Conversion of THz refractive index variation to detectable voltage change realized by a graphene-based Brewster angle device.

The Brewster effect has been previously reported as an essential mechanism for terahertz (THz) wave sensing application. However, generally in a sensing application, a complex rotation apparatus is required for detecting the slight change in Brewster angle. Here, we propose a graphene-based Brewster angle device operating at a specific terahertz frequency capable of sensing the refractive index at a fixed incident angle. In other words, our sensing device could avoid the impact of Brewster angle shift and eliminate the need for high-precision rotating equipment, which is usually required in traditional sensing applications. The conversion from the refractive index to a Volt-level detectable voltage roots from the tunability of graphene's Fermi level in the external electrical field. A linear correlation between the output voltage and the background refractive index is observed and theocratically analyzed. Furthermore, we present the improvement of our device in terms of sensing range and sensitivity by adjusting the permittivity of the dielectric substrate. As a demonstration of our proposed device, a detection range of 1.1-2.4 and a sensitivity of 20.06 V/RIU for refractive index is achieved on a high-resistance silicon substrate operating at 0.3 THz.

Enhancing the efficiency of graphene-based THz modulator by optimizing the Brewster angle.

The gate-controllable electronical property of graphene provides a possibility of active tuning property for THz modulator. However, the common modulation technology which only depends on voltage cannot solve the problem of power consumption limitation in communication applications. Here, we demonstrated a Brewster angle-controlled graphene-based THz modulator, which could achieve a relatively high modulation depth with low voltage. First, we explored the complex relationships among the Brewster angles, reflection coefficients and the conductivities of graphene. Then, we further investigated the optimal incident angle selection based on the unusual reflection effect which occurs at Brewster angle. Finally, an improved scheme by dynamically adjusting the incident angle was proposed in this paper. It would make it possible that the modulator could achieve a modulation depth of more than 90% with a Fermi level as low as 0.2eV at any specific frequency in the range of 0.4THz-2.2THz. This research will help to realize a THz modulator with high-performance and ultra-low-power in quantities of applications, such as sensing and communication.

Zr-Doped h-BN Monolayer: A High-Sensitivity Atmospheric Pollutant-Monitoring Sensor.

In the post-epidemic era, industrial production has gradually recovered, and the attendant air pollution problem has attracted much attention. In this study, the Zr-doped h-BN monolayer (Zr-BN) is proposed as a new gas sensor for air pollution. Based on density functional theory (DFT), we calculated and compared the adsorption energies (Eads), geometric parameters, the shortest distance between gas and substrate (dsub/gas), density of states (DOS), electron localization function (ELF), charge density difference (CDD), band structure, band gap energy change rate (ΔEg), and sensitivity (S) of Zr-BN adsorption systems (SO2F2, SOF2, SO2, NO, and CO2 adsorption systems). The results show that Zr-BN had strong adsorption and high sensitivity to the above-mentioned polluted gases, and the sensitivity was in the order of SOF2 > SO2F2 > CO2 > SO2 > NO. Therefore, this study provides a theoretical basis for the preparation of Zr-BN gas sensors and provides new ideas and methods for the development of other gas sensors.

As-Doped h-BN Monolayer: A High Sensitivity and Short Recovery Time SF6 Decomposition Gas Sensor.

SF6 is a common insulating medium of gas-insulated switchgear (GIS). However, it is inevitable that SF6 will be decomposed due to partial discharge (PD) in GIS, which will cause hidden dangers to the safe and stable operation of equipment. Based on the DFT method, the two-dimensional nano-composite As-doped h-BN (As-BN) monolayer was proposed. By modeling and calculating, the ability of an As-BN monolayer as a specific sensor for SO2F2 (compared with an H2O adsorption system and CO2 adsorption system) was evaluated by parameters such as the binding energy (Eb), adsorption energy (Eads), transfer charge (ΔQ), geometric structure parameters, the total density of states (TDOS), band structure, charge difference density (CDD), electron localization function (ELF), sensitivity (S), and recovery time (τ). The results showed that an As-BN monolayer showed strong adsorption specificity, high sensitivity, and short recovery time for SO2F2 gas molecules. Therefore, the As-BN monolayer sensor has great application potential in the detection of SF6 decomposition gases.

Transition-Metal Carbides as Hydrogen Evolution Reduction Electrocatalysts: Synthetic Methods and Optimization Strategies.

