An analysis of the piezoresistive response of n-type, bottom-up, functionalized silicon microwires
Bibliographic record
Abstract
As the world’s population increases, the demand for energy also grows. The strain on our limited resources of fossil fuels is unsustainable in the long term. An alternative, renewable method of energy generation must be implemented. Solar energy has good potential as an environmentally sound, unlimited energy source, but solar devices are not yet able to efficiently store energy for later use. A device has been proposed which uses direct sunlight to split water into hydrogen and oxygen. The hydrogen can then be harvested and stored as fuel, solving the question of how to effectively store energy generated during times of peak sunlight for use when sunlight levels are low. The prototype device incorporates arrays of doped silicon microwires which function as light absorbers and current-carriers, driving the chemical reactions that evolve hydrogen from water. This work aims to quantify and characterize the reduction in microwire resistivity that is achievable through application of silicon’s piezoresistive properties. Silicon displays a change in electrical resistance as a function of applied mechanical strain. This electromechanical effect has been studied extensively in bulk and top-down (etched) microstructures, but studies on microstructures grown bottom-up have been limited. A simple method is presented for piezoresistive characterization of individual, released, bottom-up silicon microwires. It is shown that these n-type microwires display a consistent negative piezoresistive response which increases in magnitude with increasing doping concentration. It was found that harnessing the piezoresistive response of moderately-doped (∼10^17 cm^−3) n-type wires allowed for a maximum observed reduction in resistivity of 49%, which translated to a 1% reduction in overall system resistance of a prototype unit cell of the artificial photosynthesis device, if all other components therein remained unchanged.
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".