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"Tian, Jia"
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Aspects of three-dimensional C-metric
A
bstract
In this work, we present an extensive analysis of the thermodynamics and holographic properties of three-dimensional C-metrics in the FG gauge, where we find that the free energy is equal to the Euclidean on-shell action with a generic conformal factor. For the black hole solutions we find that Smarr relation and the first law of thermodynamics can be formulated when the contributions of the boundary entropy are considered. We also compute holographic entanglement entropy following the AdS/BCFT formalism. By comparing the free energies of different bulk solutions with a fixed flat torus boundary geometry, we find that a specific type of accelerating black hole is dominant in the high temperature regime.
Journal Article
Imparting multi-functionality to covalent organic framework nanoparticles by the dual-ligand assistant encapsulation strategy
The potential applications of covalent organic frameworks (COFs) can be further developed by encapsulating functional nanoparticles within the frameworks. However, the synthesis of monodispersed core@shell structured COF nanocomposites without agglomeration remains a significant challenge. Herein, we present a versatile dual-ligand assistant strategy for interfacial growth of COFs on the functional nanoparticles with abundant physicochemical properties. Regardless of the composition, geometry or surface properties of the core, the obtained core@shell structured nanocomposites with controllable shell-thickness are very uniform without agglomeration. The derived bowl-shape, yolk@shell, core@satellites@shell nanostructures can also be fabricated delicately. As a promising type of photosensitizer for photodynamic therapy (PDT), the porphyrin-based COFs were grown onto upconversion nanoparticles (UCNPs). With the assistance of the near-infrared (NIR) to visible optical property of UCNPs core and the intrinsic porosity of COF shell, the core@shell nanocomposites can be applied as a nanoplatform for NIR-activated PDT with deep tissue penetration and chemotherapeutic drug delivery.
Despite many reports on nanoparticle-covalent organic frameworks (COF) composites, a universal strategy for the synthesis of monodisperse core-shell structured COF nanocomposites remains challenging. Here, the authors develop a strategy for interfacial growth of highly crystalline COFs on functional nanoparticles with abundant optical, electrical and magnetic properties.
Journal Article
Superhydrophobic, flame-retardant and conductive cotton fabrics via layer-by-layer assembly of carbon nanotubes for flexible sensing electronics
Functional textiles are ideal substrates for wearable electronics. Herein, superhydrophobic, flame-retardant and conductive cotton fabrics were fabricated by sequential assembly of poly(ethylenimine), ammonium polyphosphate and carbon nanotubes, followed by post-treatment with poly(dimethylsiloxane). The resulting fabrics possessed excellent superhydrophobic stability toward acid, alkali, organic solvent, UV irradiation, abrasion and long-time laundering. Meanwhile, when suffering to fire, the coated fabric could generate an efficient char layer and extinguish the fire to protect the cotton fiber from forming flame. Furthermore, this conductive cotton fabric exhibited stable sensing ability in contact with water droplets, showing wide potential application in wearable electronics as multifunctional smart textiles.
Journal Article
A facile way to construct sensor array library via supramolecular chemistry for discriminating complex systems
Differential sensing, which discriminates analytes via pattern recognition by sensor arrays, plays an important role in our understanding of many chemical and biological systems. However, it remains challenging to develop new methods to build a sensor unit library without incurring a high workload of synthesis. Herein, we propose a supramolecular approach to construct a sensor unit library by taking full advantage of recognition and assembly. Ten sensor arrays are developed by replacing the building block combinations, adjusting the ratio between system components, and changing the environment. Using proteins as model analytes, we examine the discriminative abilities of these supramolecular sensor arrays. Then the practical applicability for discriminating complex analytes is further demonstrated using honey as an example. This sensor array construction strategy is simple, tunable, and capable of developing many sensor units with as few syntheses as possible.
The development of sensor unit libraries that require minimal synthetic effort is challenging. Here, the authors report the preparation of sensor array libraries based on a supramolecular approach that relies on the recognition and assembly of macrocyclic amphiphiles.
