Waste paper is often underutilized as a low-value recyclable resource and can be a potential source of cellulose nanofibers (CNFs) due to its rich cellulose content. Three different processes, low acid treatment, alkali treatment and bleaching treatment, were used to pretreat the waste paper in order to investigate the effect of different pretreatments on the prepared CNFs, and CNFs obtained from bleached pulp boards were used as control. All sample fibers were successfully prepared into CNFs by 2,2,6,6-tetramethyl-piperidine-1-oxyl (TEMPO) oxidation. It was quite obvious that the bleached CNFs samples showed dense fibrous structures on a scanning electron microscopy (SEM), while needle-like fibers with width less than 20 nm were observed on a transmission electron microscopy (TEM). Meanwhile, the bleaching treatment resulted in a 13.5% increase in crystallinity and a higher TEMPO yield (e.g., BCNF, 60.88%), but a decrease in thermal stability. All pretreated CNFs samples showed narrow particle size distribution, good dispersion stability (zeta potential less than −29.58 mV), good light transmission (higher than 86.5%) and low haze parameters (lower than 3.92%). This provides a good process option and pathway for scalable production of CNFs from waste papers.

No abstract

Machine learning and deep learning have facilitated various successful studies of molecular property predictions. The rapid development of natural language processing and graph neural network (GNN) further pushed the state-of-the-art prediction performance of molecular property to a new level. A geometric graph could describe a molecular structure with atoms as the nodes and bonds as the edges. Therefore, a graph neural network may be trained to better represent a molecular structure. The existing GNNs assumed homogeneous types of atoms and bonds, which may miss important information between different types of atoms or bonds. This study represented a molecule using a heterogeneous graph neural network (MolHGT), in which there were different types of nodes and different types of edges. A transformer reading function of virtual nodes was proposed to aggregate all the nodes, and a molecule graph may be represented from the hidden states of the virtual nodes. This proof-of-principle study demonstrated that the proposed MolHGT network improved the existing studies of molecular property predictions. The source code and the training/validation/test splitting details are available at .

This study aimed to assess the role of pre-designed route on computer tomography urography (CTU) in the ultrasound-guided percutaneous nephrolithotomy (PCNL) for renal calculus.From August 2013 to May 2016, a total of 100 patients diagnosed with complex renal calculus in our hospital were randomly divided into CTU group and control group (without CTU assistance). CTU was used to design a rational route for puncturing in CTU group. Ultrasound was used in both groups to establish a working trace in the operation areas. Patients’ perioperative parameters and postoperative complications were recorded.All operations were successfully performed, without transferring to open surgery. Time of channel establishment in CTU group (6.5 ± 4.3 minutes) was shorter than the control group (10.0 ± 6.7 minutes) (P = .002). In addition, there was shorter operation time, lower rates of blood transfusion, secondary operation, and less establishing channels. The incidence of postoperative complications including residual stones, sepsis, severe hemorrhage, and perirenal hematoma was lower in CTU group than in control group.Pre-designing puncture route on CTU images would improve the puncturing accuracy, lessen establishing channels as well as improve the security in the ultrasound-guided PCNL for complex renal calculus, but at the cost of increased radiation exposure.

Metalens with broadband and high-efficiency focusing functionality is desired in various underwater acoustic applications such as sonar and oceanography. Here we design and demonstrate a metagrating-based lens consisting of spatially sparse and wavelength-scale meta-atoms with optimized structures. With the help of grating diffraction analysis and intelligent optimization algorithm, the reflective metalens enables broadband and high-numerical-aperture focusing for waterborne sound over a 40 kHz-bandwidth for working frequency at 200 kHz. Full-wave numerical simulations unambiguously verify a sharp and high-efficiency focusing of sound wave intensity, with the full width at half maximum (FWHM) at the focal spot being smaller than 0.5λ and thus beating the Rayleigh-Abbe diffraction limit. Our work not only provides an intelligent design paradigm of high-performance metalens, but also presents a potential solution for the development of planar acoustic devices for high-resolution applications.