Nanoparticle heterogeneity: an emerging structural parameter influencing particle fate in biological media?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drug nanocarriers' surface chemistry is often presumed to be uniform. For instance, the polymer surface coverage and distribution of ligands on nanoparticles are described with averaged values obtained from quantification techniques based on particle populations. However, these averaged values may conceal heterogeneities at different levels, either because of the presence of particle sub-populations or because of surface inhomogeneities, such as patchy surfaces on individual particles. The characterization and quantification of chemical surface heterogeneities are tedious tasks, which are rather limited by the currently available instruments and research protocols. However, heterogeneities may contribute to some non-linear effects observed during the nanoformulation optimization process, cause problems related to nanocarrier production scale-up and correlate with unexpected biological outcomes. On the other hand, heterogeneities, while usually unintended and detrimental to nanocarrier performance, may, in some cases, be sought as adjustable properties that provide NPs with unique functionality. In this review, results and processes related to this issue are compiled, and perspectives and possible analytical developments are discussed.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it