A new smart smudge attack using CNN

International Journal of Information Security - This paper deals with a well-known problem in the area of the smudge attacks: when a user draws a pattern to unlock the pattern lock on a smartphone...     

Reusable Polyacrylonitrile-Sulfur Extractor of Heavy Metal Ions from Wastewater

Mercury, lead, and cadmium are among the most toxic and carcinogenic heavy metal ions (HMIs), posing serious threats to the sustainability of aquatic ecosystems and public health. There is an urgent need to remove these ions from water by a cheap but green process. Traditional methods have insufficient removal efficiency and reusability. Structurally robust, large surface-area adsorbents functionalized with high-selectivity affinity to HMIs are attractive filter materials. Here, an adsorbent prepared by vulcanization of polyacrylonitrile (PAN), a nitrogen-rich polymer, is reported, giving rise to PAN-S nanoparticles with cyclic π-conjugated backbone and electronic conductivity. PAN-S can be coated on ultra-robust melamine (ML) foam by simple dipping and drying. In agreement with hard/soft acid/base theory, N- and S-containing soft Lewis bases have strong binding to Hg²⁺, Pb²⁺, Cu²⁺, and Cd²⁺, with extraordinary capture efficiency and performance stability. Furthermore, the used filters, when collected and electrochemically biased in a recycling bath, can release the HMIs into the bath and electrodeposit on the counter-electrode as metallic Hg⁰, Pb⁰, Cu⁰, and Cd⁰, and the PAN-S@ML filter can then be reused at least 6 times as new. The electronically conductive PAN-S@ML filter can be fabricated cheaply and holds promise for scale-up applications.     

Theoretical study on a kind of cyclization mechanism of boron trichloride and hexamethyldisilazane to form the borazine ring for SiBCN ceramics precursor

Borazine rings act as a pivotal part in siliconboroncarbonitride ceramics (SiBCN) for high-temperature stability and great resistance to crystallization. A detailed investigation of the ring formation mechanism will guide the design and synthesis of SiBCN to meet application requirements under extreme conditions. Boron trichloride (BCl₃) and hexamethyldisilazane (HN(SiMe₃)₂) are common raw materials for the synthesis of precursors for SiBCN. In this paper, quantum chemical calculation was used to study the cyclization reaction mechanism between BCl₃ and HN(SiMe₃)₂ to form trichloroborazine (TCBZ) at the MP2/6-31G (d,p) level of theory. We discussed the structure properties, reaction pathways, energy barriers, reaction rates, and other aspects in detail. The results show that BCl₃ and HN(SiMe₃)₂ alternately participate in the reaction process, accompanied by the release of trimethylchlorosilane (TMCS), and that the entire reaction shows an absolute advantage in terms of energy. In the Step by step reaction, lower reaction barriers are formed due to the introduction of BCl₃ with more heat released compared to that for the introduction of HN(SiMe₃)₂. The final single-molecule cyclization and TMCS elimination steps are found to be faster compared to all previous bimolecular reactions.     

Progressive NO2 Sensors with Rapid Alarm and Persistent Memory-Type Responses for Wide-Range Sensing Using Antimony Triselenide Nanoflakes

Antimony triselenide (Sb₂Se₃) nanoflake-based nitrogen dioxide (NO₂) sensors exhibit a progressive bifunctional gas-sensing performance, with a rapid alarm for hazardous highly concentrated gases, and an advanced memory-type function for low-concentration (<1 ppm) monitoring repeated under potentially fatal exposure. Rectangular and cuboid shaped Sb₂Se₃ nanoflakes, comprising van der Waals planes with large surface areas and covalent bond planes with small areas, can rapidly detect a wide range of NO₂ gas concentrations from 0.1 to 100 ppm. These Sb₂Se₃ nanoflakes are found to be suitable for physisorption-based gas sensing owing to their anisotropic quasi-2D crystal structure with extremely enlarged van der Waals planes, where they are humidity-insensitive and consequently exhibit an extremely stable baseline current. The Sb₂Se₃ nanoflake sensor exhibits a room-temperature/low-voltage operation, which is noticeable owing to its low energy consumption and rapid response even under a NO₂ gas flow of only 1 ppm. As a result, the Sb₂Se₃ nanoflake sensor is suitable for the development of a rapid alarm system. Furthermore, the persistent gas-sensing conductivity of the sensor with a slow decaying current can enable the development of a progressive memory-type sensor that retains the previous signal under irregular gas injection at low concentrations.     

Nanomaterials for radiotherapeutics-based multimodal synergistic cancer therapy

Over the past decade, numerous studies have attempted to enhance the effectiveness of radiotherapy (external beam radiotherapy and internal radioisotope therapy) for cancer treatment. However, the low radiation absorption coefficient and radiation resistance of tumors remain major critical challenges for radiotherapy in the clinic. With the development of nanomedicine, nanomaterials in combination with radiotherapy offer the possibility to improve the efficiency of radiotherapy in tumors. Nanomaterials act not only as radiosensitizers to enhance radiation energy, but also as nanocarriers to deliver therapeutic units in combating radiation resistance. In this review, we discuss opportunities for a synergistic cancer therapy by combining radiotherapy based on nanomaterials designed for chemotherapy, photodynamic therapy, photothermal therapy, gas therapy, genetic therapy, and immunotherapy. We highlight how nanomaterials can be utilized to amplify antitumor radiation responses and describe cooperative enhancement interactions among these synergistic therapies. Moreover, the potential challenges and future prospects of radio-based nanomedicine to maximize their synergistic efficiency for cancer treatment are identified.

     

Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric

Machine Learning - We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning). Recent studies in PU learning have shown superior...