Thermally Responsive Fibers for Versatile Thermoactivated Protective Fabrics

Smart textiles with good mechanical adaptability play an important role in personal protection, health monitoring, and aerospace applications. However, most of the reported thermally responsive polymers has long response time and poor processability, comfort, and wearability. Skin-core structures of thermally responsive fibers with multiple commercial fiber cores and temperature-responsive hydrogel skins are designed and fabricated, which exhibit rapid mechanical adaptability, good thermohardening, and thermal insulation. This universal method enables tight bonding between various commercial fiber cores and hydrogel skins via specific covalently anchored networks. At room temperature, prepared fibers show softness, flexibility, and skin compatibility similar to those of ordinary fibers. As temperature rises, smart fibers become hard, rigid, and self-supporting. The modulus of hydrogel skin increases from 304% to 30883%, showing good mechanoadaptability and impact resistance owing to the synergy between hydrophobic interactions and ionic bonding. Moreover, this synergistic effect leads to an increase in heat absorption, and fibers exhibit good thermal insulation, which reduces the contact temperature of the body surface by ≈25 °C under the external temperature of 95 °C, effectively preventing thermal burns. Notably, the active mechanoadaptability of these smart fibers using conductive fibers as cores is demonstrated. This study provides feasibility for fabricating environmentally adaptive intelligent textiles.     

2D Bi2O2Te Semiconductor with Single-Crystal Native Oxide Layer

Following logic in the silicon semiconductor industry, the existence of native oxide and suitable fabrication technology is essential for 2D semiconductors in planar integronics, which are surface-sensitive to typical coating technologies. To date, very few types of integronics are found to possess this feature. Herein, the 2D Bi₂O₂Te developed recently is reported to possess large-area synthesis and controllable thermal oxidation behavior toward single-crystal native oxides. This shows that surface-adsorbed oxygen atoms are inclined to penetrate across [Bi₂O₂]_n²ⁿ⁺ layers and bond with the underlying [Te]_n²ⁿ⁻ at elevated temperatures, transforming directly into [TeO₄]_n²ⁿ⁻ with the basic architecture remaining stable. The oxide can be adjusted to form in an accurate layer-by-layer manner with a low-stress sharp interface. The native oxide Bi₂TeO₆ layer (bandgap of ≈2.9 eV) exhibits visible-light transparency and is compatible with wet-chemical selective etching technology. These advances demonstrate the potential of Bi₂O₂Te in planar-integrated functional nanoelectronics such as tunnel junction devices, field-effect transistors, and memristors.     

A 2.20 eV Bandgap Polymer Donor for Efficient Colorful Semitransparent Organic Solar Cells

Semitransparent organic solar cells (ST-OSCs) have attracted increasing attention due to their promising prospect in building-integrated photovoltaics. Generally, efficient ST-OSCs with good average visible transmittance (AVT) can be realized by developing active layer materials with light absorption far from the visible light range. Herein, the development of ultrawide bandgap polymer donors with near-ultraviolet absorption, paired with near-infrared acceptors, is proposed to achieve high-performance ST-OSCs. The key points for the design of ultrawide bandgap polymers include constructing donor–donor type conjugated skeleton, suppressing the quinoidal resonance effect, and minimizing the twist of conjugated skeleton via noncovalent conformational locks. As a proof of concept, a polymer named PBOF with an optical bandgap of 2.20 eV is synthesized, which exhibited largely reduced overlap with the human eye photopic response spectrum and afforded a power conversion efficiency (PCE) of 16.40% in opaque device. As a result, ST-OSCs with a PCE over 10% and an AVT over 30% are achieved without optical modulation. Moreover, colorful ST-OSCs with visual aesthetics can be achieved by tuning the donor/acceptor weight ratio in active layer benefiting from the ultrawide bandgap nature of PBOF. This study demonstrates the great potential of ultrawide bandgap polymers for efficient colorful ST-OSCs.     

