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Orientation-controlled growth of two-dimensional (2D) transition metal dichalcogenides (TMDCs) may enable many new electronic and optical applications. However, previous studies reporting aligned growth of WSe2 usually yielded very small domain sizes. Herein, we introduced gold vapor into the chemical vapor deposition (CVD) process as a catalyst to assist the growth of WSe2 and successfully achieved highly aligned monolayer WSe2 triangular flakes grown on c-plane sapphire with large domain sizes (130 µm) and fast growth rate (4.3 µm·s−1). When the aligned WSe2 domains merged together, a continuous monolayer WSe2 was formed with good uniformity. After transferring to Si/SiO2 substrates, field effect transistors were fabricated on the continuous monolayer WSe2, and an average mobility of 12 cm2·V−1·s−1 was achieved, demonstrating the good quality of the material. This report paves the way to study the effect of catalytic metal vapor in the CVD process of TMDCs and contributes a novel approach to realize the growth of aligned TMDC flakes.

Red phosphorus offers a high theoretical sodium capacity and has been considered as a candidate anode for sodium-ion batteries. Similar to silicon anodes for lithium-ion batteries, the electrochemical performance of red phosphorus is plagued by the large volume variation upon sodiation. Here we perform in situ transmission electron microscopy analysis of the synthesized red-phosphorus-impregnated carbon nanofibers with the corresponding chemo-mechanical simulation, revealing that, the sodiated red phosphorus becomes softened with a “liquid-like” mechanical behaviour and gains superior malleability and deformability against pulverization. The encapsulation strategy of the synthesized red-phosphorus-impregnated carbon nanofibers has been proven to be an effective method to minimize the side reactions of red phosphorus in sodium-ion batteries, demonstrating stable electrochemical cycling. Our study provides a valid guide towards high-performance red-phosphorus-based anodes for sodium-ion batteries., Red phosphorus is a promising anode for Na-ion batteries but suffers from large volume change upon cycling. Here the authors show a red-phosphorus-impregnated carbon nanofiber design in which the sodiated red phosphorus is featured by a “liquid-like” behavior and ultra-stable electrochemical performance is realized.

Nonvolatile, flexible artificial synapses that can be used for brain-inspired computing are highly desirable for emerging applications such as human-machine interfaces, soft robotics, medical implants, and biological studies. Printed devices based on organic materials are very promising for these applications due to their sensitivity to ion injection, intrinsic printability, biocompatibility, and great potential for flexible/stretchable electronics. Herein, we report the experimental realization of a nonvolatile artificial synapse using organic polymers in a scalable fabrication process. The three-terminal electrochemical neuromorphic device successfully emulates the key features of biological synapses: long-term potentiation/depression, spike timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow energy consumption. The artificial synapse network exhibits an excellent endurance against bending tests and enables a direct emulation of logic gates, which shows the feasibility of using them in futuristic hierarchical neural networks. Based on our demonstration of 100 distinct, nonvolatile conductance states, we achieved a high accuracy in pattern recognition and face classification neural network simulations.

Severe lithium (Li) dendrite growth during the plating/stripping process, which results in low Coulombic efficiency and safety issues, strongly hinders the practical applications of Li metal anodes. Herein, we propose a novel design of three-dimensional (3D) interconnected graphene (IG) framework synthesized with the help of nickel (Ni) microspheres for stable Li metal anodes. The as-prepared IG framework consists of multiple stacks of two-dimensional (2D) graphene layers and plenty of hollow graphene microspheres in between, and thus provides protective layers on the top to suppress lithium dendrites, sufficient surface area to reduce the effective current density, as well as ion channels for fast Li transport, which is confirmed by post-cycle morphology characterization. When Li foil was used as the counter electrode for Li deposition, the assembled coin cell maintained an average Coulombic efficiency of more than 97.5% for 100 cycles at current density of 1 mA cm−2 with a Li loading of 1 mAh cm−2. Furthermore, we achieved stable cycling for more than 300 h at a current density of 1 mA cm−2 with a Li loading of 2 mAh cm−2 when assembled as symmetric cells. This strategy of vertically stacking 2D materials provides a novel approach towards dendrite-free Li metal anodes for the next-generation energy storage systems.

