Multi-Objective Optimization of Graphite Heat Spreader for Portable Systems Applications
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
The advancement made in portable electronic systems has primarily been due to miniaturization of electronic devices. This results in an increase in power density that leads to higher temperatures and formation of hot spots. There is a temperature specification of system surfaces for human comfort (such as the surface close to a keyboard on laptops). The challenge of cooling portable devices is that there is not enough room to accommodate heat sinks. It is therefore important to have heat spreaders that can transfer the heat from critical devices to regions where cooling is available. Traditionally, copper has been the best heat spreader due to its high thermal conductivity. However, copper has a relatively high density and correspondingly high weight. Graphite is a suitable alternative. Recent advances in graphite technology have resulted in fairly high conductivity in the planar directions. In spite of these advances, the cost of graphite is an issue. In this paper, a multi-objective optimization is utilized that considers weight of the graphite heat spreader as objective functions. The data is then compared to published data that utilizes graphite in a laptop.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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