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Record W7133074081

Energy Level Alignment at Organic Semiconductor Interfaces

2023· dissertation· W7133074081 on OpenAlexaff
Yiying Li

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOrganic semiconductorInterface (matter)OLEDOrganic solar cellSemiconductorEnergy (signal processing)Range (aeronautics)Organic electronics
DOInot available

Abstract

fetched live from OpenAlex

Organic semiconductors are promising materials for future generations of light-weight and flexible electronic devices. Several distinct functional organic semiconducting layers are usually stacked together to create a variety of devices such as organic light emitting diodes and organic solar cells. The relative alignment of each layer’s frontier molecular orbital energies with those in its adjacent layers is critical in dictating a device’s performance and functionality. With a rapidly increasing number of new functional organic semiconducting layers in modern devices, the energy level alignment (ELA) at organic-organic interfaces is becoming an increasingly crucial aspect in material selections and device design. Despite of this, there is still lack of a general scientific guideline in charting interface energy levels at such interfaces. In this thesis, a comprehensive experimental study is conducted on several dozens of organic heterointerfaces by using photoelectron spectroscopy. Broad sets of molecules having a diverse range of electronic and geometric structures were carefully selected. After correcting variables such as molecular orientation-dependent ionization energies, a general energy level alignment rule for charting interface energy levels (energy offsets and charge-transfer dipoles) over three distinct regions was found. This universal energy level alignment rule covers a wide achievable range of frontier orbital energies at organic heterojunctions, and is expected to provide a master guide in selection of new materials for fabricating future generations of superior organic semiconductor devices.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.025
GPT teacher head0.275
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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