Axial dispersion-managed liquid-core fibers: A platform for tailored higher-order mode supercontinuum generation
Bibliographic record
Abstract
Soliton-based supercontinuum generation is a powerful approach for generating light with the desired properties, although limited dispersion tuning capabilities remain a key challenge. Here, we introduce liquid-core fibers (LCFs) with longitudinally controlled dispersion of a higher-order mode, achieved by axial modulation of the liquid core diameter. This approach provides a versatile photonic platform with unique dispersion control capabilities that are particularly relevant to ultrafast, non-linear frequency conversion. Our tuning concept uses LCFs with anomalous dispersion at telecommunication wavelengths (TE01-mode) and relies on the strong dependence of dispersion on the core diameter. Non-monotonic, complex dispersion profiles feature multiple dispersive waves formation when launching ultrashort pulses. For example, this effect has been used to fill spectral gaps in fibers with linearly decreasing core diameter in order to spectrally smooth the output spectra. Our results highlight the potential of LCFs for controlling dispersion, particularly along the fiber axis, thus yielding novel dispersion landscapes that can reveal unexplored nonlinear dynamics and generate tailored broadband spectra.
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 | 0.000 |
| 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 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".