C3N4 and C3N5 Nanosheets As Passivation Layers and Carrier Extractors for Inorganic Semiconductor Nanowires and Quantum Dots
Bibliographic record
Abstract
Inorganic semiconductor nanowires and quantum dots made of chalcogenides, III-V semiconductors and halide perovskites offer exciting potential for optoelectronic devices. The orthogonalization possible in nanowires between the normally competing processes of charge generation and charge separation have been used to project very high operating performance exceeding that of thin films and single crystals in light harvesting devices such as solar cells, photodetectors, photocatalysts and photoelectrolyzers. Likewise, inorganic quantum dots with size-tunable absorption and emission spectra are excellent candidates for light emitting devices as well as light harvesting devices. However, the practical performance of inorganic nanowire based optoelectronic devices has significantly lagged theoretical predictions. A major reason for the discrepancy between theory and experiment is the presence of surface traps and defects in nanowires and quantum dots, which exhibit a large surface area to volume ratio. Several chemical treatments and annealing regimens have been employed to heal surface defects in nanowires and quantum dots. One popular strategy involves wrapping nanowires and/or quantum dots with a thin coating of a molecular monolayer (e.g. alkanethiols) or an atomic layer deposited conformal oxide. While such core-shell architectures are frequently effective in reducing surface defects, the surface passivation is invariably accompanied by a deterioration in optoelectronic properties due to the difficulty experienced by charge carriers in tunneling through the thin shell layer. The resulting trade-off between surface passivation and carrier extraction limits performance improvements in light harvesting devices. Thus there is a strong need for passivating layers that do not negatively impact carrier extraction. Herein, we show that graphitic carbon nitride coatings are highly effective in passivating the surfaces of inorganic nanowires and quantum dots while preserving excellent carrier transport and extraction. Three illustrative examples are provided together with in-depth spectroscopic and electrical characterization: (1) Cesium lead bromide (CsPbBr3) quantum dots passivated by g-C3N4 nanosheets and performing spectacularly as CO2 photoreduction catalysts and water-splitting photoanodes (2) The double helical ternary semiconductor SnIP passivated by g-C3N4 nanosheets which experienced a remarkable improvement in photoelectrochemical performance (3) Cadmium sulfide (CdS) nanowires passivated by C3N5 nanosheets resulting in a superior photocatalytic performance
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".