Recent advances in batch production of transfer-free graphene.

Large-area transfer-free graphene films prepared via chemical vapor deposition have proved appealing for various applications, with exciting examples in electronics, photonics, and optoelectronics. To achieve their commercialisation, batch production is a prerequisite. Nevertheless, the prevailing scalable synthesis strategies that have been reported are still obstructed by production inefficiencies and non-uniformity. There has also been a lack of reviews in this realm. We present herein a comprehensive and timely summary of recent advances in the batch production of transfer-free graphene. Primary issues and promising approaches for improving the graphene growth rate are first addressed, followed by a discussion of the strategies to guarantee in-plane and batch uniformity for graphene grown on planar plates and wafer-scale substrates, with the design of the target equipment to meet productivity requirements. Finally, potential research directions are outlined, aiming to offer insights into guiding the scalable production of transfer-free graphene.

Compact terahertz harmonic generation in the Reststrahlenband using a graphene-embedded metallic split ring resonator array.

Harmonic generation is a result of a strong non-linear interaction between light and matter. It is a key technology for optics, as it allows the conversion of optical signals to higher frequencies. Owing to its intrinsically large and electrically tunable non-linear optical response, graphene has been used for high harmonic generation but, until now, only at frequencies < 2 THz, and with high-power ultrafast table-top lasers or accelerator-based structures. Here, we demonstrate third harmonic generation at 9.63 THz by optically pumping single-layer graphene, coupled to a circular split ring resonator (CSRR) array, with a 3.21 THz frequency quantum cascade laser (QCL). Combined with the high graphene nonlinearity, the mode confinement provided by the optically-pumped CSRR enhances the pump power density as well as that at the third harmonic, permitting harmonic generation. This approach enables potential access to a frequency range (6-12 THz) where compact sources remain difficult to obtain, owing to the Reststrahlenband of typical III-V semiconductors.

A robust approach for multi-type classification of brain tumor using deep feature fusion.

Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients' lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting. Firstly, the extracted features of three pre-trained models including ResNet101, DenseNet121, and EfficientNetB0 are adjusted to ensure that the shape of extracted features for the three models is the same. Secondly, the three models are fine-tuned to extract features from brain tumor images. Thirdly, pairwise summation of the extracted features is carried out to achieve feature fusion. Finally, classification of brain tumors based on fused features is performed. The public datasets including Figshare (Dataset 1) and Kaggle (Dataset 2) are used to verify the reliability of the proposed method. Experimental results demonstrate that the fusion method of ResNet101 and DenseNet121 features achieves the best performance, which achieves classification accuracy of 99.18 and 97.24% in Figshare dataset and Kaggle dataset, respectively.

Bifidobacterium improves oestrogen-deficiency-induced osteoporosis in mice by modulating intestinal immunity.

Osteoporosis has become one of the major diseases that threaten the health of middle-aged and elderly people, and with the growth of an ageing population, more and more people are affected by osteoporosis these days. In recent years, intestinal flora has been found to affect the host immune system, and an overactive immune system is closely related to bone resorption. Probiotics can effectively improve bone density and strength, reduce bone loss, and improve osteoporosis, but their mechanism of action and relationship with intestinal microbiota are still unclear. In this study, two strains of Bifidobacterium (Bifidobacterium bifidum FL228.1 and Bifidobacterium animalis subsp. Lactis F1-7) that can alleviate intestinal inflammation were screened based on previous experiments. Through the construction of an ovariectomized mouse model, the improvement of osteoporosis by Bifidobacterium was detected, and the influence of Bifidobacterium on intestinal immunity was explored. The results show that Bifidobacterium treatment significantly improved bone mineral density (BMD), bone volume/total volume ratio (BV/TV), and trabecular number (Tb·N), and effectively suppressed bone loss. Furthermore, Bifidobacterium treatment could inhibit the expression of inflammatory cytokines in the gut, alleviate gut inflammation, and thus suppress excessive osteoclast generation. Its mechanism of action includes factors that protect the mucosal barrier, including occludin, ZO-1, claudin-2, and MUC2, and the reduction of pro-inflammatory M1 macrophages. B. bifidum FL228.1 increased the abundance of beneficial bacteria in the colon, including Lactobacillus and Colidextribacter. B. animalis F1-7 increased the abundance of Bifidobacterium and decreased the abundance of Desulfovibrio and Ruminococcus in the colon. These research findings expand our understanding of the gut-bone axis and provide new guidance for the development of probiotic-based therapies for osteoporosis in the future.

