Liquid metal compartmented by polyphenol‐mediated nanointerfaces enables high‐performance thermal management on electronic devices
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
Abstract The exponentially increasing heat generation in electronic devices, induced by high power density and miniaturization, has become a dominant issue that affects carbon footprint, cost, performance, reliability, and lifespan. Liquid metals (LMs) with high thermal conductivity are promising candidates for effective thermal management yet are facing pump‐out and surface‐spreading issues. Confinement in the form of metallic particles can address these problems, but apparent alloying processes elevate the LM melting point, leading to severely deteriorated stability. Here, we propose a facile and sustainable approach to address these challenges by using a biogenic supramolecular network as an effective diffusion barrier at copper particle‐LM (EGaIn/Cu@TA) interfaces to achieve superior thermal conduction. The supramolecular network promotes LM stability by reducing unfavorable alloying and fluidity transition. The EGaIn/Cu@TA exhibits a record‐high metallic‐mediated thermal conductivity (66.1 W m −1 K −1 ) and fluidic stability. Moreover, mechanistic studies suggest the enhanced heat flow path after the incorporation of copper particles, generating heat dissipation suitable for computer central processing units, exceeding that of commercial silicone. Our results highlight the prospects of renewable macromolecules isolated from biomass for the rational design of nanointerfaces based on metallic particles and LM, paving a new and sustainable avenue for high‐performance thermal management. image
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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 | 0.001 |
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