Explore the vast range of titles available.

A BP/CdS heterostructure-based artificial photonic synapse with an ultra-low power consumption is proposed, presenting great potential in high-performance neuromorphic vision systems. A BP/CdS heterostructure-based artificial photonic synapse with an ultra-low power consumption is proposed, presenting great potential in high-performance neuromorphic vision systems.

Low output in stress and energy in rubbery state has been a bottleneck for wide-spread applications of thermoset shape memory polymers (SMPs). Traditionally, stress or energy storage in thermoset network is through entropy reduction by mechanical deformation or programming. We here report another mechanism for energy storage, which stores energy primarily through enthalpy increase by stretched bonds during programming. As compared to entropy-driven counterparts, which usually have a stable recovery stress from tenths to several MPa and energy output of several tenths MJ/m 3 , our rubbery network achieved a recovery stress of 17.0 MPa and energy output of 2.12 MJ/m 3 in bulk form. The giant stress and energy release in the rubbery state will enhance applications of thermoset SMPs in engineering structures and devices. Energy storage in thermoset shape memory polymers happens through entropy reduction during the programming step, but low energy release is known to be a bottleneck for wide-spread application. Here, the authors show a thermoset network that stores energy primarily through enthalpy increase by bond length change, which leads to an improved energy output.

Herein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and octet have been designed for lightweight structures, it is plausible that other cells exist which might perform better than the existing counterparts. Machine learning technique was used to discover new optimal unit cells. An 8-node cube containing a maximum of 27 elements, which extended into an eightfold unit cell, was taken as representative volume element (RVE). Numerous possible unit cells within the RVE were generated using permutations and combinations through MATLAB coding. Uniaxial compression tests using ANSYS were performed to form a dataset, which was used to train machine learning algorithms and form predictive model. The model was then used to further optimize the unit cells. A total of 20 optimal symmetric unit cells were predicted which showed 51–57% higher capacity than octet cell. Particularly, if the solid rods were replaced by porous biomimetic rods, an additional 130–160% increase in buckling resistance was achieved. Sandwich structures made of these 3D printed optimal symmetric unit cells showed 13–35% higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures.

In the recent decades, with rapid development in computing power and algorithms, machine learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the history of ML is described and the basic process of ML accelerated polymer discovery is summarized. Next, the four steps in this process are reviewed, that is, dataset selection, fingerprinting, ML framework, and new polymer generation. Finally, a couple of main challenges for ML accelerated polymer discovery is presented and the outlooks in this field are prospected. It is expected that this review can service as a useful tool for the people who just step into this field and deepen the understanding for the people who are already in this field. Basic flow for machine learning‐assisted polymer discovery, which consists of nine steps, including Step 1: data collection, Step 2: fingerprinting, Step 3: machine learning prediction, Step 4: machine learning model, Step 5: new data points, Step 6: new polymer structures, Step 7: screening with threshold, Step 8: screening with chemical expertise, and Step 9: desired polymer.

Due to the constraints of agricultural computing resources and the diversity of plant diseases, it is challenging to achieve the desired accuracy rate while keeping the network lightweight. In this paper, we proposed a computationally efficient deep learning architecture based on the mobile vision transformer (MobileViT) for real-time detection of plant diseases, which we called plant-based MobileViT (PMVT). Our proposed model was designed to be highly accurate and low-cost, making it suitable for deployment on mobile devices with limited resources. Specifically, we replaced the convolution block in MobileViT with an inverted residual structure that employs a 7×7 convolution kernel to effectively model long-distance dependencies between different leaves in plant disease images. Furthermore, inspired by the concept of multi-level attention in computer vision tasks, we integrated a convolutional block attention module (CBAM) into the standard ViT encoder. This integration allows the network to effectively avoid irrelevant information and focus on essential features. The PMVT network achieves reduced parameter counts compared to alternative networks on various mobile devices while maintaining high accuracy across different vision tasks. Extensive experiments on multiple agricultural datasets, including wheat, coffee, and rice, demonstrate that the proposed method outperforms the current best lightweight and heavyweight models. On the wheat dataset, PMVT achieves the highest accuracy of 93.6% using approximately 0.98 million (M) parameters. This accuracy is 1.6% higher than that of MobileNetV3. Under the same parameters, PMVT achieved an accuracy of 85.4% on the coffee dataset, surpassing SqueezeNet by 2.3%. Furthermore, out method achieved an accuracy of 93.1% on the rice dataset, surpassing MobileNetV3 by 3.4%. Additionally, we developed a plant disease diagnosis app and successfully used the trained PMVT model to identify plant disease in different scenarios.