Controlled Growth of Single-Crystal Graphene Wafers on Twin-Boundary-Free Cu(111) Substrates.

Single-crystal graphene (SCG) wafers are needed to enable mass-electronics and optoelectronics owing to their excellent properties and compatibility with silicon-based technology. Controlled synthesis of high-quality SCG wafers can be done exploiting single-crystal Cu(111) substrates as epitaxial growth substrates recently. However, current Cu(111) films prepared by magnetron sputtering on single-crystal sapphire wafers still suffer from in-plane twin boundaries, which degrade the SCG chemical vapor deposition. Here, it is shown how to eliminate twin boundaries on Cu and achieve 4 in. Cu(111) wafers with ≈95% crystallinity. The introduction of a temperature gradient on Cu films with designed texture during annealing drives abnormal grain growth across the whole Cu wafer. In-plane twin boundaries are eliminated via migration of out-of-plane grain boundaries. SCG wafers grown on the resulting single-crystal Cu(111) substrates exhibit improved crystallinity with >97% aligned graphene domains. As-synthesized SCG wafers exhibit an average carrier mobility up to 7284 cm2 V-1 s-1 at room temperature from 103 devices and a uniform sheet resistance with only 5% deviation in 4 in. region.

EFF_D_SVM: a robust multi-type brain tumor classification system.

Brain tumors are one of the most threatening diseases to human health. Accurate identification of the type of brain tumor is essential for patients and doctors. An automated brain tumor diagnosis system based on Magnetic Resonance Imaging (MRI) can help doctors to identify the type of tumor and reduce their workload, so it is vital to improve the performance of such systems. Due to the challenge of collecting sufficient data on brain tumors, utilizing pre-trained Convolutional Neural Network (CNN) models for brain tumors classification is a feasible approach. The study proposes a novel brain tumor classification system, called EFF_D_SVM, which is developed on the basic of pre-trained EfficientNetB0 model. Firstly, a new feature extraction module EFF_D was proposed, in which the classification layer of EfficientNetB0 was replaced with two dropout layers and two dense layers. Secondly, the EFF_D model was fine-tuned using Softmax, and then features of brain tumor images were extracted using the fine-tuned EFF_D. Finally, the features were classified using Support Vector Machine (SVM). In order to verify the effectiveness of the proposed brain tumor classification system, a series of comparative experiments were carried out. Moreover, to understand the extracted features of the brain tumor images, Grad-CAM technology was used to visualize the proposed model. Furthermore, cross-validation was conducted to verify the robustness of the proposed model. The evaluation metrics including accuracy, F1-score, recall, and precision were used to evaluate proposed system performance. The experimental results indicate that the proposed model is superior to other state-of-the-art models.

Ampere-hour-scale soft-package potassium-ion hybrid capacitors enabling 6-minute fast-charging.

Extreme fast charging of Ampere-hour (Ah)-scale electrochemical energy storage devices targeting charging times of less than 10 minutes are desired to increase widespread adoption. However, this metric is difficult to achieve in conventional Li-ion batteries due to their inherent reaction mechanism and safety hazards at high current densities. In this work, we report 1 Ah soft-package potassium-ion hybrid supercapacitors (PIHCs), which combine the merits of high-energy density of battery-type negative electrodes and high-power density of capacitor-type positive electrodes. The PIHC consists of a defect-rich, high specific surface area N-doped carbon nanotube-based positive electrode, MnO quantum dots inlaid spacing-expanded carbon nanotube-based negative electrode, carbonate-based non-aqueous electrolyte, and a binder- and current collector-free cell design. Through the optimization of the cell configuration, electrodes, and electrolyte, the full cells (1 Ah) exhibit a cell voltage up to 4.8 V, high full-cell level specific energy of 140 Wh kg-1 (based on the whole mass of device) with a full charge of 6 minutes. An 88% capacity retention after 200 cycles at 10 C (10 A) and a voltage retention of 99% at 25 ± 1 °C are also demonstrated.

