Bibliographic record
Abstract
Heat generation at disk microelectrodes by a high-amplitude (few volt) and high-frequency (0.1-2 GHz) alternating voltage is described. This method allows changing electrode temperature very rapidly and maintaining it well above the boiling point of solution for a very long time without any indication of boiling. The size of the hot zone in solution is determined by the radius of the electrode. There is no obvious limit in regard to the electrode size, so theoretically, by this method, it should be possible to create hot spots that are much smaller than those created with laser beams. That could lead to potential applications in medicine and biology. The heat-generating waveform does not electrically interfere with normal electroanalytical measurements. The noise level at hot microelectrodes is only slightly higher, as compared to normal microelectodes, but diffusion-controlled currents at hot microelectrodes may be up to 7 times higher, and an enhancement of kinetically controlled currents may be even larger. Hot microelectrodes can be used for end-column detection in capillary electrophoresis and for in-line or in vivo analyses. Temperature gradients at hot microelectrodes may exceed 1.5 x 10(5) K/cm, which makes them useful in studies of Soret diffusion and thermoelectric phenomena.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".