Summary Flexible sensors are essential for advancing implantable and wearable bioelectronics toward monitoring chemical signals within and on the body. Developing biosensors for monitoring multiple neurotransmitters in real time represents a key in vivo application that will increase understanding of information encoded in brain neurochemical fluxes. Here, arrays of devices having multiple In2O3 nanoribbon field-effect transistors (FETs) were fabricated on 1.4-μm-thick polyethylene terephthalate (PET) substrates using shadow mask patterning techniques. Thin PET-FET devices withstood crumpling and bending such that stable transistor performance with high mobility was maintained over >100 bending cycles. Real-time detection of the small-molecule neurotransmitters serotonin and dopamine was achieved by immobilizing recently identified high-affinity nucleic-acid aptamers on individual In2O3 nanoribbon devices. Limits of detection were 10 fM for serotonin and dopamine with detection ranges spanning eight orders of magnitude. Simultaneous sensing of temperature, pH, serotonin, and dopamine enabled integration of physiological and neurochemical data from individual bioelectronic devices., Graphical Abstract, Highlights • We fabricated flexible In2O3 nanoribbon transistors using cleanroom-free processes • Flexible In2O3 transistors withstood crumpling and bending with stable performance • Flexible aptamer biosensors detect neurotransmitters in real time • Multiplexed sensors monitor temperature, pH, serotonin, and dopamine simultaneously, Nanoscience; Bioelectronics; Nanotechnology Fabrication

International audience; Active biodegradable packaging films were developed with polyvinyl alcohol (PVA), chitosan (CS) and D-Limonene (DL). The effect of various DL content levels (0%, 2.5%, 5%, 7.5%, and 10% w/w) on the structural, mechanical, biodegradable and antimicrobial properties of PVA/CS films was systematically studied. Good compatibility between DL and PVA/CS, and good dispersion of DL in the PVA/CS matrix were demonstrated by Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). PVA/CS films greatly improved their antibacterial, mechanical and barrier properties, which are associated with the considerable biodegradability, after they were incorporated with DL. However, the overloading with DL may weaken hydrogen bonds between polymer chains, resulting in the negative effects on the physical performances of the film. Particularly, the PVA/CS/DL-5% film showed the highest water contact angle and transmittance value, and also showed effective preservations of packaged mango fruits during 10 day of storage at 20 +/- 2 degrees C, based on the characterization by fruits weight loss, decay rate, firmness, titratable acidity, soluble solids, and ascorbic acid. Consequently, DL/PVA/CS composite films may be a promising eco-friendly packaging material for food preservation. (C) 2018 Published by Elsevier B.V.

Quasi-two-dimensional (2D) β-Ga2O3 is a rediscovered metal-oxide semiconductor with an ultra-wide bandgap of 4.6–4.9 eV. It has been reported to be a promising material for next-generation power and radio frequency electronics. Field effect transistors (FETs) that can switch at high voltage are key components in power and radio frequency devices, and reliable Ohmic contacts are essential for high FET performance. However, obtaining low contact resistance on β-Ga2O3 FETs is difficult since reactions between β-Ga2O3 and metal contacts are not fully understood. Herein, we experimentally demonstrate the importance of reactions at the metal/β-Ga2O3 interface and the corresponding effects of these reactions on FET performance. When Ti is employed as the metal contact, annealing of β-Ga2O3 FETs in argon can effectively transform Schottky contacts into Ohmic contacts and permit a large drain current density of ~ 3.1 mA/μm. The contact resistance (Rcontact) between the Ti electrodes and β-Ga2O3 decreased from ~ 430 to ~ 0.387 Ω·mm after annealing. X-ray photoelectron spectroscopy (XPS) confirmed the formation of oxygen vacancies at the Ti/β-Ga2O3 interface after annealing, which is believed to cause the improved FET performance. The results of this study pave the way for greater application of β-Ga2O3 in electronics.