Journal Article
Decomposition characteristics of rice straw returned to the soil in northeast China
The straw return method has been increasingly implemented in rice production in Northeast China. In-depth studies on the characteristics of rice straw decomposition are of great importance for achieving sustainable agricultural development. In this study, the nylon mesh bagging method was used to study the patterns of rice straw decomposition and nutrient release during a 5-year period of rice growth. The results showed that straw decomposition occurred mainly during the first 3 years after straw return, with the cumulative amount of decomposition reaching 77.0%, and that the rate of straw decomposition decreased linearly with time. The release of carbon, nitrogen, cellulose and hemicellulose occurred mainly during the first and second years after straw return. Moreover, the release of phosphorus and potassium occurred mainly during the first month after straw return, and lignin was released at various rates throughout the entire study period. These results indicated that straw returned to the soil acts both as a source of phosphorus and potassium in the short term and as a source of nitrogen and carbon in the long term during the rice growing season in Northeast China.
Journal Article
Tailored self-assembled photocatalytic nanofibres for visible-light-driven hydrogen production
The creation of efficient artificial systems that mimic natural photosynthesis represents a key current challenge. Here, we describe a high-performance recyclable photocatalytic core–shell nanofibre system that integrates a cobalt catalyst and a photosensitizer in close proximity for hydrogen production from water using visible light. The composition, microstructure and dimensions—and thereby the catalytic activity—of the nanofibres were controlled through living crystallization-driven self-assembly. In this seeded growth strategy, block copolymers with crystallizable core-forming blocks and functional coronal segments were coassembled into low-dispersity, one-dimensional architectures. Under optimized conditions, the nanofibres promote the photocatalytic production of hydrogen from water with an overall quantum yield for solar energy conversion to hydrogen gas of ~4.0% (with a turnover number of >7,000 over 5 h, a frequency of >1,400 h−1 and a H2 production rate of >0.327 μmol h−1 with 1.34 μg of catalytic polymer (that is, >244,300 μmol h−1 g−1 of catalytic polymer)).Artificial systems capable of photocatalytic hydrogen production are not typically based on precisely controlled scaffolds. Now, statistical seeded crystallization of block copolymers—bearing either a pendant cobalt catalyst or a photosensitizer—from solution has been shown to yield recyclable, colloidally stable nanofibres that can be tailored to promote photocatalytic hydrogen production from water.
Journal Article
Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling
ObjectivesThe tyrosine kinase inhibitor (TKI)-sensitive mutations of the epidermal growth factor receptor (EGFR) gene is essential in the treatment of lung adenocarcinoma. To overcome the difficulty of EGFR gene test in situations where surgery and biopsy samples are too risky to obtain, we tried a noninvasive imaging method using radiomics features and random forest models.MethodsFive hundred three lung adenocarcinoma patients who received surgery-based treatment were included in this study. The diagnosis and EGFR gene test were based on resections. TKI-sensitive mutations were found in 60.8% of the patients. CT scans before any invasive operation were gathered and analyzed to extract quantitative radiomics features and build random forest classifiers to identify EGFR mutants from wild types. Clinical features (sex and smoking history) were added to the image-based model. The model was trained on a set of 345 patients and validated on an independent test group (n = 158) using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.ResultsThe performance of the random forest model with 94 radiomics features reached an AUC of 0.802. Its AUC was further improved to 0.828 by adding sex and smoking history. The sensitivity and specificity are 60.6% and 85.1% at the best diagnostic decision point.ConclusionOur results showed that radiomics could not only reflect the genetic differences among tumors but also have diagnostic value and the potential to be a diagnostic tool.Key Points• Radiomics provides a potential noninvasive method for the prediction of EGFR mutation status.• In situations where surgeries and biopsy are not available, CT image-based radiomics models could help to make treatment decisions.• The accuracy, sensitivity, and specificity still need to be improved before the image-based EGFR identifier could be used in clinics.
Journal Article
Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data
Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user’s quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model’s potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.
Journal Article
Negative Rényi entropy and brane intersection
A
bstract
In this work, we revisit the calculation of Rényi entropy in AdS
3
/(B)CFT
2
. We find that gravity solutions with brane intersection will lead to negative Rényi entropy.
Journal Article