Thermal resistance-based bounds for the effective conductivity of composite thermal interface materials
Bibliographic record
Abstract
Thermally enhanced greases made of dispersions of small conductive particles suspended in fluidic polymers can offer significant advantages when used as a thermal interface material (TIM) in microelectronics cooling applications. The following study presents the application of two simple theorems for establishing bounds on the effective thermal conductivity of such inhomogeneous media. An upper bound is established when isotherms are assumed perpendicular to the direction of heat flow through the material. In a similar manner, a lower bound is established when adiabats are assumed parallel to the direction of heat flow. As an example of the application of these theorems, the TIM is assumed to be composed of a cubic array of uniform spheres in a surrounding medium. In most instances, a geometric mean of the bounding solutions determined for this case gives good agreement with experimental data available in the literature. Numerical simulations of a spherical particle in a unit cube cell confirm the validity of the model. This model is not applicable to systems in which the discontinuous phase is either well-connected throughout or has settled. The potential of extending this preliminary thermal resistance-based approach to investigate other geometries and effects associated with distribution, orientation, and boundary resistance is discussed
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.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".