Atomically Thin Antimony‐Doped Indium Oxide Nanosheets for Optoelectronics
Bibliographic record
Abstract
Abstract Wide bandgap semiconducting oxides are emerging as potential 2D materials for transparent electronics and optoelectronics. This fuels the quest for discovering new 2D metal oxides with ultrahigh transparency and high mobility. While the former can be achieved by reducing the thickness of oxide films to only a few nanometers, the latter is more commonly realized by intentional doping. This article reports a one‐step synthesis of few‐unit‐cell‐thick and laterally large antimony‐doped indium oxide (IAO). The doping process occurs spontaneously when the oxide is grown on the surface of a molten Sb–In alloy and 2D IAO nanosheets can be easily printed onto desired substrates. With thicknesses at the atomic scale, these materials exhibit excellent transparency exceeding 98% across the visible and near‐infrared range. Field‐effect transistors based on low‐doped IAO nanosheets reveal a high electron mobility of ≈40 cm2 V−1 s−1. Additionally, a notable photoresponse is observed in 2D IAO‐based photodetectors under ultraviolet (UV) radiation. Photoresponsivities of low‐doped and highly doped IAO at a wavelength of 285 nm are found to be 1.2 × 103 and 0.7 × 103 A W−1, respectively, identifying these materials as promising candidates for the fabrication of high‐performance optoelectronics in the UV region.
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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".