Terahertz Spectroscopy: Studying Carrier Dynamics in Semiconductor Nanostructures
Bibliographic record
Abstract
Understanding the ultrafast dynamics of photoexcited carriers in semiconductor nanostructures and their dependence on sample morphology is crucial for their incorporation into photonic devices. Time-resolved terahertz (THz) spectroscopy is an all-optical, contact free technique that allows directly measuring the transient mobile carrier dynamics and terahertz conductivity over picosecond time scales, and is uniquely suitable as a probe of conductivity in nanomaterials. We have applied time-resolved THz spectroscopy to investigate ultrafast carrier dynamics to a variety of nanostructured semiconducting systems such as silicon nanocrystal films, nanogranular iron pyrate (FeS2) and vanadium dioxide (VO2). Furthermore, terahertz emission spectroscopy of nanomaterials, where the sample itself emits THz radiation in response to optical excitation, provides important insights into carrier dynamics. We have recently generated THz pulses from optically excited macroscopic arrays of aligned single-wall carbon nanotubes (SWCNTs). We propose that top-bottom asymmetry present in the SWCNT arrays produces a built-in electric field in semiconducting SWCNTs, which enables generation of polarized THz radiation by a photocurrent surge.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".