Globalization and Policy Diffusion: Explaining Three Decades of Liberalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We report the development of low-cost triboelectric nanogenerators (TENGs) based on polypropylene (PP) fabrics formulated via an inexpensive melt-blowing process with an output voltage as high as 50 V. By disinfection methods such as exposure to steam, ethanol, and dry heat at 75 °C, the commercial medical masks and N95 filtering facepiece respirators (FFRs) can be reused to fabricate PP fiber based TENGs, which provide a novel regime for energy-harvesting devices based on reusable materials. As a power source, the output of one TENG can drive 15 serially connected light-emitting diodes (LEDs) or a commercial electric calculator. PP fabric TENGs can also work as self-powered sensors for the high-sensitivity detection of mechanical impact. We provide examples where the TENG is used to detect biomechanical motion such as that associated with the extension of an elbow, the touch of a finger, the impact of footsteps, and the bending of a knee without an external power supply. Most importantly, these PP fabrics for TENGs can be obtained from decontaminated medical masks that are generated as tremendous wastes every day, which provide a great potential as sustainable energy. These properties suggest that PP fabric based TENGs are promising for harvesting energy from biological systems and that they may facilitate the large-scale production of a new range of inexpensive self-powered multifunctional wearable sensors for applications in healthcare, security, and information networks.
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.
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 it