Ligand-Induced Surface Charge Density Modulation Generates Local Type-II Band Alignment in Reduced-Dimensional Perovskites
Bibliographic record
Abstract
Two-dimensional (2D) and quasi-2D perovskite materials have enabled advances in device performance and stability relevant to a number of optoelectronic applications. However, the alignment among the bands of these variably quantum confined materials remains a controversial topic: there exist multiple experimental reports supporting type-I, and also others supporting type-II, band alignment among the reduced-dimensional grains. Here we report a combined computational and experimental study showing that variable ligand concentration on grain surfaces modulates the surface charge density among neighboring quantum wells. Density functional theory calculations and ultraviolet photoelectron spectroscopy reveal that the effective work function of a given quantum well can be varied by modulating the density of ligands at the interface. These induce type-II interfaces in otherwise type-I aligned materials. By treating 2D perovskite films, we find that the effective work function can indeed be shifted down by up to 1 eV. We corroborate the model via a suite of pump-probe transient absorption experiments: these manifest charge transfer consistent with a modulation in band alignment of at least 200 meV among neighboring grains. The findings shed light on perovskite 2D band alignment and explain contrasting behavior of quasi-2D materials in light-emitting diodes (LEDs) and photovoltaics (PV) in the literature, where materials can exhibit either type-I or type-II interfaces depending on the ligand concentration at neighboring surfaces.
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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 | 0.000 | 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".