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A highly efficient drilling process is found in non-transparent metallic materials enabled by the use of non-diffractive ultrafast Bessel beams. Applied for deep drilling through a 200 μm-thick steel plate, the Bessel beam demonstrates twofold higher drilling efficiency compared to a Gaussian beam of similar fluence and spot size. Notwithstanding that surface ablation occurs with the same efficiency for both beams, the drilling booster results from a self-replication and reconstruction of the beam along the axis, driven by internal reflections within the crater at quasi-grazing incidence, bypassing potential obstacles. The mechanism is the consequence of an oblique wavevectors geometry with low angular dispersion and generates a propagation length beyond the projection range allowed by the geometry of the channel. With only the main lobe being selected by the channel entrance, side-wall reflection determines the refolding of the lobe on the axis, enhancing and replicating the beam multiple times inside the channel. The process is critically assisted by the reduction of particle shielding enabled by the intrinsic self-healing of the Bessel beam. Thus the drilling process is sustained in a way which is uniquely different from that of the conventional Gaussian beam, the latter being damped within its Rayleigh range. These mechanisms are supported and quantified by Finite Difference Time Domain calculations of the beam propagation. The results show key advantages for the quest towards efficient laser drilling and fabrication processes.
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http://dx.doi.org/10.1038/s41598-022-05967-5 | DOI Listing |
Front Sports Act Living
August 2025
Department of Psychology, University of Cyprus, Nicosia, Cyprus.
Introduction: In this study, we investigated the involvement of different aspects of attention in a light training task requiring fast physical responses to targets.
Methods: Fifty adult participants carried out drills in SpeedPad, a Virtual Reality (VR) adaptation of the Batak Pro and the Fitlight Trainer systems commonly used by athletes of various sports. Participants also carried out three established cognitive tasks on a desktop computer: the Posner cueing task, a visual conjunction search task, and a Motion Object Tracking (MOT) task.
Rev Sci Instrum
September 2025
Downhole Measurement and Control Laboratory of National Engineering Laboratory of Oil and Gas Drilling Technology, Xi'an 710065, China.
Currently, notable difficulties exist regarding the real-time uploading of data and fast logging in remote-detection acoustic logging, which can be mitigated via downhole data compression. This study systematically analyzed a wavelet transform-based data compression method and developed hardware platforms based on a digital signal processor (DSP) and field programmable gate array (FPGA). The wavelet transform-based acoustic-logging-data compression algorithm was executed on both the hardware platforms, and the corresponding decompression algorithm was implemented on the host computer.
View Article and Find Full Text PDFSci Rep
September 2025
Department of Microbial Interactions, Institute of Microbiology, Friedrich Schiller University (FSU), Jena, Germany.
Subsurface habitats, found under various geological conditions, exhibit diverse microbial communities. The vadose zone, a previously unexplored subsurface compartment, connects the surface to phreatic groundwater. Drilling into the subsurface allows access to these habitats for microbial diversity study.
View Article and Find Full Text PDFPLoS One
August 2025
Bristol Business School, University of Bristol, Bristol, United Kingdom.
Northern Shaanxi's oil-gas drilling produces large amounts of waste drilling fluids with high-value solids (barite, bentonite). Traditional disposal causes resource waste and pollution. This study proposes a stepwise flotation process for typical local oil-based waste: surface cleaning to break oil film wrapping and combined reagents to regulate mineral surface hydrophobicity differences, enabling efficient separation and recovery of barite and bentonite.
View Article and Find Full Text PDFSci Rep
August 2025
Department of Civil Engineering, University of Tabuk, Tabuk, Saudi Arabia.
This study examines the intricate task of predicting construction duration for drill-and-blast tunnels utilizing machine learning (ML) techniques. First, a comprehensive dataset (500 data points) encompassing 20 diverse parameters was compiled by constructing eight tunnels. After meticulous analysis, 17 of the 20 parameters were identified as crucial for training the algorithms.
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