Resolving and Tuning Carrier Capture Rates at a Single Silicon Atom Gap State
Bibliographic record
Abstract
We report on tuning the carrier capture events at a single dangling bond (DB) midgap state by varying the substrate temperature, doping type, and doping concentration. All-electronic time-resolved scanning tunneling microscopy (TR-STM) is employed to directly measure the carrier capture rates on the nanosecond time scale. A characteristic negative differential resistance (NDR) feature is evident in the scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS) measurements of DBs on both n- and p-type doped samples. We find that a common model accounts for both observations. Atom-specific Kelvin probe force microscopy (KPFM) measurements confirm the energetic position of the DB's charge transition levels, corroborating STS studies. We show that under different tip-induced fields the DB can be supplied with electrons from two distinct reservoirs: the bulk conduction band and/or the valence band. We measure the filling and emptying rates of the DBs in the energy regime where electrons are supplied by the bulk valence band. We show that adding point charges in the vicinity of a DB shifts observed STS and NDR features due to Coulombic interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".