With the strengths of zero carbon emission and high gravimetric energy density, hydrogen energy is recognized as a primary choice for future energy supply. Electrochemical water splitting provides a promising strategy for effective and sustainable hydrogen production through renewable electricity, and one of the immediate challenges toward its large-scale application is the availability of low-cost and efficient electrocatalysts for the hydrogen evolution reaction (HER). Given the enormous efforts in the exploration of potential transition-metal carbide (TMC) electrocatalysts, this review aims to summarize the recent advances in synthetic methods and optimization strategies of TMC electrocatalysts. Additionally, the perspectives for the development of novel efficient TMC-based catalysts are also proposed.

Relationship between the Electrical Characteristics of Molecules and Fast Streamers in Ester Insulation Oil.

The effects of C=C, ester and ß-H groups on the ionization potential (IP) and electron affinity (EA) of molecules in natural ester insulation oil were investigated by density functional theory (DFT). The major contribution to the highest occupied molecular orbital (HOMO) comes from the carbon atoms adjacent to C=C. Thus, the IPs of triglycerides decrease as the number of C=C double bonds increases. The C=C in alkanes may also lower the IP. However, the ß-H in triglycerides has little effect on the IP, and C=C and ß-H have only a small effect on the EAs of the triglycerides because of the major contributions of atoms near the ester group in triglycerides to the lowest unoccupied molecular orbital (LUMO). This study calculated the IPs of 53 kinds of molecules in FR3, which are significantly lower compared with those of molecules in mineral oil (MO) and trimethylolpropane triester without C=C. However, the lightning impulse breakdown voltage (LI Vb) of trimethylolpropane triester is still significantly lower than that of MO at the large gap. Therefore, the transition from slow to fast streamers under low lighting impulse voltage is determined by the ester group rather than by C=C and ß-H. The ester group may attract more electrons, impacting itself more compared to alkane in MO and facilitating the transition from slow to fast streamers.

Surface-Electron Coupling for Efficient Hydrogen Evolution.

Maximizing the activity of materials towards the alkaline hydrogen evolution reaction while maintaining their structural stability under realistic working conditions remains an area of active research. Herein, we report the first controllable surface modification of graphene(G)/V8 C7 heterostructures by nitrogen. Because the introduced N atoms couple electronically with V atoms, the V sites can reduce the energy barrier for water adsorption and dissociation. Investigation of the multi-regional synergistic catalysis on N-modified G/V8 C7 by experimental observations and density-functional-theory calculations reveals that the increase of electron density on the epitaxial graphene enable it to become favorable for H* adsorption and the subsequent reaction with another H2 O molecule. This work extends the range of surface-engineering approaches to optimize the intrinsic properties of materials and could be generalized to the surface modification of other transition-metal carbides.

Ganoderma-Like MoS2 /NiS2 with Single Platinum Atoms Doping as an Efficient and Stable Hydrogen Evolution Reaction Catalyst.

Herein, a unique ganoderma-like MoS2 /NiS2 hetero-nanostructure with isolated Pt atoms anchored is reported. This novel ganoderma-like heterostructure can not only efficiently disperse and confine the few-layer MoS2 nanosheets to fully expose the edge sites of MoS2 , and provide more opportunity to capture the Pt atoms, but also tune the electronic structure to modify the catalytic activity. Because of the favorable dispersibility and exposed large specific surface area, single Pt atoms can be easily anchored on MoS2 nanosheets with ultrahigh loading of 1.8 at% (the highest is 1.3 at% to date). Owing to the ganoderma-like structure and platinum atoms doping, this catalyst shows Pt-like catalytic activity for the hydrogen evolution reaction with an ultralow overpotential of 34 mV and excellent durability of only 2% increase in overpotential for 72 h under the constant current density of 10 mA cm-2 .

Regulation of the Interface Compatibility of the 3D-Printing Interpenetration Networks Toward Reduced Structure Anisotropy and Enhanced Performances.