Phosphorus (P) removal is a significant issue in wastewater treatment. This study applies Fe-Al composite coagulant to the advanced treatment of different P forms in biological effluent. For 90% total P removal, the dosage of FeCl 3 -AlCl 3 composite coagulant reduces by 27.19% and 43.28% than FeCl 3 and AlCl 3 only, respectively. Changes in effluent P forms could explain the phenomenon of composite coagulant dosage reduction. The suspended P in the effluent of composite coagulant is easier removed by precipitation than single coagulant. In this study, the hydrolysis speciations of Fe 3+ , Fe 2+ , and Al 3+ at a pH range are calculated by Visual MINTEQ. Changes in the morphology of metal hydroxides correlate with P removal at pH 4–9. Besides, analyses of scanning electron microscope (SEM), Fourier transformed infrared (FTIR), and X-ray photoelectron spectroscopy (XPS) are performed on the coagulation precipitations. Fe 2+ reacts directly with P to form flocs of Fe 3 (PO 4 ) 2 , and Al 2 (SO 4 ) 3 assists in the sedimentation of the small-volume flocs. Al 13 is a significant hydrolysis product of Al 3+ , and Fe and P would substitute for the peripheral Al VI of the Al 13 structure to form stable Fe–O–Al covalent bonds.

We report a molecular assay, Methyl-HiC, that can simultaneously capture the chromosome conformation and DNA methylome in a cell. Methyl-HiC reveals coordinated DNA methylation status between distal genomic segments that are in spatial proximity in the nucleus, and delineates heterogeneity of both the chromatin architecture and DNA methylome in a mixed population. It enables simultaneous characterization of cell-type-specific chromatin organization and epigenome in complex tissues.

The extracellular matrix (ECM) is a significant factor in cancer progression. Collagens, as the main component of the ECM, are greatly remodeled alongside cancer development. More and more studies have confirmed that collagens changed from a barrier to providing assistance in cancer development. In this course, collagens cause remodeling alongside cancer progression, which in turn, promotes cancer development. The interaction between collagens and tumor cells is complex with biochemical and mechanical signals intervention through activating diverse signal pathways. As the mechanism gradually clears, it becomes a new target to find opportunities to diagnose and treat cancer. In this review, we investigated the process of collagen remodeling in cancer progression and discussed the interaction between collagens and cancer cells. Several typical effects associated with collagens were highlighted in the review, such as fibrillation in precancerous lesions, enhancing ECM stiffness, promoting angiogenesis, and guiding invasion. Then, the values of cancer diagnosis and prognosis were focused on. It is worth noting that several generated fragments in serum were reported to be able to be biomarkers for cancer diagnosis and prognosis, which is beneficial for clinic detection. At a glance, a variety of reported biomarkers were summarized. Many collagen-associated targets and drugs have been reported for cancer treatment in recent years. The new targets and related drugs were discussed in the review. The mass data were collected and classified by mechanism. Overall, the interaction of collagens and tumor cells is complicated, in which the mechanisms are not completely clear. A lot of collagen-associated biomarkers are excavated for cancer diagnosis. However, new therapeutic targets and related drugs are almost in clinical trials, with merely a few in clinical applications. So, more efforts are needed in collagens-associated studies and drug development for cancer research and treatment.

Liquid crystals (LCs), as the remarkable optical materials possessing stimuli-responsive property and optical modulation property simultaneously, have been utilized to fabricate a wide variety of optical devices. Integrating the LCs and receptors together, LC biosensors aimed at detecting various biomolecules have been extensively explored. Compared with the traditional biosensing technologies, the LC biosensors are simple, visualized, and efficient. Owning to the irreplaceable superiorities, the research enthusiasm for the LC biosensors is rapidly rising. As a result, it is necessary to overview the development of the LC biosensors to guide future work. This article reviews the basic theory and advanced applications of LC biosensors. We first discuss different mesophases and geometries employed to fabricate LC biosensors, after which we introduce various detecting mechanisms involved in biomolecular detection. We then focus on diverse detection targets such as proteins, enzymes, nucleic acids, glucose, cholesterol, bile acids, and lipopolysaccharides. For each of these targets, the development history and state-of-the-art work are exhibited in detail. Finally, the current challenges and potential development directions of the LC biosensors are introduced briefly.

Herein, we report hybrid fibrous artificial muscles with reversible actuation, i.e., expansion upon cooling and contraction upon heating, under external compression. Although many fibrous polymeric artificial muscles by twist insertion in precursor fibers have been developed, most of them cannot reversibly actuate without an external tensile load. While heterochiral Nylon muscles can reversibly actuate under external compressive load, the compressive stress applied is low (0.078 MPa). In this study, we inserted pre-tensioned polymeric fibers with reversible actuation into pre-compressed helical metallic spring and obtained hybrid fibrous artificial muscles. We employed two types of two-way shape memory polymers, one type of fishing line artificial muscle, and seven types of helical springs in preparing seven types of hybrid muscles. A structural mechanics model was developed, and numerical simulation was conducted to evaluate the effect of the design parameters on the actuation strain. It is found that all the hybrid muscles were free-standing (reversibly actuate without external load) and beyond free-standing (reversibly actuate under external compression load). As an example, one hybrid muscle actuated reversibly under 24 MPa compressive stress without buckling. We expect that this study will open new opportunities for the use of fibrous artificial muscles as linear actuators in soft robotics or other applications that need reversible actuation under external compression.