Evidence map›Paper›PMID 42122341›Full record

ArticleSensors (Basel, Switzerland)2026

Turbulence-Resistant Femtosecond Filaments via Nonlinear Self-Guiding and OAM Modulation.

Jinpei Liu, Xi Yang, Weiyun Jin, Zuyou Ren, Caiyi Yang, Tingting Shi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jinpei LiuCollege of Information Engineering, Hebei GEO University, Shijiazhuang 052161, China.
Xi YangCollege of Information Engineering, Hebei GEO University, Shijiazhuang 052161, China.
Weiyun JinCollege of Information Engineering, Hebei GEO University, Shijiazhuang 052161, China.
Zuyou RenCollege of Information Engineering, Hebei GEO University, Shijiazhuang 052161, China.
Caiyi YangCollege of Information Engineering, Hebei GEO University, Shijiazhuang 052161, China.
Tingting ShiCollege of Information Engineering, Hebei GEO University, Shijiazhuang 052161, China.

Funding

Hebei GEO University 2025 Provincial Higher Education Institution Basic Research Operating Fund Research Project QN25018Youth Fund Project of Hebei Provincial Department of Education QN2025153
6 · The paper itself

Abstract

As a prominent frontier in ultrafast laser-matter interaction, femtosecond laser filamentation holds great potential for atmospheric pollutant detection and remote sensing. However, its practical application in the open atmosphere is severely hampered by atmospheric turbulence, which induces beam wander, wavefront distortion, and intensity scintillations. In this study, we numerically investigated the propagation dynamics of femtosecond laser filaments in a turbulent medium and elucidated the underlying physical mechanisms. The results show that, compared to linear propagation, the nonlinear self-guiding effect inherent to filamentation effectively suppresses turbulence-induced beam wander. Furthermore, by employing vortex beams carrying orbital angular momentum (OAM), we significantly suppressed the stochastic generation of multiple filaments, thereby notably improving the stability of long-range filament propagation in complex atmospheric conditions. These findings provide new insights into the physical mechanisms and novel strategies for improving the robustness of laser filamentation technology in real-world turbulent environments.

Indexed as

femtosecond laser filamentationmultiple filamentsnonlinear self-guiding effectorbital angular momentumturbulence

Identifiers

PMID42122341
PMCPMC13165875

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.