, and energy industries. They are also released in the environment through natural and incidental pathways. Despite our heavy reliance on NMs, the potential risk they pose to the environment and to the biological systems is still of major concern In this work, we evaluate intrinsic and extrinsic NM descriptors to aid in the prediction of biomolecular interactions at the surface of NMs and development of structure-activity relationships between their physicochemical characteristics and their toxicity Intrinsic properties are solely based on the molecular and electronic structure of the NM, while the extrinsic properties describe a NM that comes in contact with a protein in a solvent. The NM models for the calculation of intrinsic descriptors are associated with the core of the NM that is described as a periodic bulk material In this work, we present a database of calculated descriptors for several common NMs. The provided list contained various samples of NP (metals, oxides, minerals, polymers, and carbon-based compounds such as carbon nanotubes (CNTs) and graphene sheets) that were used in toxicological experiments.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".