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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. 

Although the relationship between plastics and their embedded small molecules has been previously hypothesized, direct and systematic evidence remains limited. Herein, we introduced an innovative approach to validate this relationship by screening specific small molecules as markers to decode plastic information. Given the mature techniques available for polymer identification, enabling subsequent validation, this study focused on screening polymer-specific small molecule markers. Specifically, plastic samples of various polymer types─including raw plastic pellets and postprocessed plastic products─were collected, extracted, and analyzed with a nontargeted method. Distinct polymer-based features were observed in raw plastic pellets: 21 in polyethylene (PE), 69 in polypropylene (PP), 119 in poly(ethylene terephthalate) (PET), and 14 in polystyrene (PS). Of these, 2, 28, 101, and 10 features were also detected in postprocessed plastic products of the same polymer, indicating these co-occurring features could serve as polymer-specific markers. Representative markers were identified, including Irganox 1010 transformation products in PP-based plastics, PET oligomers in PET-based plastics, and dibenzoylmethane in PS-based plastics. These markers were then used to identify the polymer type of two additional plastic bottles as PET, consistent with results obtained from pyrolysis-gas chromatography/mass spectrometry. This work provides a proof-of-concept for employing small molecule markers to decode plastic-related information. 

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 Mechanical stress can directly activate chemical reactions by reducing the reaction energy barrier. A possible mechanism of such mechanochemical activation is structural deformation of the reactant species. However, the effect of deformation on the reaction energetics is unclear, especially, for shear stress-driven reactions. Here, we investigated shear stress-driven oligomerization reactions of cyclohexene on silica using a combination of reactive molecular dynamics simulations and ball-on-flat tribometer experiments. Both simulations and experiments captured an exponential increase in reaction yield with shear stress. Elemental analysis of ball-on-flat reaction products revealed the presence of oxygen in the polymers, a trend corroborated by the simulations, highlighting the critical role of surface oxygen atoms in oligomerization reactions. Structural analysis of the reacting molecules in simulations indicated the reactants were deformed just before a reaction occurred. Quantitative evidence of shear-induced deformation was established by comparing bond lengths in cyclohexene molecules in equilibrium and prior to reactions. Nudged elastic band calculations showed that the deformation had a small effect on the transition state energy but notably increased the reactant state energy, ultimately leading to a reduction in the energy barrier. Finally, a quantitative relationship was developed between molecular deformation and energy barrier reduction by mechanical stress. 

Intertwined charge and spin correlations are ubiquitous in a wide range of transition metal oxides and are often perceived as intimately related to unconventional superconductivity. Theoretically envisioned as driven by strong electronic correlations, the intertwined order is usually found to be strongly coupled to the lattice as signaled by pronounced phonon softening. Recently, both charge and spin density waves (CDW and SDW) and superconductivity have been discovered in several Ruddlesden-Popper (RP) nickelates, in particular trilayer nickelates $R{E}_4{\mathrm{Ni}}_3{\mathrm{O}}_{10}$( $RE=\mathrm{Pr}$, La). The nature of the intertwined order and the role of lattice-charge coupling are at the heart of the debate about these materials. Using inelastic x-ray scattering, we mapped the low-energy phonon dispersions in $R{E}_4{\mathrm{Ni}}_3{\mathrm{O}}_{10}$and found no evidence of softening near the CDW wave vector over a wide temperature range, which contrasts with the pronounced anomalies frequently observed in cuprate superconductors. Calculations of the electronic susceptibility revealed a peak at the observed SDW ordering vector but not at the CDW wave vector. Our experimental and theoretical findings highlight the crucial role of the spin degree of freedom and establish a foundation for understanding the interplay between superconductivity and density-wave transitions in RP nickelate superconductors and beyond. 

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.