Discharge of antibiotic-containing wastewater causes environmental pollution and threatens biological and human health. An efficient treatment method for this wastewater is urgently required. We prepared inorganic-organic hybrid MXene-pillararene nanosheets with a large lateral size (5-8 μm). The hybrid nanosheets were stacked on supports via vacuumassisted filtration to prepare membranes with regular parallel slits and an interlayer spacing of 1.36 nm, which were used to purify antibiotic-containing water. Permeance through the membrane increased 100-fold compared with most polymeric and other two-dimensional nanofiltration membranes with similar rejection. This high permeance and rejection was attributed to the large lateral size of the nanosheets, regular interlayer spacing, and electrostatic interaction between the membrane and antibiotics. These membranes will broaden the applications of lamellar materials for the separation of highvalue-added drugs in academia and industry.
Thousands of vulnerabilities are discovered in programs every day, which is extremely harmful to software security. Thus, discovering vulnerabilities in projects has become a central issue. Facing a sustained growth of software complexity and large code size, manual code auditing becomes time-consuming and labor-intensive. With more open source programs available and a high degree of code formalization, it is possible to study features from source code to guide vulnerability discovery work. In this paper, we present a lightweight-assisted vulnerability discovery method using a deep neural network (LAVDNN) to detect weakness and to provide guidance for manual auditing. The method proposed in this paper leverages function names as semantics features to uncover weak functions in large-scale open source programs. First, we extract function names and classify into weak and benign datasets. Then, we construct deep neural networks and compare the performances of different models. According to the experimental results, our method performs well for both C/C++ and Python programs, with the F 2 -score reaching 0.91 and 0.915, respectively. Ultimately, we evaluate the method by comparing with other approaches using the libraries FFmpeg 0.6 and LibTIFF 4.0.6. The results show that the LAVDNN could narrow the range of functions to be analyzed and report more weak functions without any prior vulnerability information. As a lightweight-assisted tool, the LAVDNN significantly reduces the false positive rate and hardly misses weak functions.INDEX TERMS Code auditing, deep neural network, source code, vulnerability discovery.
Supramolecular coordination complexes with solidstate stimuli-responsive characteristics are highly desirable but are rarely reported. Herein, we describe two coordination-driven selfassembled monoanthracene or dianthracene-based hexagonal metallacycles by subtle structure modification. Notably, the dianthracenecontaining hexagon 1 exhibits tricolor mechanochromic and vapochromic characteristics, while the monoanthracene-containing hexagon 4 does not show obvious changes toward mechanical force. Further studies have indicated that changes in hexagon 1, especially the ulterior anthracene of hexagon 1 in the molecular stacking through intermolecular interactions toward external stimuli, are responsible for the above behavioral differences. Furthermore, the present work also demonstrates a novel light-harvesting strategy for achieving high-contrast mechanochromic fluorescence involving solidstate energy transfer from hexagon 1 to an organic carbazole derivant 6 without mechanofluorochromism or tetraphenylethylene derivant 7 exhibiting inconspicuous mechanofluorochromism.
The
development of organic nanoparticles that fluoresce in the
near-infrared, especially in the second near-infrared (NIR-II) window,
improves in vivo fluorescence imaging due to deeper penetration and
higher spatiotemporal resolution. We report two kinds of NIR-II fluorescent
molecules with twisted intramolecular charge-transfer (TICT) and aggregation-induced
emission (AIE) characteristics. The virus-like particles (VLPs) of
simian virus 40 (SV40) were used as templates to encapsulate the molecules
in a well-defined structure (referred to as CH1-SV40 and CH2-SV40).
The CH1-SV40 dots exhibited a highly uniform size of 21.5 nm, strong
fluorescence, high photostability, and good biocompatibility in vitro
and in vivo. Their fluorescence spectrum exhibited a peak at 955 nm,
with a tail extending to 1200 nm. Moreover, the CH1-SV40 dots, with
a quantum yield of 13.03%, enabled blood vessel imaging and image-guided
surgery with a high signal-to-background ratio. Overall, the hybrid
nanoparticles represent a new kind of NIR-II AIE nanoprobes for biomedical
imaging.
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