Nanoscale Si fishbone structures for manipulating heat transport using phononic resonators for thermoelectric applications
Bibliographic record
Abstract
Abstract Thermoelectric materials have the potential to convert waste heat into electricity, but their thermoelectric efficiency must be improved before they are effective and economically viable. One promising route to improving thermoelectric efficiency in thin-film thermoelectric materials is to reduce the material’s thermal conductivity through nanopatterning the surface. In this work nanoscale phononic resonators are introduced to the surface, and their potential to reduce thermal conductivity is explored via coupled experimental and theoretical techniques. Atomistic modelling is used to predict the dependence of the thermal conductivity on different design parameters and used to guide the design and fabrication of silicon fishbone nanostructures. The nanostructure design incorporates a variation on design parameters such as barb length, width and spacing along the shaft length to enable correlation with changes in thermal conductivity. The thermal characteristics of the nanostructures are investigated experimentally using the spatial resolution of scanning thermal microscopy to correlate changes in thermal conductivity with the changes in the structure parameters. The method developed uses a microheater to establish a temperature gradient along the structure which will be affected by any local variations in thermal conductivity. The impact on the thermal gradient and consequently on the tip temperature is modelled using finite element computer simulations. Experimental changes as small as 7.5% are shown to be detectable in this way. Despite the experimental technique being shown to be able to detect thermal changes far smaller than those predicted by the modelling, no modifications of the thermal conductivity are detected. It is concluded that in order to realise the effects of phononic resonators to reduce thermal conductivity, that much smaller structures with a greater ratio of resonator to shaft will be needed.
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 | 0.000 | 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".