Electrospun Multifunctional Radiation Shielding Nanofibrous Membrane for Daily Human Protection
Bibliographic record
Abstract
Abstract With the widespread utilization of many radiation instruments, people are inadvertently suffering from radiation damage. But nowadays, there is still a lack of a lightweight, comfortable, and efficient wearable radiation‐proof multifunctional composite fibrous membranes in people's lives. To solve this problem, a green and efficient radiation‐resistant nanofibrous membrane consisting of cellulose fluorescent particles (CFP), ZnO‐Bi2O3 nanoparticles, and polyacrylonitrile (PAN) solution is prepared by an electrospinning technology. The microscopic characterization shows that the nanoparticles are uniformly dispersed on the surface of the nanofibrous membrane, forming a dense fibrous structure. The PAN/CFP/ZnO‐Bi2O3 nanofibrous membrane exhibits not only an excellent ultraviolet(UV) interception with an ultraviolet protection factor value of 337.99 ± 83.67, but also a strong shielding effect on low‐energy X‐rays. Simultaneously, it can not only photocatalytically degrade 88.17% of methylene blue solution within 150 min, but also remove 94.16 ± 0.96% of Escherichia coli and 99.07 ± 0.59% of Staphylococcus aureus within 3 h under dark incubation and kill all bacteria within 3 h under light. The adhesion, growth, and proliferation of bone marrow mesenchymal stem cells on the PAN/CFP/ZnO‐Bi2O3 nanofibrous membranes confirm its low cytotoxicity and good biosafety. Thus, it could be expected to be used in daily life products such as UV and X‐rays radiation protective clothing.
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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 | 0.001 | 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".