Electrically Tunable Nonlinearity at 3.2 Terahertz in Single-Layer Graphene.

Graphene is a nonlinear material in the terahertz (THz) frequency range, with χ(3) ∼ 10-9 m2/V2 ∼ 15 orders of magnitude higher than that of other materials used in the THz range, such as GaAs or lithium niobate. This nonlinear behavior, combined with ultrafast dynamic for excited carriers, proved to be essential for third harmonic generation in the sub-THz and low (<2.5 THz) THz range, using moderate (60 kV/cm) fields and at room temperature. Here, we show that, for monochromatic high peak power (1.8 W) input THz signals, emitted by a quantum cascade laser, the nonlinearity can be controlled using an ionic liquid gate that tunes the graphene Fermi energy up to >1.2 eV. Pump and probe experiments reveal an intense absorption nonlinearity at 3.2 THz, with a dominant 3rd-order contribution at EF > 0.7 eV, hence opening intriguing perspectives per engineering novel architectures for light generation at frequencies > 9 THz.

Fast synthesis of large-area bilayer graphene film on Cu.

Bilayer graphene (BLG) is intriguing for its unique properties and potential applications in electronics, photonics, and mechanics. However, the chemical vapor deposition synthesis of large-area high-quality bilayer graphene on Cu is suffering from a low growth rate and limited bilayer coverage. Herein, we demonstrate the fast synthesis of meter-sized bilayer graphene film on commercial polycrystalline Cu foils by introducing trace CO2 during high-temperature growth. Continuous bilayer graphene with a high ratio of AB-stacking structure can be obtained within 20 min, which exhibits enhanced mechanical strength, uniform transmittance, and low sheet resistance in large area. Moreover, 96 and 100% AB-stacking structures were achieved in bilayer graphene grown on single-crystal Cu(111) foil and ultraflat single-crystal Cu(111)/sapphire substrates, respectively. The AB-stacking bilayer graphene exhibits tunable bandgap and performs well in photodetection. This work provides important insights into the growth mechanism and the mass production of large-area high-quality BLG on Cu.

Spontaneous Internal Electric Field in Heterojunction Boosts Bifunctional Oxygen Electrocatalysts for Zinc-Air Batteries: Theory, Experiment, and Application.

Heterojunctions are a promising class of materials for high-efficiency bifunctional oxygen electrocatalysts in both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). However, the conventional theories fail to explain why many catalysts behave differently in ORR and OER, despite a reversible path (* O2 ⇋* OOH⇋* O⇋* OH). This study proposes the electron-/hole-rich catalytic center theory (e/h-CCT) to supplement the existing theories, it suggests that the Fermi level of catalysts determines the direction of electron transfer, which affects the direction of the oxidation/reduction reaction, and the density of states (DOS) near the Fermi level determines the accessibility for injecting electrons and holes. Additionally, heterojunctions with different Fermi levels form electron-/hole-rich catalytic centers near the Fermi levels to promote ORR/OER, respectively. To verify the universality of the e/h-CCT theory, this study reveals the randomly synthesized heterostructural Fe3 N-FeN0.0324 (Fex N@PC with DFT calculations and electrochemical tests. The results show that the heterostructural F3 N-FeN0.0324 facilitates the catalytic activities for ORR and OER simultaneously by forming an internal electron-/hole-rich interface. The rechargeable ZABs with Fex N@PC cathode display a high open circuit potential of 1.504 V, high power density of 223.67 mW cm-2 , high specific capacity of 766.20 mAh g-1 at 5 mA cm-2 , and excellent stability for over 300 h.

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy.

BACKGROUND: Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results. METHODS: In order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals. RESULTS: The empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%. CONCLUSION: The proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.

Oxidization-Temperature-Triggered Rapid Preparation of Large-Area Single-Crystal Cu(111) Foil.

