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Clinical phototheranostic agents suffer from low absorption in near-infrared (NIR) region, decreasing singlet oxygen quantum yield (1O2QY) caused by aggregation in water, and low photothermal conversion efficiency (PCE), all of which are factors weakening their phototheranostic efficacy. Herein, we designed and synthesized a donor-acceptor-donor (D-A-D) structured boron-dipyrromethene derivative (B-2TPA) which exhibited NIR absorption and fluorescence. After being encapsulated in amphiphilic distearoyl phosphoethanolamine polyethyleneglycol 2000 (DSPE-PEG-2000), the water-soluble B-2TPAnanoparticles (NPs) had increasing 1O2QY (6.7%) due to the intermolecular aggregation-induced decrease in the energy gap between singlet and triplet excited states. Moreover, the quenched fluorescence and stable twisted intramolecular charge transfer in aggregates further increased the PCE of B-2TPANPs to 60.1%. In vitroand in vivostudies confirmed that B-2TPANPs could be used in NIR fluorescence and photoacoustic imaging-guided synergistic photodynamic and photothermal therapy in tumor treatment.

We aimed to determine the most efficient quantitative parameters to establish a contrast‐enhanced ultrasound (US) assessment system for distinguishing between benign and malignant thyroid nodules. A total of 167 patients with thyroid solitary nodules had the diagnosis confirmed by surgery or fine‐needle aspiration. Quantitative contrast‐enhanced US indicators (time to peak, time from peak to one‐half, ascend slope, descend slope, peak intensity, and area under the curve [AUC]) were gathered in nodule and perinodule areas. Univariate and multivariate logistic regression analyses were performed. Receiver operating characteristic curves were generated. Sensitivities, specificities, and positive and negative predictive values were calculated to identify the best cutoff value. The univariate logistic regression model showed that the peak intensity, ascend slope, descend slope, and AUC were significant indicators for discriminating benign from malignant nodules under contrast‐enhanced US (P< .0001). For thyroid nodules, low peak intensity, ascend slope, and AUC and high descend slope values were significant indicators of malignancy. However, in perinodule areas, high peak intensity, ascend slope, and AUC and low descend slope values were significantly associated with malignancies. The cutoff values for the nodule peak intensity, ascend slope, descend slope, and AUC were 20.75, 0.91, −0.2, and 1818.23, respectively. The cutoff values for the ratios of the nodule versus perinodule peak intensity, ascend slope, descend slope, and AUC were 0.90, 0.95, 0.96, and 0.96. The nodule‐to‐perinodule peak intensity ratio showed the best diagnostic efficiency, with 80.41% sensitivity and 80.00% specificity. Quantitative contrast‐enhanced US indicators help discriminate benign from malignant thyroid nodules. The nodule‐to‐perinodule peak intensity ratio showed the best diagnostic efficiency.

The main factor contributing to the decline in effective fracture width and conductivity is proppant embedding into the fracture surface. In the deep shale's high-temperature, high-pressure, and high-stress environment, the rheological properties of rock cause proppant embedding to be deeper. Additionally, the effect of hydraulic fracture is difficult to maintain after fracturing, which causes a sharp decline in cumulative production. In this paper, the Hertz contact theory is used to establish a long-term fracture conductivity model that incorporates the two embedding behaviors of proppant elastic deformation and reservoir creep deformation. Through time integration, the variation of long-term fracture conductivity is obtained. The experimental data and the theoretical model agree well. The results show that long-term fracture conductivity gradually decreases as the proppant progresses from the elastic embedding stage to the creep embedding stage. The elastic modulus, viscoelastic coefficient, and particle size significantly impact on the fracture width. The rock's elastic modulus and viscoelastic coefficient have a negligible impact on the long-term fracture conductivity, which is positively correlated with sand concentration, proppant particle size, and elastic modulus. In this research, an accurate and effective analysis model is proposed to quantify the long-term fracture conductivity, reveal the hydraulic fracture closure mechanism of deep shale under high temperature and high stress, and provide technological solutions for long-term maintenance of high conductivity fracture channels, which is useful to increase deep shale production efficiency, lower the production decline rate, and extend the stable production cycle.

This paper proposes a new interatomic potential energy neural network, AisNet, which can efficiently predict atomic energies and forces covering different molecular and crystalline materials by encoding universal local environment features, such as elements and atomic positions. Inspired by the framework of SchNet, AisNet consists of an encoding module combining autoencoder with embedding, the triplet loss function and an atomic central symmetry function (ACSF), an interaction module with a periodic boundary condition (PBC), and a prediction module. In molecules, the prediction accuracy of AisNet is comparabel with SchNet on the MD17 dataset, mainly attributed to the effective capture of chemical functional groups through the interaction module. In selected metal and ceramic material datasets, the introduction of ACSF improves the overall accuracy of AisNet by an average of 16.8% for energy and 28.6% for force. Furthermore, a close relationship is found between the feature ratio (i.e., ACSF and embedding) and the force prediction errors, exhibiting similar spoon-shaped curves in the datasets of Cu and HfO2. AisNet produces highly accurate predictions in single-commponent alloys with little data, suggesting the encoding process reduces dependence on the number and richness of datasets. Especially for force prediction, AisNet exceeds SchNet by 19.8% for Al and even 81.2% higher than DeepMD on a ternary FeCrAl alloy. Capable of processing multivariate features, our model is likely to be applied to a wider range of material systems by incorporating more atomic descriptions.