Ballistic Impact of Single Particles Into Gelatin: Experiments and Modeling With Application to Transdermal Pharmaceutical Delivery
Bibliographic record
Abstract
The impact and penetration of high speed particles with the human skin is of interest for targeted drug delivery by transdermal powder injection. However, it is often difficult to perform penetration experiments on dermal tissue using micron scale particles. To address this, a finite element model of the impact and penetration of a 2 μm gold particle into the human dermis was developed and calibrated using experiments found in the literature. Using dimensional analysis, the model was linked to a larger scale steel ball-gelatin system in order to extract key material parameters for both systems and perform impact studies. In this manner, an elastic modulus of 2.25 MPa was found for skin, in good agreement with reported values from the literature. Further gelatin experiments were performed with steel, polymethyl methacrylate, titanium, and tungsten carbide balls in order to determine the effects of particle size and density on penetration depth. Both the finite element model and the steel-gelatin experiments were able to predict the penetration behavior that was found by other investigators in the study of the impact of typical particles used for vaccine delivery into the human dermis. It can therefore be concluded that scaled up systems utilizing ballistic gelatins can be used to investigate the performance of transdermal powder injection technology.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".