Flexible, anti-damage, and non-contact sensing electronic skin implanted with MWCNT to block public pathogens contact infection
Bibliographic record
Abstract
If a person comes into contact with pathogens on public facilities, there is a threat of contact (skin/wound) infections. More urgently, there are also reports about COVID-19 coronavirus contact infection, which once again reminds that contact infection is a very easily overlooked disease exposure route. Herein, we propose an innovative implantation strategy to fabricate a multi-walled carbon nanotube/polyvinyl alcohol (MWCNT/PVA, MCP) interpenetrating interface to achieve flexibility, anti-damage, and non-contact sensing electronic skin (E-skin). Interestingly, the MCP E-skin had a fascinating non-contact sensing function, which can respond to the finger approaching 0-20 mm through the spatial weak field. This non-contact sensing can be applied urgently to human-machine interactions in public facilities to block pathogen. The scratches of the fruit knife did not damage the MCP E-skin, and can resist chemical corrosion after hydrophobic treatment. In addition, the MCP E-skin was developed to real-time monitor the respiratory and cough for exercise detection and disease diagnosis. Notably, the MCP E-skin has great potential for emergency applications in times of infectious disease pandemics. Electronic Supplementary Material: Supplementary material (fabrication of MCP E-skin, laser confocal tomography, parameter optimization, mechanical property characterization, finite element simulation, sensing mechanism, signal processing) is available in the online version of this article at 10.1007/s12274-021-3831-z.
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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 | 0.000 |
| 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) | 0.001 | 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".