Digital light processing three-dimensional (DLP 3D) printing, as a promising manufacturing technology with the capability of fabricating 3D objects with complex shapes, typically develops inconsistent material properties due to the stair-stepping effect caused by weak layer-interface compatibility. Here, we report the regulation of the interface compatibility of the 3D-printing resin with versatile photocuring characteristics and the subsequent mechanical, thermal, and dielectric performances by introducing the interpenetration network (IPN). The preparation procedures, interface structure, flexural and tensile strength, modulus, and dielectric performances of the IPN are presented. The greater penetration depth in 3D printing and the subsequently thermocured epoxy network passing through the printing interface synergistically enhance the interface compatibility of 3D-printing samples, with an unobvious printing texture on the surface of the 3D-printing objects. The mechanical performances of the IPN demonstrate little anisotropy, with a bending strength twice as much as the photosensitive resin. Dynamic mechanical analysis of the IPN indicates that the storage modulus increases by 70% at room temperature and the glass transition temperature (Tg) increases by 57%. The dielectric performance of the IPN demonstrates a 36% decrease in dielectric constant and a 28.4% increase in breakdown strength. Molecular dynamics studies have shown that the IPN takes on higher nonbonded energies and hydrogen bonds than the photosensitive resin, indicating a stronger bonding force between molecular chains, thus leading to better physical properties. These results illustrate the effectiveness of the IPN toward enhanced 3D-printing interlayer compatibility for excellent mechanical, thermal, and electrical performances.

Investigation on Adsorption of Polar Molecules in Vegetable Insulating Oil by Functional Fossil Graphene.

As a new engineering dielectric, vegetable insulating oil is widely used in electrical equipment. Small polar molecules such as alcohol and acid will be produced during the oil-immersed electrical equipment operation, which seriously affects the safety of equipment. The polar molecule can be removed by using functional fossil graphene materials. However, the structural design and group modification of graphene materials lack a theoretical basis. Therefore, in this paper, molecular dynamics (MD) and quantum mechanics theory (Dmol3) were utilized to study the adsorption kinetics and mechanism of graphene (GE), porous graphene (PGE), porous hydroxy graphene (HPGE), and porous graphene modified by hydroxyl and carboxyl groups (COOH-HPGE) on polar small molecules in vegetable oil. The results show that graphene-based materials can effectively adsorb polar small molecules in vegetable oil, and that the modification of graphene materials with carboxyl and hydroxyl groups improves their adsorption ability for polar small molecules, which is attributed to the conversion of physical adsorption to chemical adsorption by the modification of oxygen-containing groups. This study provides a theoretical basis for the design and preparation of graphene materials with high adsorption properties.

Pentagonal CmXnY6-m-n (m = 2, 3; n = 1, 2; X, Y = B, N, Al, Si, P) Monolayers: Janus Ternaries Combine Omnidirectional Negative Poisson Ratios with Giant Piezoelectric Effects.

Two-dimensional (2D) materials composed of pentagon and Janus motifs usually exhibit unique mechanical and electronic properties. In this work, a class of ternary carbon-based 2D materials, CmXnY6-m-n (m = 2, 3; n = 1, 2; X, Y = B, N, Al, Si, P), are systematically studied by first-principles calculations. Six of 21 Janus penta-CmXnY6-m-n monolayers are dynamically and thermally stable. The Janus penta-C2B2Al2 and Janus penta-Si2C2N2 exhibit auxeticity. More strikingly, Janus penta-Si2C2N2 exhibits an omnidirectional negative Poisson ratio (NPR) with values ranging from -0.13 to -0.15; in other words, it is auxetic under stretch in any direction. The calculations of piezoelectricity reveal that the out-of-plane piezoelectric strain coefficient (d32) of Janus panta-C2B2Al2 is up to 0.63 pm/V and increases to 1 pm/V after a strain engineering. These omnidirectional NPR, giant piezoelectric coefficients endow the Janus pentagonal ternary carbon-based monolayers as potential candidates in the future nanoelectronics, especially in the electromechanical devices.

Excellent-Moisture-Resistance Fluorinated Polyimide Composite Film and Self-Powered Acoustic Sensing.

As a clean, sustainable energy source, sound can carry a wealth of information and play a huge role in the Internet of Things era. In recent years, triboelectric acoustic sensors have received increasing attention due to the advantages of self-power supply and high sensitivity. However, the triboelectric charge is susceptible to ambient humidity, which reduces the reliability of the sensor and limits the application scenarios significantly. In this paper, a highly moisture-resistant fluorinated polyimide composited with an amorphous fluoropolymer film was prepared. The charge injection performance, triboelectric performance, and moisture resistance of the composite film were investigated. In addition, we developed a self-powered, highly sensitive, and moisture-resistant porous-structure acoustic sensor based on contact electrification. The detection characteristics of the acoustic sensor are also obtained.

Conversion of Dielectric Surface Effect into Volume Effect for High Output Energy.

