Near Band Edge excitation in <scp>2D</scp> materials by Transmission Electron Microscopy
Bibliographic record
Abstract
In this work, we report on the characterization of near band edge excitation by electron energy loss spectroscopy (EELS). This technique is operated in a Transmission Electron Microscope and allows to rely the structure of a material obtained by HR‐TEM with its chemical and physical properties deduced from EELS. Indeed, when the energy transfered by a transmitted electron remains below 50 eV, it is possible to have access to the electronic structure of the material and more precisely to its dielectric function. [1] In other words, we are able to obtain informations such as, plasmon resonances, interband transitions and band gap measurements. We used a Libra 200 equipped with an electrostatic monochromator operating at 80 kV. Thanks to the in‐column filter, energy loss signal is recorded on a CCD camera with a spectral resolution of 150 meV. The sharp cut‐off of the omega filter allows to probe the dielectric properties of semiconducting materials down to 1eV losses. We are able to determine bandgaps in several 2D materials and rely them to the number of layers. For instance, we can see the blue shift of the “optical absorption” from several MoS 2 layers (1.4 eV) down to a single layer (1.8 eV). Recently, thanks to dedicated operating modes [2,3], we have been able to obtain additional informations on the plasmons and interband transitions over the Brillouin Zone in hexagonal Boron Nitride (hBN). Energy Filtered scattering patterns have been recorded in the TEM to have access to the symmetries of the dipole matrix elements involved in the observed transitions. Moreover, by dispersing the energy along specific crystallographic directions, we accessed to the related dispersion of plasmons and interband transitions with the so‐called ω‐q maps [2] as representated on fig 1. We show that, due to a strong electron‐hole interaction, the observed dispersion is related to the one of the exciton [4]. The experimental results are in good agreements with inelastic X‐ray scattering experiments [5] and calculations [6] as shown on fig 3.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".