Application of Non-Destructive Testing Techniques (NDTT) to Characterize Nanocarriers Used for Drug Delivery: A Mini Review
Bibliographic record
Abstract
The synthesis of tailored and highly engineered multifunctional pharmaceutical nanocarriers is an emerging field of study in drug delivery applications. They have a high surface-area-to-volume ratio, aiding the targeted drug’s biodistribution and pharmacokinetic properties. Therefore, the characterization of nanocarriers is critical for understanding their physicochemical properties, which significantly impact their molecular and systemic functioning. To achieve specific goals, particle size, surface characteristics, and drug release properties of nanocarriers must be managed. This mini review provides an overview of the applications of non-destructive testing techniques (NDTT) to reveal the characteristics of nanocarriers, considering their surface charge, porosity, size, morphology, and crystalline organization. The compositional and microstructural characterization of nanocarriers through NDTT, such as dynamic light scattering, X-ray diffraction, confocal laser scanning microscopy, ultraviolet-visible spectroscopy, scanning electron microscopy, atomic force microscopy, and nuclear magnetic resonance spectroscopy, have been comprehensively reviewed. Furthermore, NDTT is only used to characterize physicochemical parameters related to the physiological performance of nanocarriers but does not account for nanocarrier toxicity. Hence, it is highly recommended that in the future, NDTT be developed to assess the toxicity of nanocarriers. In addition, by developing more advanced, effective, and precise techniques, such as machine vision techniques using artificial intelligence, the future of using NDTT for nanocarrier characterization will improve the evaluation of internal quality parameters.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".