Bench‐Top Fabrication of Hierarchically Structured High‐Surface‐Area Electrodes
Bibliographic record
Abstract
Abstract Fabrication of hierarchical materials, with highly optimized features from the millimeter to the nanometer scale, is crucial for applications in diverse areas including biosensing, energy storage, photovoltaics, and tissue engineering. In the past, complex material architectures have been achieved using a combination of top‐down and bottom‐up fabrication approaches. A remaining challenge, however, is the rapid, inexpensive, and simple fabrication of such materials systems using bench‐top prototyping methods. To address this challenge, the properties of hierarchically structured electrodes are developed and investigated by combining three bench‐top techniques: top‐down electrode patterning using vinyl masks created by a computer‐aided design (CAD)‐driven cutter, thin film micro/nanostructuring using a shrinkable polymer substrate, and tunable electrodeposition of conductive materials. By combining these methods, controllable electrode arrays are created with features in three distinct length scales: 40 μm to 1 mm, 50 nm to 10 μm, and 20 nm to 2 μm. The electrical and electrochemical properties of these electrodes are analyzed and it is demonstrated that they are excellent candidates for next generation low‐cost electrochemical and electronic devices.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".