Multi‐Level High Entropy‐Dissipative Structure Enables Efficient Self‐Decoupling of Triple Signals
Bibliographic record
Abstract
Abstract The theory of high entropy‐dissipative structure is confined to high‐entropy alloys and their oxide materials under harsh conditions, but it is very difficult to obtain high entropy‐dissipative structure for smart sensors based on polymers and metal oxides under mild conditions. Moreover, multiple signal coupling effect heavily hinder the sensor applications, and current multimodal integrated devices can solve two signal‐decoupling, but need very complicated process way. In this work, new synthesis concept is the first time to fabricate high entropy‐dissipative conductive layer of smart sensors with triple‐signal response and self‐decoupling ability within poly‐pyrrole/zinc oxide (PPy/ZnO) system. The sensor (SPZ 20 ) amplifies pressure (17.54%/kPa) and gas (0.37%/ppm), reduces humidity (0.41%/% RH) and temperature (0.12%/°C) signals, simultaneously achieving the triple self‐decoupling effect of pressure and gas in the complex temperature‐humidity field because of the enlarged pressure‐contact area, enhanced gas‐responsive sites, altered vapor path and its own heat insulation function. Additionally, it inherits the strong robustness (500 rubbing, washing, and heating or freezing cycles) and endurance (10 000 photo‐purification cycles) of traditional high‐entropy materials for information transmission and smart alarms in emergencies or harsh environments. This work gives a new insight into the multiple‐signal response and smart flexible electronic design from natural fibers.
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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".