High sensitivity detection of nanoparticles permeation through polymer membranes: A physico-chemical and nuclear imaging measurement approach
Bibliographic record
Abstract
Diffusion cells are devices made of donor and acceptor compartments (DC and AC), separated by a membrane. They are widely used in pharmaceutical, cosmetic, toxicology, and protective equipment tests (e.g., gloves) to measure the kinetics of permeants (molecules and nanoparticles) across biological membranes as the skin. However, rarely is the concentration of permeants in the AC measured in continuous or in real-time, and this limitation leads to significant discrepancies in the calculations of kinetic parameters that define the permeation mechanisms. In this study, a diffusion cell compatible with positron emission tomography was used to measure the permeation kinetics of nanoparticles across glove membranes. The technology allows for the measurement of nanoparticle concentration in real-time in the two compartments (DC and AC) and at a detection sensitivity several orders of magnitude higher compared with conventional spectroscopies, thus allowing a much more precise extraction of kinetic parameters. Ultra-small (<10 nm) gold nanoparticles were used as a model nanoparticle contaminant. They were radiolabeled, and their diffusion kinetics was measured in continuous through latex and nitrile polymer membranes. Permeation profiles were recorded at sub-nanomolar sensitivity and in real-time, thus allowing the high precision extraction of kinetic permeation parameters. The technology, methodology, and data extraction process developed in this work could be applied to measure in real-time the kinetics of diffusion of a whole range of potentially toxic molecules and nanoparticles across polymer membranes, including glove membranes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".