Charge Extraction by Linearly Increasing Voltage (CELIV) Method for Investigation of Charge Carrier Transport and Recombination in Disordered Materials
Bibliographic record
Abstract
The technique of charge carrier extraction by linearly increasing voltage (CELIV) and its variations have been proposed as a powerful tool to characterize charge transport in disordered semiconductors including inorganic and organic materials and to overcome certain limitations of the traditionally used time-of-flight technique, especially when charge transport is highly dispersive. In the original version of CELIV (often referred to as dark-CELIV), a linearly increasing voltage (triangular pulse) is applied to extract thermally generated carriers and the mobility is obtained from the time it takes to reach the peak of the extraction transient. In organic semiconductors or wide-bandgap inorganic semiconductors, where the concentration of thermally generated carriers is low, either photogeneration of charge carriers or charge injection is used. These variations are called photo-CELIV and i-CELIV, respectively. This chapter discusses the principles of the CELIV techniques and provides an overview of the applicability of different variations for different materials and structures, including dark-CELIV for the determination of mobility, bulk conductivity, and density of equilibrium charge carriers, photo-CELIV for the investigation of the relaxation of both the mobility and density of photogenerated charge carriers, and i-CELIV for the independent investigation of transport peculiarities of both injected holes and electrons in bulk heterojunctions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".