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Pressure-induced phase transformations (PTs) in Si, the most important electronic material, have been broadly studied. However, strain-induced PTs in Si were never studied in situ. Here, we revealed in situ various important plastic strain-induced PT phenomena. A correlation between the particle size's direct and inverse Hall-Petch effect on yield strength and pressure for strain-induced PT is found. For 100 nm particles, strain-induced PT Si-I³Si-II initiates at 0.3 GPa versus 16.2 GPa under hydrostatic conditions; Si-I³Si-III PT starts at 0.6 GPa and does not occur under hydrostatic pressure. Pressure in small Si-II and Si-III regions is ~5-7 GPa higher than in Si-I. Retaining Si-II and single-phase Si-III at ambient pressure and obtaining reverse Si-II³Si-I PT demonstrates the possibilities of manipulating different synthetic paths. The obtained results corroborate the elaborated dislocation pileup-based mechanism and have numerous applications for developing economic defect-induced synthesis of nanostructured materials, surface treatment (polishing, turning, etc.), and friction. 

Earth-abundant, cost-effective electrode materials are essential for sustainable rechargeable batteries and global decarbonization. Manganese dioxide (MnO₂) and hard carbon both exhibit high structural and chemical tunability, making them excellent electrode candidates for batteries. Herein, we elucidate the impact of electrolytes on the cycling performance of commercial electrolytic manganese dioxide in Li chemistry. We leverage synchrotron X-ray analysis to discern the chemical state and local structural characteristics of Mn during cycling, as well as to quantify the Mn deposition on the counter electrode. By using an ether-based electrolyte instead of conventional carbonate electrolytes, we circumvent the formation of a surface Mn(II)-layer and Mn dissolution from Li_xMnO₂. Consequently, we achieved an impressive ∼100% capacity retention for MnO₂after 300 cycles at C/3. To create a lithium metal-lean full cell, we introduce hard carbon as the anode which is compatible with ether-based electrolytes. Commercial hard carbon delivers a specific capacity of ∼230 mAh g⁻¹at 0.1 A g⁻¹without plateau, indicating a surface-adsorption mechanism. The resulting manganese dioxide||hard carbon full cell exhibits stable cycling and high Coulombic efficiency. Our research provides a promising solution to develop cost-effective, scalable, and safe energy storage solutions using widely available manganese oxide and hard carbon materials.

 

Aqueous Li-ion batteries (ALIBs) are an important class of battery chemistries owing to the intrinsic non-flammability of aqueous electrolytes. However, water is detrimental to most cathode materials and could result in rapid cell failure. Identifying the degradation mechanisms and evaluating the pros and cons of different cathode materials are crucial to guide the materials selection and maximize their electrochemical performance in ALIBs. In this study, we investigate the stability of LiFePO₄(LFP), LiMn₂O₄(LMO) and LiNi_0.8Mn_0.1Co_0.1O₂(NMC) cathodes, without protective coating, in three different aqueous electrolytes, i.e., salt-in-water, water-in-salt, and molecular crowding electrolytes. The latter two are the widely reported “water-deficient electrolytes.” LFP cycled in the molecular crowding electrolyte exhibits the best cycle life in both symmetric and full cells owing to the stable crystal structure. Mn dissolution and surface reduction accelerate the capacity decay of LMO in water-rich electrolyte. On the other hand, the bulk structural collapse leads to the degradation of NMC cathodes. LMO demonstrates better full-cell performance than NMC in water-deficient aqueous electrolytes. LFP is shown to be more promising than LMO and NMC for long-cycle-life ALIB full cells, especially in the molecular crowding electrolyte. However, none of the aqueous electrolytes studied here provide enough battery performance that can compete with conventional non-aqueous electrolytes. This work reveals the degradation mechanisms of olivine, spinel, and layered cathodes in different aqueous electrolytes and yields insights into improving electrode materials and electrolytes for ALIBs.

 

High reproductive compatibility between crops and their wild relatives can provide benefits for crop breeding but also poses risks for agricultural weed evolution. Weedy rice is a feral relative of rice that infests paddies and causes severe crop losses worldwide. In regions of tropical Asia where the wild progenitor of rice occurs, weedy rice could be influenced by hybridization with the wild species. Genomic analysis of this phenomenon has been very limited. Here we use whole genome sequence analyses of 217 wild, weedy and cultivated rice samples to show that wild rice hybridization has contributed substantially to the evolution of Southeast Asian weedy rice, with some strains acquiring weed-adaptive traits through introgression from the wild progenitor. Our study highlights how adaptive introgression from wild species can contribute to agricultural weed evolution, and it provides a case study of parallel evolution of weediness in independently-evolved strains of a weedy crop relative.

 

Physically unclonable hardware fingerprints can be used for device authentication. The photo-response non-uniformity (PRNU) is the most reliable hardware fingerprint of digital cameras and can be conveniently extracted from images. However, we find image post-processing software may introduce extra noise into images. Part of this noise remains in the extracted PRNU fingerprints and is hard to be eliminated by traditional approaches, such as denoising filters. We define this noise as software noise, which pollutes PRNU fingerprints and interferes with authenticating a camera armed device. In this paper, we propose novel approaches for fingerprint matching, a critical step in device authentication, in the presence of software noise. We calculate the cross correlation between PRNU fingerprints of different cameras using a test statistic such as the Peak to Correlation Energy (PCE) so as to estimate software noise correlation. During fingerprint matching, we derive the ratio of the test statistic on two PRNU fingerprints of interest over the estimated software noise correlation. We denote this ratio as the fingerprint to software noise ratio (FITS), which allows us to detect the PRNU hardware noise correlation component in the test statistic for fingerprint matching. Extensive experiments over 10,000 images taken by more than 90 smartphones are conducted to validate our approaches, which outperform the state-of-the-art approaches significantly for polluted fingerprints. We are the first to study fingerprint matching with the existence of software noise. 

Severe plastic deformations under high pressure are used to produce nanostructured materials but were studied ex-situ. Rough diamond anvils are introduced to reach maximum friction equal to yield strength in shear and the first in-situ study of the evolution of the pressure-dependent yield strength and radial distribution of nano structural parameters are performed for severely pre-deformed Zr.ω-Zr behaves like perfectly plastic, isotropic, and strain-path-independent and reaches steady values of the crystallite size and dislocation density, which are pressure-, strain- and strain-path-independent. However, steady states forα-Zr obtained with smooth and rough anvils are different, causing major challenge in plasticity theory. 

We studied laser-induced liquid indentations generated by the Marangoni effect. We showed experimental results along with the simulation model based on the lubrication theory. 

This work is to present a learning observer-based method for simultaneous detection and estimation of false data injection attacks (FDIAs) to the cyber-physical battery systems. The original battery system in a state-space formulation is transformed into two separate subsystems: one contains both disturbances and the FDIAs and the second one is free from disturbances but subject to FDIAs. A learning observer is then designed for the second subsystem such that the FDIA signals can be estimated and further detected without being affected by the disturbances. This makes the proposed learning observer-based detection and estimation method is robust to disturbances and false declaration of FDIAs can be avoided. Another advantage of the proposed method is that the computing load is low because of the design of a reduced-order learning observer. With a three-cell battery string, a simulation study is employed to verify the effectiveness of proposed detection and estimation method for the FDIAs.