Metal Nanogap Memory: Performances and Switching Mechanism.

The nanogap memory (NGM) device, emerging as a promising nonvolatile memory candidate, has attracted increasing attention for its simple structure, nano/atomic scale size, elevated operating speed, and robustness to high temperatures. In this study, nanogap memories based on Pd, Au, and Pt were fabricated by combining nanofabrication with electromigration technology. Subsequent evaluations of the electrical characteristics were conducted under ambient air or vacuum conditions at room temperature. The investigation unveiled persistent challenges associated with metal NGM devices, including (1) prolonged SET operation time in comparison to RESET, (2) the potential generation of error bits when enhancing switching speeds, and (3) susceptibility to degradation during program/erase cycles. While these issues have been encountered by predecessors in NGM device development, the underlying causes have remained elusive. Employing molecular dynamics (MD) simulation, we have, for the first time, unveiled the dynamic processes of NGM devices during both SET and RESET operations. The MD simulation highlights that the adjustment of the tunneling gap spacing in nanogap memory primarily occurs through atomic migration or field evaporation. This dynamic process enables the device to transition between the high-resistance state (HRS) and the low-resistance state (LRS). The identified mechanism provides insight into the origins of the aforementioned challenges. Furthermore, the study proposes an effective method to enhance the endurance of NGM devices based on the elucidated mechanism.

HBV DNA polymerase regulates tumor cell glycogen to enhance the malignancy of HCC cells.

BACKGROUND: The essential function of HBV DNA polymerase (HBV-DNA-Pol) is to initiate viral replication by reverse transcription; however, the role of HBV-DNA-Pol in HBV-associated HCC has not been clarified. Glycogen phosphorylase L (PYGL) is a critical regulator of glycogenolysis and is involved in tumorigenesis, including HCC. However, it is unknown whether HBV-DNA-Pol regulates PYGL to contribute to HCC tumorigenesis. METHODS: Bioinformatic analysis, real-time quantitative PCR, western blotting, and oncology functional assays were performed to determine the contribution of HBV-DNA-Pol and PYGL to HCC development and glycolysis. The mechanisms of co-immunoprecipitation and ubiquitination were employed to ascertain how HBV-DNA-Pol upregulated PYGL. RESULTS: Overexpression of HBV-DNA-Pol enhanced HCC progression in vitro and in vivo. Mechanistically, HBV-DNA-Pol interacted with PYGL and increased PYGL protein levels by inhibiting PYGL ubiquitination, which was mediated by the E3 ligase TRIM21. HBV-DNA-Pol competitively impaired the binding of PYGL to TRIM21 due to its stronger binding affinity to TRIM21, suppressing the ubiquitination of PYGL. Moreover, HBV-DNA-Pol promoted glycogen decomposition by upregulating PYGL, which led to an increased flow of glucose into glycolysis, thereby promoting HCC development. CONCLUSIONS: Our study reveals a novel mechanism by which HBV-DNA-Pol promotes HCC by controlling glycogen metabolism in HCC, establishing a direct link between HBV-DNA-Pol and the Warburg effect, thereby providing novel targets for HCC treatment and drug development.

Pull-to-Peel of Two-Dimensional Materials for the Simultaneous Determination of Elasticity and Adhesion.

The flexible and clinging nature of ultrathin films requires an understanding of their elastic and adhesive properties in a wide range of circumstances from fabrications to applications. Simultaneously measuring both properties, however, is extremely difficult as the film thickness diminishes to the nanoscale. Here we address such difficulties through peeling by pulling thin films off from the substrates (we thus refer to it as "pull-to-peel"). Particularly, we perform in situ pull-to-peel of graphene and MoS2 films in a scanning electron microscope and achieve simultaneous determination of their Young's moduli and adhesions to gold substrates. This is in striking contrast to other conceptually similar tests available in the literature, including indentation tests (only measuring elasticity) and spontaneous blisters (only measuring adhesion). Furthermore, we show a weakly nonlinear Hooke's relation for the pull-to-peel response of two-dimensional materials, which may be harnessed for the design of nanoscale force sensors or exploited in other thin-film systems.

Machine learning for structure-property mapping of Ising models: Scalability and limitations.

We present a scalable machine learning (ML) framework for predicting intensive properties and particularly classifying phases of Ising models. Scalability and transferability are central to the unprecedented computational efficiency of ML methods. In general, linear-scaling computation can be achieved through the divide-and-conquer approach, and the locality of physical properties is key to partitioning the system into subdomains that can be solved separately. Based on the locality assumption, ML model is developed for the prediction of intensive properties of a finite-size block. Predictions of large-scale systems can then be obtained by averaging results of the ML model from randomly sampled blocks of the system. We show that the applicability of this approach depends on whether the block-size of the ML model is greater than the characteristic length scale of the system. In particular, in the case of phase identification across a critical point, the accuracy of the ML prediction is limited by the diverging correlation length. We obtain an intriguing scaling relation between the prediction accuracy and the ratio of ML block size over the spin-spin correlation length. Implications for practical applications are also discussed. While the two-dimensional Ising model is used to demonstrate the proposed approach, the ML framework can be generalized to other many-body or condensed-matter systems.

Toward High Carrier Mobility and Low Contact Resistance: Laser Cleaning of PMMA Residues on Graphene Surfaces.

ABSTRACT: Poly(methyl methacrylate) (PMMA) is widely used for graphene transfer and device fabrication. However, it inevitably leaves a thin layer of polymer residues after acetone rinsing and leads to dramatic degradation of device performance. How to eliminate contamination and restore clean surfaces of graphene is still highly demanded. In this paper, we present a reliable and position-controllable method to remove the polymer residues on graphene films by laser exposure. Under proper laser conditions, PMMA residues can be substantially reduced without introducing defects to the underlying graphene. Furthermore, by applying this laser cleaning technique to the channel and contacts of graphene field-effect transistors (GFETs), higher carrier mobility as well as lower contact resistance can be realized. This work opens a way for probing intrinsic properties of contaminant-free graphene and fabricating high-performance GFETs with both clean channel and intimate graphene/metal contact.