Towards a Dual-Tip STM Application in Mesoscopic Electron Transport

A new design for a dual-tip scanning tunneling microscope (STM) is presented. The design is a variation on the mechanically controllable break-junction with two electron beam-induced deposition nano-tips. The new design enables one to scan surfaces simultaneously with two probes having a nano-gap separation. By collecting the lateral current flowing between the tips, the transconductance map can then be compared with the STM images for local characterizations of the electron transport. Since the lateral beam carries the property of the density of states of the surface at momentum space, the dispersion of the electronic structure should give an orientation and position dependence of the local transconductance current. In addition, the reduced terminal separation, on the order of the characteristic mesoscopic length scales, is likely to be sensitive to a variety of typically observed interactions and interference effects.     

Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.     

Social reader: towards browsing the social web

We describe Social Reader, a feed-reader-plus-social-network aggregator that mines comments from social media in order to display a user’s relational neighborhood as a navigable social network. Social Reader’s network visualization enhances mutual awareness of blogger communities, facilitates their exploration and growth with a fully dragn- drop interface, and provides novel ways to filter and summarize people, groups, blogs and comments. We discuss the architecture behind the reader, highlight tasks it adds to the workflow of a typical reader, and assess their cost. We also explore the potential of mood-based features in social media applications. Mood is particularly relevant to social media, reflecting the personal nature of the medium. We explore two prototype mood-based features: colour coding the mood of recent posts according to a valence/arousal map, and a mood-based abstract of recent activity using image media. A six week study of the software involving 20 users confirmed the usefulness of the novel visual display, via a quantitative analysis of use logs, and an exit survey.     

Micro cam system driven by electrostatic comb-drive actuators based on SOI-MEMS technology

Microsystem Technologies - This paper reports a design and fabrication process of a micro cam system (MCS) with a flat-faced translating follower. The cam rim with cover diameter of 2.4 mm...     

Adsorption of four perfluorinated acids on non ion exchange polymer sorbents

Perfluorinated compounds (PFCs) have attracted global concern due to their ubiquitous distribution and properties of persistence, bio accumulation and toxicity. The process of adsorption has been identified as an effective technique to remove PFCs in water. Different non ion-exchange polymeric adsorbents were tested with regard to their sorption kinetics and isotherms at low PFCs concentrations. Selected PFCs were perfluorobutanoic acid (PFBA), perfluoroheptanoic acid (PFHpA), perfluorooctanoic acid (PFOA) and perfluorodecanoic acid (PFDA) and the tested polymers were three types of Dowex optopores (V-493, V503, and L493), Amberlite XAD-4, and Filtrasorb 400 (Granular Activated Carbon-GAC). We observed the selective adsorption of PFCs on synthetic polymers. For PFDA, Amberlite XAD-4 gave the Freundlich adsorption constant of 2,965 (microg PFCs/g sorbent)(microg PFCs/L)(-n), which was higher than that of GAC (121.89 (microg PFCs/g sorbent) (microg PFCS/L)(-n)). In the case of PFBA, GAC showed better performance (13.36) (microg PFCs/g sorbent) microg PFCS/L)(-n) than synthetic polymers (0.62-5.23) (microg PFCs/g sorbent) (microg PFCS/L)(-n). Adsorption kinetics of all adsorbents were well described (R2 = 0.85-1) by pseudo-second order kinetic model. Sorption capacity was influenced by initial PFCs concentration for all adsorbents. GAC reached the equilibrium concentration within 4 hours, Amberlite XAD 4 reached it within 10 hours and other polymers took more than 70 hours.     

Unsupervised context detection using wireless signals

The sensing context plays an important role in many pervasive and mobile computing applications. Continuing from previous work [D. Phung, B. Adams, S. Venkatesh, Computable social patterns from sparse sensor data, in: Proceedings of First International Workshop on Location Web, World Wide Web Conference (WWW), New York, NY, USA, 2008, ACM 69–72.], we present an unsupervised framework for extracting user context in indoor environments with existing wireless infrastructures. Our novel approach casts context detection into an incremental, unsupervised clustering setting. Using WiFi observations consisting of access point identification and signal strengths freely available in office or public spaces, we adapt a density-based clustering technique to recover basic forms of user contexts that include user motion state and significant places the user visits from time to time. High-level user context, termed rhythms, comprising sequences of significant places are derived from the above low-level context by employing probabilistic clustering techniques, latent Dirichlet allocation and its n-gram temporal extension. These user contexts can enable a wide range of context-ware application services. Experimental results with real data in comparison with existing methods are presented to validate the proposed approach. Our motion classification algorithm operates in real-time, and achieves a 10% improvement over an existing method; significant locations are detected with over 90% accuracy and near perfect cluster purity. Richer indoor context and meaningful rhythms, such as typical daily routines or meeting patterns, are also inferred automatically from collected raw WiFi signals.     

Metal Single-Site Molecular Complex–MXene Heteroelectrocatalysts Interspersed Graphene Nanonetwork for Efficient Dual-Task of Water Splitting and Metal–Air Batteries

Development of multifunctional electrocatalysts with high efficiency and stability is of great interest in recent energy conversion technologies. Herein, a novel heteroelectrocatalyst of molecular iron complex (Fe_MC)-carbide MXene (Mo₂TiC₂T_x) uniformly embedded in a 3D graphene-based hierarchical network (GrH) is rationally designed. The coexistence of Fe_MC and MXene with their unique interactions triggers optimum electronic properties, rich multiple active sites, and favorite free adsorption energy for excellent trifunctional catalytic activities. Meanwhile, the highly porous GrH effectively promotes a multichannel architecture for charge transfer and gas/ion diffusion to improve stability. Therefore, the Fe_MC–MXene/GrH results in superb performances towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) in alkaline medium. The practical tests indicate that Zn/Al–air batteries derived from Fe_MC–MXene/GrH cathodic electrodes produce high power densities of 165.6 and 172.7 mW cm⁻², respectively. Impressively, the liquid-state Zn–air battery delivers excellent cycling stability of over 1100 h. In addition, the alkaline water electrolyzer induces a low cell voltage of 1.55 V at 10 mA cm⁻² and 1.86 V at 0.4 A cm⁻² in 30 wt.% KOH at 80 °C, surpassing recent reports. The achievements suggest an exciting multifunctional electrocatalyst for electrochemical energy applications.