Macrophage Membrane-Coated Nanoparticles Sensitize Glioblastoma to Radiation by Suppressing Proneural–Mesenchymal Transformation in Glioma Stem Cells

Radiotherapy is identified as a crucial treatment for patients with glioblastoma, but recurrence is inevitable. The efficacy of radiotherapy is severely hampered partially due to the tumor evolution. Growing evidence suggests that proneural glioma stem cells can acquire mesenchymal features coupled with increased radioresistance. Thus, a better understanding of mechanisms underlying tumor subclonal evolution may develop new strategies. Herein, data highlighting a positive correlation between the accumulation of macrophage in the glioblastoma microenvironment after irradiation and mesenchymal transdifferentiation in glioblastoma are presented. Mechanistically, elevated production of inflammatory cytokines released by macrophages promotes mesenchymal transition in an NF-κB-dependent manner. Hence, rationally designed macrophage membrane-coated porous mesoporous silica nanoparticles (MMNs) in which therapeutic anti-NF-κB peptides are loaded for enhancing radiotherapy of glioblastoma are constructed. The combination of MMNs and fractionated irradiation results in the blockage of tumor evolution and therapy resistance in glioblastoma-bearing mice. Intriguingly, the macrophage invasion across the blood-brain barrier is inhibited competitively by MMNs, suggesting that these nanoparticles can fundamentally halt the evolution of radioresistant clones. Taken together, the biomimetic MMNs represent a promising strategy that prevents mesenchymal transition and improves therapeutic response to irradiation as well as overall survival in patients with glioblastoma.     

Proximity-Induced Fully Ferromagnetic Order with Eightfold Magnetic Anisotropy in Heavy Transition Metal Oxide CaRuO3

Ferromagnetic materials with a strong spin-orbit coupling (SOC) have attracted much attention in recent years because of their exotic properties and potential applications in energy-efficient spintronics. However, such materials are scarce in nature. Here, a proximity-induced paramagnetic to ferromagnetic transition for the heavy transition metal oxide CaRuO₃ in (001)-(LaMnO₃/CaRuO₃) superlattices is reported. Anomalous Hall effect is observed in the temperature range up to 180 K. Maximal anomalous Hall conductivity and anomalous Hall angle are as large as ∼15 Ω⁻¹ cm⁻¹ and ∼0.93%, respectively, by one to two orders of magnitude larger than those of the typical 3d ferromagnetic oxides such as La_0.67Sr_0.33MnO₃. Density functional theory calculations indicate the existence of avoid band crossings in the electronic band structure of the ferromagnetic CRO layer, which enhances Berry curvature thus strong anomalous Hall effects. Further evidences from polarized neutron reflectometry show that the CaRuO₃ layers are in a fully ferromagnetic state (∼0.8 μ_B/Ru), in sharp contrast to the proximity-induced canted antiferromagnetic state in 5d oxides SrIrO₃ and CaIrO₃ (∼0.1 μ_B/Ir). More than that, the magnetic anisotropy of the (001)-(LaMnO₃/CaRuO₃) superlattices is eightfold symmetric, showing potential applications in the technology of multistate data storage.     

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.     

Mixed reality depth contour occlusion using binocular similarity matching and three-dimensional contour optimisation

Virtual Reality - Mixed reality applications often require virtual objects that are partly occluded by real objects. However, previous research and commercial products have limitations in terms of...     

Novel,liquid crystal formula to overcome LTPS LCD image sticking without adjustment of gamma code,optical, VHR and IS evaluation

In this article, novel n-FFS liquid crystal material from Slichem was used in order to elevate the anti-backlight long aging effect; a trace amount of free radical trap HALS-like additive was added, the principle of which can be expressed via Denisov cycle, confirmed by measuring the temporal VHR changes in test cells; three different resolution (FHD ~ QHD) models paired with N19 and N21 were selected for testing by comparing the actual panel. It is verified that the optimized material greatly improves the image sticking performance of the panel and keeping optical performance.     

Semi-supervised transformable architecture search for feature distillation

Pattern Analysis and Applications - The designed method aims to perform image classification tasks efficiently and accurately. Different from the traditional CNN-based image classification methods,...     

DDNSR: a dual-input degradation network for real-world super-resolution

Pattern Analysis and Applications - Recently, Real-World Super-Resolution has become one of the most popular research fields in the scope of Single Image Super-Resolution, as it focuses on...