The controlled preparation of single-crystal Cu(111) is intensively investigated owing to the superior properties of Cu(111) and its advantages in synthesizing high-quality 2D materials, especially graphene. However, the accessibility of large-area single-crystal Cu(111) is still hindered by time-consuming, complicated, and high-cost preparation methods. Here, the oxidization-temperature-triggered rapid preparation of large-area single-crystal Cu(111) in which an area up to 320 cm2 is prepared within 60 min, and where low-temperature oxidization of polycrystalline Cu foil surface plays a vital role, is reported. A mechanism is proposed, by which the thin Cux O layer transforms to a Cu(111) seed layer on the surface of Cu to induce the formation of a large-area Cu(111) foil, which is supported by both experimental data and molecular dynamics simulation results. In addition, a large-size high-quality graphene film is synthesized on the single-crystal Cu(111) foil surface and the graphene/Cu(111) composites exhibit enhanced thermal conductivity and ductility compared to their polycrystalline counterpart. This work, therefore, not only provides a new avenue toward the monocrystallinity of Cu with specific planes but also contributes to improving the mass production of high-quality 2D materials.

Uniform thin ice on ultraflat graphene for high-resolution cryo-EM.

Cryo-electron microscopy (cryo-EM) visualizes the atomic structure of macromolecules that are embedded in vitrified thin ice at their close-to-native state. However, the homogeneity of ice thickness, a key factor to ensure high image quality, is poorly controlled during specimen preparation and has become one of the main challenges for high-resolution cryo-EM. Here we found that the uniformity of thin ice relies on the surface flatness of the supporting film, and developed a method to use ultraflat graphene (UFG) as the support for cryo-EM specimen preparation to achieve better control of vitreous ice thickness. We show that the uniform thin ice on UFG improves the image quality of vitrified specimens. Using such a method we successfully determined the three-dimensional structures of hemoglobin (64 kDa), α-fetoprotein (67 kDa) with no symmetry, and streptavidin (52 kDa) at a resolution of 3.5 Å, 2.6 Å and 2.2 Å, respectively. Furthermore, our results demonstrate the potential of UFG for the fields of cryo-electron tomography and structure-based drug discovery.

Isolation and characterisation of Pulsatilla Radix-utilising bacteria Pediococcus pentosaceus PR-1 from human faeces.

Although probiotics have been isolated from different sources, few were isolated from traditional Chinese medicine. The current study firstly isolates Pulsatilla Radix-utilising Pediococcus pentosaceus PR-1 from human faeces. Subsequently, the tolerance of PR-1 to low pH, bile salts, simulated gastric juice and succus entericus, antioxidant activity, antimicrobial activity, cholesterol assimilation and antibiotics susceptibility were investigated. After 2 h of incubation at pH 2.0, over 80% of PR-1 survived. The cell viability of PR-1 at 2 h under 0.1% bile salt condition was 99.2%. The survival rate of PR-1 in gastric juice and succus entericus was 64.48% and 81.86%, respectively. Cell-free supernatant of PR-1 culture also showed antimicrobial activity against Escherichia coli, Staphylococcus aureus and Salmonella typhimurium. Besides, antioxidant activity of PR-1 CFS was significantly greater than cell pellet. PR-1 was shown to be resistant to kanamycin, streptomycin, vancomycin and norfloxacin and was able to lower the cholesterol level to 72.5±1.5%. In addition, PR-1 displayed γ-haemolysis and was non-pathogenic.

A New Method on Construction of Brain Effective Connectivity Based on Functional Magnetic Resonance Imaging.

Most of the existing methods about the causal relationship based on functional magnetic resonance imaging (fMRI) data are either the hypothesis-driven methods or based on a linear model, which can result in the deviation for detecting the original brain activity. Therefore, it is necessary to develop a new method for detecting the effective connectivity (EC) of the brain activity by the nonlinear calculation. In this study, we firstly proposed a new technology evaluating effective connectivity of the human brain based on back-propagation neural network with nonlinear model, named EC-BP. Next, we simulated four time series for assessing the feasibility and accuracy of EC-BP compared to Granger causality analysis (GCA). Finally, the proposed EC-BP was applied to the brain fMRI from 60 healthy subjects. The results from the four simulated time series showed that the proposed EC-BP can detect the originally causal relationship, consistent with the actual causality. However, the GCA can not find nonlinear causality. Based on the analysis of the fMRI data from the healthy participants, EC-BP and GCA showed the huge differences in the top 50 connections in descending order of EC. EC-BP showed all ECs related to hippocampus and parahippocampus, whereas GCA showed most ECs related to the paracentral lobule, caudate, putamen, and pallidum, which represents the brain regions with most frequent information passing measured by different methods. The proposed EC-BP method can provide supplementary information to GCA, which will promote more comprehensive detection and evaluation of brain EC.

