How to unravel the chemical structure and component localization of individual drug-loaded polymeric nanoparticles by using tapping AFM-IR
Bibliographic record
Abstract
AFM-IR is a photothermal technique that combines AFM and infrared (IR) spectroscopy to unambiguously identify the chemical composition of a sample with tens of nanometer spatial resolution. So far, it has been successfully used in contact mode in a variety of applications. However, the contact mode is unsuitable for soft or loosely adhesive samples such as polymeric nanoparticles (NPs) of less than 200 nm of wide interest for biomedical applications. We describe here the theoretical basis of the innovative tapping AFMIR mode that can address novel challenges in imaging and chemical mapping. The new method enables gaining information not only on NP morphology and composition, but also reveals drug location and core-shell structures. Whereas up to now the locations of NP components could only be hypothesized, tapping AFM-IR allows accurately visualizing both the location of the NPs' shells and that of the incorporated drug, pipemidic acid. The preferential accumulation of the drug in the NPs' top layers was proved, despite its low concentration (<1 wt%). These studies pave the way towards the use of tapping AFM-IR as a powerful tool to control the quality of NP formulations based on individual NP detection and component quantification.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".