Charge Carrier Transport and Injection Across Organic Heterojunctions
Bibliographic record
Abstract
The discovery of highly efficient organic light-emitting diodes (OLEDs) in the 1980s has stimulated extensive research on organic semiconductors and devices. Underlying this breakthrough is the realization of the organic heterojunction (OH). Besides OLEDs, the implementation of the OH also significantly improves the power conversion efficiency in organic photovoltaic cells (OPVs). The continued technological advancements in organic electronic devices depend on the accumulation of knowledge of the intrinsic properties of organic materials and related interfaces. Among them, charge-carrier transport and carrier injection are two key factors that govern the performance of a device.\n\nThis thesis mainly focuses on the charge carrier injection and transport at organic heterojunctions. The carrier transport properties of different organic materials used in this study are characterized by time-of-flight (TOF) and admittance spectroscopy (AS). An injection model is formulated by considering the carrier distribution at both sides of the interface. Using a steady-state simulation approach, the effect of accumulated charges on energy level alignment at OH is revealed. Instead of a constant injection barrier, it is found that the barrier varies with applied voltage. Moreover, an escape probability function in the injection model is modified by taking into account the total hopping rate and available hopping sites at the interface. The model predicts that the injection current at low temperature can be dramatically modified by an extremely small density of deep trap states. More importantly, the temperature dependence of the injection current is found to decrease with increasing barrier height. This suggests that extracting the barrier height from the J vs 1/T plot, as commonly employed in the literature, is problematic. These theoretical predictions are confirmed by a series of experiments on heterojunction devices with various barrier heights. In addition, the presence of deep trap states is also consistent with carrier mobility measurements at low temperature. \nFrom the point of view of application, an interface chemical doping method is proposed to engineer the carrier injection at an organic heterojunction. It is found that the the injection current can be effectively increased or suppressed by introducing a thin (2 nm) doped organic layer at the interface. This technique is further extended to study the impact of an injection barrier at the OH. in OLEDs, on device performance. It is shown that a 0.3 eV injection barrier at the OH, that is normally negligible at metal/organic interface, can reduce the device efficiency by 25 %. This is explained by the carrier distribution in the density-of-states at the OH. \nFurthermore, the carrier transport properties in a bulk heterojunction system are investigated. The bulk heterojunction consists of an interpenetrating network of a polymeric electron donor and a molecular electron acceptor. This material system has been studied in the last few years as an attractive power conversion efficiency (5% under AM 1.5) of OPV cells has been demonstrated. It is found that the electron mobility is greatly dependent on the thermal treatment of the film. Interfacial dipole effect at the heterojunction between the donor and the acceptor is proposed to be the determining factor that alters the carrier mobility in different nano-scale structures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".