Hot-Probe Method for Majority Charge Carrier Determination in Methylammonium Lead Halide Perovskites
Bibliographic record
Abstract
Perovskites, due to their promising capabilities, are among the major candidates to substitute early-generation silicon solar cells. Despite their outstanding electrical and optical properties, actual comprehension of their behavior requires further studies and also investigation tools. It is shown that vacancy traps in organic-inorganic perovskites induce different polarities, where lead and methylamine vacancies posses p- and n-type polarity, respectively. Referred to as the self-doping property, different molar ratios of chemical precursors lead to controllable polarity and intrinsic doping. In other reports, thermal annihilation is also shown to convert p-type perovskite to n-type. According to the broad range of synthesized perovskites and their current application and promising future in optoelectronic devices, a simple, quick, and dependable method for trap concentration and major carrier-type determination is essential. Hot probe is a simple, affordable, and fast method for extracting polarity type in bulk and thin-film semiconductors. So far, this method and its variations have been applied for measuring conductivity, polarity, and impurity concentration in diverse ranges of semiconductors. In this method, free carriers are generated by raising the temperature of hot-probe contact and induced diffusion from this contact toward the other cold contact is studied. An equilibrium state is reached as a result of the formed built-in electric field between the cold and hot electrodes and also temperature elevation of this cold electrode, which act as diffusion dampers. Here, different types of organic-inorganic perovskites are synthesized and their electrical behavior is evaluated by the hot-probe method. Finally, it is concluded that the hot-probe approach is a dependable, fast, and accurate method to be applied in the field of perovskite material evaluations.
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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 | 0.000 |
| 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".