Modulation of Electronic Behaviors of InSe Nanosheet and Nanoribbons: The First‐Principles Study
Bibliographic record
Abstract
Abstract Recently, InSe monolayer, a new member added to the 2D materials, has been extensively studied in theories and experiments. Here, the electronic structures of the tunable band‐gap semiconductor of 2D InSe nanosheets and the band structure and electronic transport properties of quasi‐1D InSe nanoribbons (ISNs) are presented by using the first‐principles method. The calculated band structures show that an enlarged indirect band gap appears in 2D InSe nanosheet by external strain, and the gap reduces monotonically as the strain changing from the compression to stretch independent of the zigzag or armchair direction. Moreover, the band structures of quasi‐1D ISNs show that armchair ISNs are all nonmagnetic semiconductors, and the zigzag ISNs (zISNs) exhibit metallic regardless of the ribbon width or magnetic states. The non‐equilibrium Green's function calculation reveals the electronic transport properties of zISNs with different widths. An obvious negative differential resistive (NDR) effect is found in the current–voltage curves independent of the ribbon width due to the bias‐dependent transmission within the voltage window. The tunable band‐gap semiconductor behavior of 2D InSe nanosheet and the novel NDR effect in quasi‐1D zISNs indicate a very promising application of InSe‐based nanomaterials in electronic nanodevices.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".