Lattice Boltzmann modeling of heat conduction enhancement by colloidal nanoparticle deposition in microporous structures
Bibliographic record
Abstract
Drying of colloidal suspension towards the exploitation of the resultant nanoparticle deposition has been applied in different research and engineering fields. Recent experimental studies have shown that neck-based thermal structure (NTS) by colloidal nanoparticle deposition between microsize filler particle configuration (FPC) can significantly enhance vertical heat conduction in innovative three-dimensional chip stacks [Brunschwiler et al., J. Electron. Packag. 138, 041009 (2016)10.1115/1.4034927]. However, an in-depth understanding of the mechanisms of colloidal liquid drying, neck formation, and their influence on heat conduction is still lacking. In this paper, using the lattice Boltzmann method, we model neck formation in FPCs and evaluate the thermal performances of resultant NTSs. The colloidal liquid is found drying continuously from the periphery of the microstructure to its center with a decreasing drying rate. With drying, more necks of smaller size are formed between adjacent filler particles, while fewer necks of larger size are formed between filler particle and the top/bottom plate of the FPCs. The necks, forming critical throats between the filler particles, are found to improve the heat flux significantly, leading to an overall heat conduction enhancement of 2.4 times. In addition, the neck count, size, and distribution as well as the thermal performance of NTSs are found to be similar for three different FPCs at a constant filler particle volume fraction. Our simulation results on neck formation and thermal performances of NTSs are in good agreement with experimental results. This demonstrates that the current lattice Boltzmann models are accurate in modeling drying of colloidal suspension and heat conduction in microporous structures, and have high potentials to study other problems such as surface coating, salt transport, salt crystallization, and food preserving.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".