Electrochemical Impedance Spectroscopy: a tool on the electrochemical investigations
Bibliographic record
Abstract
Since 2000 the number of publications related to Electrochemical Impedance Spectroscopy -EIS has been gradually increasing, going from just over 81 publications in 2000 to over 1800 publications in 2019. In 2020, more than 1028 publications are seen in just 4 months, that is, more than 18,000 publications in the last 20 years. EIS is a very powerful tool in the study of several areas of knowledge, such as chemistry, physics, biology etc. On the other hand, EIS is still considered a difficult technique, due to the mathematical concepts and modeling involved in the analysis of experimental data. Thus, this paper aims to introduce the EIS technique in a more clear and simple way, providing subsidies to all entities involved in scientific research and concisely showing the main points associated with EIS mathematics and physics. Besides, the paper shows how to analyze whether the impedance data obtained is acceptable and how to check its reliability using the Lissajous plots, the Kramers-Kronig transform, and the chi-square test.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".