Improving the output energy and durability of triboelectric nanogenerators (TENGs) remains a considerable challenge for their practical applications. Owing to the interface effect of triboelectrification and electrostatic induction, thinner films with higher dielectric constants yield a higher output; however, they are not durable for practical applications. Herein, the dielectric surface effect is changed into a volume effect by adopting a millimeter-thick dielectric film with an inner porous network structure so that charges can hop in the surface state of the network. Charge migration inside the dielectric film is the key factor affecting the output of the triboelectric nanogenerator (TENG) with a thick film, based on which each working stage follows the energy-maximization principle in the voltage-charge plot. The maximum peak and average power densities of the TENG with polyurethane foam film in 1 mm thickness reach 40.9 and 20.7 W m-2  Hz-1 , respectively, under environmental conditions, and the output charge density is 5.14 times that of TENGs with a poly(tetrafluoroethylene) film of the same thickness. Superdurability is achieved in the rotary-mode TENG after 200 000 operation cycles. This study identifies the physical mechanism of the thick dielectric film used in TENGs and provides a new approach to promote the output and durability of TENGs.

A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.

Background: Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition. Methods: A fully automatic segmentation deep learning model was designed based on the attention mechanism and using U-Net as the framework. Subcutaneous fat, skeletal muscle, and visceral fat were manually segmented by two experts to serve as ground truth labels. The performance of the model was evaluated using Dice similarity coefficients (DSCs) and Hausdorff distance at 95th percentile (HD95). Results: The mean DSC for subcutaneous fat and skeletal muscle were high for both the enhanced CT test set (0.93±0.06 and 0.96±0.02, respectively) and the plain CT test set (0.90±0.09 and 0.95±0.01, respectively). Nevertheless, the model did not perform well in the segmentation performance of visceral fat, especially for the enhanced CT test set. The mean DSC for the enhanced CT test set was 0.87±0.11, while the mean DSC for the plain CT test set was 0.92±0.03. We discuss the reasons for this result. Conclusions: This work demonstrates a method for the automatic outlining of subcutaneous fat, skeletal muscle, and visceral fat areas at L3.

Deterministic Manipulation of Heat Flow via Three-Dimensional-Printed Thermal Meta-Materials for Multiple Protection of Critical Components.

Heat dissipation is necessary for the safer operation of high-power electronic devices and high-capacity batteries. Thermal meta-materials can efficiently manipulate heat flow by molding natural materials into specific structures. In this study, we construct a three-dimensional-printed meta-material structure with efficient and deterministic heat conduction through combining the 2D boron nitride (BN) with nano-diamond (DM) bridging. A research of thermal conductivity and dielectric properties exhibits that the nanosized diamond-bridged and oriented 2D boron nitride endows efficient heat transfer and maintains low dielectric loss with low filler loading. The composites loaded with 19 wt% BN platelets and 1 wt% DM have the highest thermal conductivity of 3.687 W/(m·K) in the heat flow orientation, while the thermal conductivity is only 0.632 W/(m·K) in the vertical heading of heat flow. The thermal conductive networks with thermal meta-materials based on the structural characteristics have been designed to secure critical device components from the heat source and dissipate heat flow in a definite way. The infrared images show that the temperature difference of monitoring points in different directions on the BN-oriented composite substrate is 9 °C, which realizes the protection of the heat source and key components. This study shows the latent capacity of 3D-printed structured materials for critical device component protection and heat administration applications in electronic devices and electric equipment.

TG-Net: Combining transformer and GAN for nasopharyngeal carcinoma tumor segmentation based on total-body uEXPLORER PET/CT scanner.

Nasopharyngeal carcinoma (NPC) is a malignant tumor, and the main treatment is radiotherapy. Accurate delineation of the target tumor is essential for radiotherapy of NPC. NPC tumors are small in size and vary widely in shape and structure, making it a time-consuming and laborious task for even experienced radiologists to manually outline tumors. However, the segmentation performance of current deep learning models is not satisfactory, mainly manifested by poor segmentation boundaries. To solve this problem, this paper proposes a segmentation method for nasopharyngeal carcinoma based on dynamic PET-CT image data, whose input data include CT, PET, and parametric images (Ki images). This method uses a generative adversarial network with a modified UNet integrated with a Transformer as the generator (TG-Net) to achieve automatic segmentation of NPC on combined CT-PET-Ki images. In the coding stage, TG-Net uses moving windows to replace traditional pooling operations to obtain patches of different sizes, which can reduce information loss in the coding process. Moreover, the introduction of Transformer can make the network learn more representative features and improve the discriminant ability of the model, especially for tumor boundaries. Finally, the results of fivefold cross validation with an average Dice similarity coefficient score of 0.9135 show that our method has good segmentation performance. Comparative experiments also show that our network structure is superior to the most advanced methods in the segmentation of NPC. In addition, this work is the first to use Ki images to assist tumor segmentation. We also demonstrated the usefulness of adding Ki images to aid in tumor segmentation.