International audience; To establish a “green” biorefinery extraction of apple pomace pectin, a sequential pretreatment with three natural deep eutectic solvents (NADES, choline chloride (CC): glycerol (G); CC: lactic acid (LA); potassium carbonate (K): G) was used prior to hot water extraction. A synergistic effect of CC:G and CC:LA pretreatments was observed and led to the highest recovery of pectin. The sequential NADES/water extraction process also provided a mean to tailor pectin main structure. It was explained as resulting from ion exchange and individual NADES components effects. The 13C solid state NMR T1ρH and THH parameters indicated a reorganization of cellulose in the residues following extraction of pectin, notably after alkaline K:G pretreatment/water extraction. Hence, sequential NADES pretreatments/water extraction represents a “green” alternative to mild mineral acid to extract pectin and to tailor its main structures, while the residual pomace can be further sources of valuable compounds and polymers.

With the development of information technology, the demand for information security is increasing. For more convenient and safer needs, the encryption technology based on biometrics has developed rapidly. Among them, iris technology has become an important research object of information security research due to the stability of iris characteristics and its difficulty in forgery. In this paper, the iris feature encryption technology based on the iris is studied by using the method of deep learning as the feature classification method and the iris feature as the research object. The simulation experiment is carried out by using the common iris database. The results show that the method can greatly improve the consistency of iris encryption and improve the security of encryption and decryption process.

Credit scoring process, the most important part in credit risk management, aims at estimating the probability that an applicant will perform bad credit behaviors (e.g., loan default). Managing and developing effective and reliable risk assessment procedures in order to mitigate potential loss caused by new applicants heavily relies on the performance of scoring process. Traditionally this process is manually developed, which is time-consuming. In this paper, we propose an automated credit risk management process based on machine learning to ease the scoring process in order to reduce the human effort. This process is data driven: it leverages machine learning to automatically analyze vast amounts of historical data and build predictive model. We evaluate our process with a real-world proprietary dataset and achieved good performance, which shows the feasibility of using machine learning to facilitate the credit risk management process.

Software requirement analysis is an essential step in software development process, which defines what is to be built in a project. Requirements are mostly written in text and will later evolve to fine-grained and actionable artifacts with details about system configurations, technology stacks, etc. Tracing the evolution of requirements enables stakeholders to determine the origin of each requirement and understand how well the software's design reflects to its requirements. Reckoning requirements traceability is not a trivial task, we focus on applying machine learning approach to classify traceability between various associated requirements. In particular, we investigate a 2-learner, ontology-based approach, where we train two classifiers to separately exploit two types of features, lexical features and features derived from a hand-built ontology. In comparison to a supervised baseline system that uses only lexical features, our approach yields a relative error reduction of 25.9%. Most interestingly, results do not deteriorate when the hand-built ontology is replaced with its automatically constructed counterpart.

With the advent of the Web era, data has exploded, with tens of thousands of textual data being generated every day. Traditional text sentiment analysis methods are mainly based on lexicon and machine learning-based methods. These methods show certain limitations as the data size increases. Here, we propose a new knowledge map design method based on convolutional neural network. For the academic literature data crawled from HowNet and Baidu Academic Website with the theme of computer, the corresponding ontology database, the fusion application of multiple data sources, and the mapping of different ontology libraries through data fusion are constructed for data sources in different fields. The global ontology library then uses the entity alignment and entity link methods for knowledge acquisition and fusion. Finally, the convolutional neural network is used for training and testing. The experimental results show that the subject search task can not only obtain the book through the convolution network, the effective academic literature in the question bank can also be used to obtain the relevance of the keyword in the search results, which verifies the effectiveness of the method.