Microfabricated, silicon devices with nanowells and nanogap electrodes: a platform for dielectric spectroscopy with silane-tunable response
Bibliographic record
Abstract
Combining the advantages of nanogap devices and impedance spectroscopy can potentially provide a platform for dielectric spectroscopy with widely ranging applications—from fundamental studies at the nanoscale and surfaces to label free and selective sensors. The present study characterizes the impedance response of a microfabricated, silicon-based device with a large array of nanowells surrounded by annular, nanogap detection regions. Device impedance is measured versus frequency over 5 orders in a variety of organic solvents with dielectric constants ranging over 2 orders. The study finds two key results. First, an equivalent R / C circuit model is found to compare favorably with device impedance response over these wide ranges of parameters. Importantly, the model correlates with structure of the nanogap device, which suggests that such a structure-impedance response approach can help guide modeling of other devices geometries. Second, the model points to—and data confirm—correlation between nanogap device response and dielectric constant of materials in the nanogaps, particularly at low frequencies. In addition, the correlation is significantly modified by robust, silane functionalization of the devices due to a large surface-to-volume ratio of the nanogaps. These results demonstrate that nanogap impedance spectroscopy using microfabricated/silanized silicon devices is a robust and versatile platform for dielectric spectroscopy of materials on the nanoscale and on surfaces.
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) | 0.000 | 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 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".