Improved GAN: Using a transformer module generator approach for material decomposition.

Dual-energy computed tomography (CT) can be used for material decomposition, allowing for the precise quantitative mapping of body substances; this has a wide range of clinical applications, including disease diagnosis, treatment response evaluation and prognosis prediction. However, dual-energy CT has not yet become the mainstream technique in most clinical settings due to its limited accessibility. To fully take advantage of material quantification, researchers have attempted to use deep learning to generate material decomposition maps from conventional single-energy CT images, mainly by synthesizing another single-energy CT image from a conventional single-energy CT image to form a dual-energy CT image first and then generate material decomposition maps. This is not a straightforward process, and it potentially introduces many inaccuracies after multiple steps. In this work, we proposed a generative adversarial network (GAN) framework as the base and improved its generator; this approach combines convolutional neural networks (CNNs) and a transformer module to directly generate material decomposition maps from conventional single-energy CT images. Our model pays attention to both local and global information. Then, we compared our method with 6 competitive deep learning methods on water (calcium) and calcium (water) substrate density image datasets. The average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the water (calcium) substrate density images were 32.7207, 0.9685, 0.0323, and 0.0555, respectively. Furthermore, the average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the calcium (water) substrate density images were 30.2823, 0.9449, 0.0652, and 0.0715, respectively. Our model achieved better performance and stronger stability than competing approaches.

ISA-Net: Improved spatial attention network for PET-CT tumor segmentation.

BACKGROUND AND OBJECTIVE: Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. METHODS: In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. RESULTS: We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. CONCLUSIONS: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment.

Molecular Dynamics Simulation for the Effect of Fluorinated Graphene Oxide Layer Spacing on the Thermal and Mechanical Properties of Fluorinated Epoxy Resin.

Traditional epoxy resin (EP) materials have difficulty to meet the performance requirements in the increasingly complex operating environment of the electrical and electronic industry. Therefore, it is necessary to study the design and development of new epoxy composites. At present, fluorinated epoxy resin (F-EP) is widely used, but its thermal and mechanical properties cannot meet the demand. In this paper, fluorinated epoxy resin was modified by ordered filling of fluorinated graphene oxide (FGO). The effect of FGO interlayer spacing on the thermal and mechanical properties of the composite was studied by molecular dynamics (MD) simulation. It is found that FGO with ordered filling can significantly improve the thermal and mechanical properties of F-EP, and the modification effect is better than that of FGO with disordered filling. When the interlayer spacing of FGO is about 9 Å, the elastic modulus, glass transition temperature, thermal expansion coefficient, and thermal conductivity of FGO are improved with best effect. Furthermore, we calculated the micro parameters of different systems, and analyzed the influencing mechanism of ordered filling and FGO layer spacing on the properties of F-EP. It is considered that FGO can bind the F-EP molecules on both sides of the nanosheets, reducing the movement ability of the molecular segments of the materials, so as to achieve the enhancement effect. The results can provide new ideas for the development of high-performance epoxy nanocomposites.

Triboelectric Energy Harvesting of the Superhydrophobic Coating from Dropping Water.

In this paper, the superhydrophobic coating was prepared by spraying the composites of fluorocarbon emulsion and nanosized silica on the conductive glass sheet for the triboelectric energy harvesting from water droplets. The low surface energy of fluorine in the fluorocarbon emulsion and nanosilica renders the coating with the static contact angle and sliding angle of 156.2° and 6.74°, respectively. The conductive aluminum tape was attached on the surface of the superhydrophobic coating to complete the circuit constituted with the aluminum electrode, charged superhydrophobic coating, and the conductive glass sheet. During the contact electrification with the bouncing water droplet, the superhydrophobic coating with the aluminum electrode can obtain the electric energy with an open-circuit voltage of 20 V and short-circuit current of 4.5 µA, respectively. While the control device only produced an open-circuit voltage of 0.2 V. The generated power by one drop was enough to light up 16 commercial LEDs. Results demonstrate that the fluorocarbon/silica composite superhydrophobic coating is potentially a strong candidate for scavenging energy in sliding mode from raindrops.