Tuning Plasmon Resonance of In<sub>2</sub>O<sub>3</sub> Nanocrystals throughout the Mid-Infrared Region by Competition between Electron Activation and Trapping
Bibliographic record
Abstract
Controlling plasmonic properties of aliovalently doped semiconductor nanocrystals in mid-infrared (MIR) spectral region is of a particular current interest, because of their potential application in heat-responsive devices and near-field enhanced spectroscopies. However, a lack of detailed understanding of the correlations among the electronic structure of the host lattice, dopant ions, and surface properties hampers the development of MIR-tunable plasmonic nanocrystals (NCs). In this article, we report the colloidal synthesis and spectroscopic properties of two new plasmonic NC systems based on In 2 O 3, antimony- and titanium-doped In 2 O 3 NCs, and comparative investigation of their electronic structure using the combination of the Drude–Lorenz model and density functional theory. The localized surface plasmon resonances (LSPRs) lie at lower energies and have smaller bandwidths for Ti-doped than for Sb-doped In 2 O 3 NCs with similar doping levels, indicating lower free electron density. Surprisingly, the Fermi level is found to be higher in Ti-doped In 2 O 3 than in Sb-doped In 2 O 3, suggesting the formation of electron trap states on nanocrystal surfaces, which reduce carrier density without significantly impacting their mobility. Controlling the competition between doping concentration and electron trapping allowed us to generate LSPR in Ti-doped In 2 O 3 nanocrystals deep in the MIR region, and tune the absorption spectra from 650 cm –1 to 8000 cm –1 . We also demonstrated the possibility to enhance the intensity of LSPR in these new plasmonic NCs by adjusting the synthesis and post-synthesis treatment conditions. The results of this work allow for an expansion of the tuning range of LSPR of colloidal metal oxide NCs by controlling the electronic structure of aliovalent dopant and charge carrier trapping.
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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 | 0.000 | 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) | 0.000 | 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".