Filamentation of femtosecond laser pulses as a source for radiotherapy
Bibliographic record
Abstract
Here, we report that intense ultra-short laser pulses produce a plasma of low energy electrons (LEEs) by the inverse Bremsstrahlung effect and multiphoton ionization process. The phenomena show five striking characteristics. First, the self-focusing of ultra-short laser pulses creates a plasma of LEEs (6.5 eV), which is concentrated in filaments through an avalanche process. Second, kinetically hot 6.5 eV electrons interact with surrounding molecules resulting in reactive radical species. Third, the dose rate reaches an enormous level of ~2.8 × 1011 Gy/s as determined by a cericcerous sulfate dosimetry and this leads to an ultra-high deposition of energy of between 4.6 × 107 to 8.16 × 107 keV/μm. Fourth, filaments of variable length are produced by femtosecond pulses depending on the pulse duration as determined by a tissue-equivalent radiation polymer gel dosimeter and imaged by magnetic resonance imaging (MRI). These results reveal that one of the very interesting novelty of filamentation is the very low entrance dose, similar to proton irradiation. Lastly, filamentary irradiation results in the decomposition of thymidine in the absence and the presence of oxygen similar to the radiolysis of water.
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) | 0.003 | 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".