Dynamic Liposome Sensing Platform to Wirelessly Ensure Nucleic Acid Encapsulation via Non‐Contact Perception
Bibliographic record
Abstract
Abstract The demand for a high‐throughput and noncontact monitoring system to guarantee the payload of nucleic acid in liposomes is rapidly increasing for raising efficiency in gene therapeutics. Herein, inspired by electroreceptors of elasmobranch fishes, a dynamic liposome sensing (DLs) platform is developed by implementing the electret layer (CYTOP)‐coated single‐walled carbon nanotube‐based thin film transistor ( e SWCNT‐TFT) which can monitor differences of the net‐charge on deoxyribonucleic acid (DNA)‐loaded liposomes. The SWCNT‐TFTs are roll‐to‐roll (R2R) printed on plastic film and then, simply laminated by the droplet microfluidic chip to optimize the aqueous droplet lengths by controlling a ratio of injecting speed between oil to aqueous solution. The buffer solution, DNA‐free liposomes, and DNA‐loaded liposomes respectively induced different electrostatic potentials on e SWCNT‐TFTs without direct contact with the electret layer, thereby shifting the threshold voltage (V th ). The DLs platform's integrated wireless communication module can monitor DNA‐loaded liposome droplets with encapsulation efficiency of up to 87.3 ± 3.2% with a sensitivity of 18.61 nA ppm −1 per single droplet at a flow rate of 1 µL min −1 . It can be scaled up by adding more microfluidic droplet channels on e SWCNT‐TFT arrays, making it especially useful for in‐situ checks of messenger ribonucleic acid (mRNA)‐based vaccines just before bottling.
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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".