Intrinsic Wettability in Pristine Graphene.

The wettability of graphene remains controversial owing to its high sensitivity to the surroundings, which is reflected by the wide range of reported water contact angle (WCA). Specifically, the surface contamination and underlying substrate would strongly alter the intrinsic wettability of graphene. Here, the intrinsic wettability of graphene is investigated by measuring WCA on suspended, superclean graphene membrane using environmental scanning electron microscope. An extremely low WCA with an average value ≈30° is observed, confirming the hydrophilic nature of pristine graphene. This high hydrophilicity originates from the charge transfer between graphene and water molecules through H-π interaction. The work provides a deep understanding of the water-graphene interaction and opens up a new way for measuring the surface properties of 2D materials.

Visitor's Willingness to Pay for National Park Entrance Fees in China: Evidence from a Contingent Valuation Method.

China has been making efforts in nature conservation by developing a new national park system. Setting a fee-based entrance policy for the newly established national parks can be challenging without information on visitors' willingness to pay (WTP). Thus, this study aims to evaluate visitors' WTP entrance fees with a dataset of 1215 visitors collected in China's planned Qinling National Park (QNP). Using the double bounded dichotomous choice format of the contingent valuation method, we obtained the mean WTP for the entrance fee of QNP of 200 yuan. Visitors' demand becomes relatively inelastic at the recommended entrance fee of 160 yuan when the expected ticket revenue will reach its maximum of 13.8 billion yuan. Our study also indicates that visitors' WTP is significantly influenced by their gender difference, education level, income, number of trips to natural attractions, duration of stay, the intention of a future visit, and concern about commercial exploitation. Our empirical study provides insights into developing effective pricing policies and appropriate marketing strategies for China's new national park system.

Hydrophilic, Clean Graphene for Cell Culture and Cryo-EM Imaging.

The wettability of graphene is critical for numerous applications but is very sensitive to its surface cleanness. Herein, by clarifying the impact of intrinsic contamination, i.e., amorphous carbon, which is formed on the graphene surface during the high-temperature chemical vapor deposition (CVD) process, the hydrophilic nature of clean graphene grown on single-crystal Cu(111) substrate was confirmed by both experimental and theoretical studies, with an average water contact angle of ∼23°. Furthermore, the wettability of as-transferred graphene was proven to be highly dependent on its intrinsic cleanness, because of which the hydrophilic, clean graphene exhibited improved performance when utilized for cell culture and cryoelectron microscopy imaging. This work not only validates the intrinsic hydrophilic nature of graphene but also provides a new insight in developing advanced bioapplications using CVD-grown clean graphene films.

Chip-Scalable, Room-Temperature, Zero-Bias, Graphene-Based Terahertz Detectors with Nanosecond Response Time.

The scalable synthesis and transfer of large-area graphene underpins the development of nanoscale photonic devices ideal for new applications in a variety of fields, ranging from biotechnology, to wearable sensors for healthcare and motion detection, to quantum transport, communications, and metrology. We report room-temperature zero-bias thermoelectric photodetectors, based on single- and polycrystal graphene grown by chemical vapor deposition (CVD), tunable over the whole terahertz range (0.1-10 THz) by selecting the resonance of an on-chip patterned nanoantenna. Efficient light detection with noise equivalent powers <1 nWHz-1/2 and response time ∼5 ns at room temperature are demonstrated. This combination of specifications is orders of magnitude better than any previous CVD graphene photoreceiver operating in the sub-THz and THz range. These state-of-the-art performances and the possibility of upscaling to multipixel architectures on complementary metal-oxide-semiconductor platforms are the starting points for the realization of cost-effective THz cameras in a frequency range still not covered by commercially available microbolometer arrays.