Modeling and Optimization of Drug Release from Diffusion-Controlled Spherical Devices
Bibliographic record
Abstract
This work presents an optimization approach for achieving desirable drug release from diffusion-controlled spherical devices. A mathematical model was established for description of drug release in these devices. Initial drug concentration and diffusivity profiles were optimized using a mixed Newton-Tikhonov regularization method to study various targeted release performances. Pseudo constant release, linear decrease release and linear increase followed by a constant release profiles were achieved under constant diffusivity with optimized initial drug concentration distributions, while the diffusivity profiles in devices with different initial concentration profiles were optimized to establish a pseudo constant release profile. The results show that the targeted drug release in the spherical devices can be fulfilled by optimizing the initial drug concentration or diffusivity profile. Moreover, burst effects could be minimized by maintaining low or no drug at the outer layer of the spherical devices with optimized diffusivity profiles.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".