Expandable Polymer Enabled Wirelessly Destructible High‐Performance Solid State Electronics
Bibliographic record
Abstract
In today's digital age, the increasing dependence on information also makes us vulnerable to potential invasion of privacy and cyber security. Consider a scenario in which a hard drive is stolen, lost, or misplaced, which contains secured and valuable information. In such a case, it is important to have the ability to remotely destroy the sensitive part of the device (e.g., memory or processor) if it is not possible to regain it. Many emerging materials and even some traditional materials like silicon, aluminum, zinc oxide, tungsten, and magnesium, which are often used for logic processor and memory, show promise to be gradually dissolved upon exposure of various liquid medium. However, often these wet processes are too slow, fully destructive, and require assistance from the liquid materials and their suitable availability at the time of need. This study shows Joule heating effect induced thermal expansion and stress gradient between thermally expandable advanced polymeric material and flexible bulk monocrystalline silicon (100) to destroy high‐performance solid state electronics as needed and under 10 s. This study also shows different stimuli‐assisted smartphone‐operated remote destructions of such complementary metal oxide semiconductor electronics.
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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".