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Abstract Amorphous carbons can have drastically different physical properties depending on synthetic methods. Among these, hydrogenated diamond-like carbon (HDLC) produced via plasma-enhanced chemical vapor deposition is unique in that it exhibits superlubricity with a coefficient of friction (COF) less than 0.01 in proper environmental conditions. It is known that HDLC undergoes friction-induced graphitization at the shear interface and forms a highly hydrogenated transfer film at the counter-surface sliding against it. In contrast, glassy carbon (GC) produced via pyrolysis of organic precursors rarely exhibits superlubricious behavior even though the graphitic nature probed with Raman spectroscopy is similar to that of the transfer film formed from HDLC. This study addresses this drastic difference in friction of HDLC and GC and identifies key parameters that can be tuned to achieve (nearly) superlubricious behaviors with GC. The factors influencing the superlubricity of amorphous carbon include the composition and structure of the initial carbon coating, which strongly depend on the synthetic method, and the coating failure and transfer film stability, which depend on the surface chemistry of the substrate. 

While semi‐autonomous drones are increasingly used for road infrastructure inspection, their insufficient ability to independently handle complex scenarios beyond initial job planning hinders their full potential. To address this, the paper proposes a human–drone collaborative inspection approach leveraging flexible surface electromyography (sEMG) for conveying inspectors' speech guidance to intelligent drones. Specifically, this paper contributes a new data set,sEMGCommands forPilotingDrones (sCPD), and ansEMG‐basedCross‐subjectClassificationNetwork (sXCNet), for both command keyword recognition and inspector identification. sXCNet acquires the desired functions and performance through a synergetic effort of sEMG signal processing, spatial‐temporal‐frequency deep feature extraction, and multitasking‐enabled cross‐subject representation learning. The cross‐subject design permits deploying one unified model across all authorized inspectors, eliminating the need for subject‐dependent models tailored to individual users. sXCNet achieves notable classification accuracies of 98.1% on the sCPD data set and 86.1% on the public Ninapro db1 data set, demonstrating strong potential for advancing sEMG‐enabled human–drone collaboration in road infrastructure inspection. 

Abstract Measuring the diffusion coefficient of clay-based liner materials is important in estimating and predicting long-term barrier performance in waste containment facilities. Various theoretical models, including the finite cylindrical model, have been commonly used to determine the diffusion properties of clay-based liner materials in leaching tests. However, the assumption of zero-concentration boundary conditions of the traditional finite cylindrical model contradicts the measured variation of concentration in real leaching tests, likely resulting in (1) underestimated and unconservative diffusion coefficient, or (2) requirement of a relatively large liquid-to-soil ratio and frequent leachate replacement in the experiment to maintain the zero-concentration boundary condition. In this study, a theoretical model was developed to evaluate the solute diffusion process within a soil specimen under arbitrary, time-dependent concentration boundary conditions. The proposed model, incorporating the time-dependent boundary conditions, provides efficient calculations of the concentration distribution and the cumulative fraction leached of solute across the soil specimen. The example application of the proposed model to experimental data demonstrates the capability of the proposed model to determine apparent diffusion coefficients of clay-based liner materials without introducing errors associated with the assumption of a zero concentration boundary condition. The proposed model provides a comprehensive method to investigate the dynamic transport behaviors of solutes through clay-based liner materials in future studies. 

Abstract Multimode fibers (MMFs) are gaining renewed interest for nonlinear effects due to their high-dimensional spatiotemporal nonlinear dynamics and scalability for high power. High-brightness MMF sources with effective control of the nonlinear processes would offer possibilities in many areas from high-power fiber lasers, to bioimaging and chemical sensing, and to intriguing physics phenomena. Here we present a simple yet effective way of controlling nonlinear effects at high peak power levels. This is achieved by leveraging not only the spatial but also the temporal degrees of freedom during multimodal nonlinear pulse propagation in step-index MMFs, using a programmable fiber shaper that introduces time-dependent disorders. We achieve high tunability in MMF output fields, resulting in a broadband high-peak-power source. Its potential as a nonlinear imaging source is further demonstrated through widely tunable two-photon and three-photon microscopy. These demonstrations provide possibilities for technology advances in nonlinear optics, bioimaging, spectroscopy, optical computing, and material processing. 

High-precision light manipulation is crucial for delivering information through complex media. However, existing spatial light modulation devices face a fundamental speed-fidelity tradeoff. Digital micromirror devices have emerged as a promising candidate for high-speed wavefront shaping but at the cost of compromised fidelity due to the limited control degrees of freedom. Here, we leverage the sparse-to-random transformation through complex media to overcome the dimensionality limitation of spatial light modulation devices. We demonstrate that pattern compression by sparsity-constrained wavefront optimization allows sparse and robust wavefront representations in complex media, improving the projection fidelity without sacrificing frame rate, hardware complexity, or optimization time. Our method is generalizable to different pattern types and complex media, supporting consistent performance with up to 89% and 126% improvements in projection accuracy and speckle suppression, respectively. The proposed optimization framework could enable high-fidelity high-speed wavefront shaping through different scattering media and platforms without changes to the existing holographic setups, facilitating a wide range of physics and real-world applications. 

The molecules with higher tribochemical reactivity exhibited smaller activation volume, implying that less mechanical energy was required to initiate tribochemical reactions. 

Abstract The May 2024 super storm is one of the strongest geomagnetic storms during the past 20 years. One of the most remarkable ionospheric responses to this event over East and Southeast Asia is the complex ionospheric fluctuations following the storm commencement. The fluctuations created multiple oscillations of total electron content (TEC) embedded in the diurnal variation, with amplitudes up to 10 TECu. Along the same latitude, the fluctuations were nearly synchronized over a wide longitude span up to 35°. In the meridional direction, the fluctuations over low latitudes were the most significant and complex, which contained two main components, the poleward extending oscillations originated from the magnetic equator, and the equatorward propagating traveling ionospheric disturbances (TIDs) from high latitudes. The TIDs likely occurred around the globe. The storm‐time interplanetary electric fields penetrating into equatorial latitudes of the ionosphere and the auroral energy input were suggested to drive the poleward extending oscillations and the equatorward TIDs, respectively. 

Label-free imaging through two-photon autofluorescence of NAD(P)H allows for nondestructive, high-resolution visualization of cellular activities in living systems. However, its application to thick tissues has been restricted by its limited penetration depth within 300 μm, largely due to light scattering. Here, we demonstrate that the imaging depth for NAD(P)H can be extended to more than 700 μm in living engineered human multicellular microtissues by adopting multimode fiber-based, low repetition rate, high peak power, three-photon excitation of NAD(P)H at 1100 nm. This is achieved by having more than 0.5 megawatts peak power at the band of 1100 ± 25 nm through adaptively modulating multimodal nonlinear pulse propagation with a compact fiber shaper. Moreover, the eightfold increase in pulse energy enables faster imaging of monocyte behaviors in the living multicellular models. These results represent a substantial advance for deep and dynamic imaging of intact living biosystems. The modular design is anticipated to allow wide adoption for demanding imaging applications, including cancer research, immune responses, and tissue engineering.