MXene Composite and Coaxial Fibers with High Stretchability and Conductivity for Wearable Strain Sensing Textiles

The integration of nanomaterials with high conductivity into stretchable polymer fibers can achieve novel functionalities such as sensing physical deformations. With a metallic conductivity that exceeds other solution‐processed nanomaterials, 2D titanium carbide MXene is an attractive material to produce conducting and stretchable fibers. Here, a scalable wet‐spinning technique is used to produce Ti₃C₂T_x MXene/polyurethane (PU) composite fibers that show both conductivity and high stretchability. The conductivity at a very low percolation threshold of ≈1 wt% is demonstrated, which is lower than the previously reported values for MXene‐based polymer composites. When used as a strain sensor, the MXene/PU composite fibers show a high gauge factor of ≈12900 (≈238 at 50% strain) and a large sensing strain of ≈152%. The cyclic strain sensing performance is further improved by producing fibers with MXene/PU sheath and pure PU core using a coaxial wet‐spinning process. Using a commercial‐scale knitting machine, MXene/PU fibers are knitted into a one‐piece elbow sleeve, which can track various movements of the wearer's elbow. This study establishes fundamental insights into the behavior of MXene in elastomeric composites and presents strategies to achieve MXene‐based fibers and textiles with strain sensing properties suitable for applications in health, sports, and entertainment.     

High‐Density Stretchable Electrode Grids for Chronic Neural Recording

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long‐term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long‐term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high‐density, stretchable electrode grid based on an inert, high‐performance composite material comprising gold‐coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications.     

MXene Materials for Designing Advanced Separation Membranes

MXenes are emerging rapidly as a new family of multifunctional nanomaterials with prospective applications rivaling that of graphenes. Herein, a timely account of the design and performance evaluation of MXene-based membranes is provided. First, the preparation and physicochemical characteristics of MXenes are outlined, with a focus on exfoliation, dispersion stability, and processability, which are crucial factors for membrane fabrication. Then, different formats of MXene-based membranes in the literature are introduced, comprising pristine or intercalated nanolaminates and polymer-based nanocomposites. Next, the major membrane processes so far pursued by MXenes are evaluated, covering gas separation, wastewater treatment, desalination, and organic solvent purification. The potential utility of MXenes in phase inversion and interfacial polymerization, as well as layer-by-layer assembly for the preparation of nanocomposite membranes, is also critically discussed. Looking forward, exploiting the high electrical conductivity and catalytic activity of certain MXenes is put into perspective for niche applications that are not easily achievable by other nanomaterials. Furthermore, the benefits of simulation/modeling approaches for designing MXene-based membranes are exemplified. Overall, critical insights are provided for materials science and membrane communities to navigate better while exploring the potential of MXenes for developing advanced separation membranes.     

Can Reconstructed Se-Deficient Line Defects in Monolayer VSe2 Induce Magnetism?

There have been several recent conflicting reports on the ferromagnetism of clean monolayer VSe₂. Herein, the controllable formation of 1D defect line patterns in vanadium diselenide (VSe₂) monolayers initiated by thermal annealing is presented. Using scanning tunneling microscopy and q-plus atomic force microscopy techniques, the 1D line features are determined to be 8-member-ring arrays, formed via a Se deficient reconstruction process. The reconstructed VSe₂ monolayer with Se-deficient line defects displays room-temperature ferromagnetism under X-ray magnetic circular dichroism and magnetic force microscopy, consistent with the density functional theory calculations. This study possibly resolves the controversy on whether ferromagnetism is intrinsic in monolayer VSe₂, and highlights the importance of controlling and understanding the atomic structures of surface defects in 2D crystals, which could play key roles in the material properties and hence potential device applications.     

Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and electronic sources. Only those articles written in the English language were selected as it is the most prominent language used in scientific, engineering, computer science, and biomedical researches. This literature review was conducted on the articles published between 2006 and 2020. A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening. The findings from those researches were compiled at the end of the result section. This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.     

Additive-Free Aqueous MXene Inks for Thermal Inkjet Printing on Textiles

Direct printing of functional inks onto flexible substrates allows for scalable fabrication of wearable electronics. However, existing ink formulations for inkjet printing require toxic solvents and additives, which make device fabrication more complex, limit substrate compatibility, and hinder device performance. Even water-based carbon or metal nanoparticle inks require supplemental surfactants, binders, and cosolvents to produce jettable colloidal suspensions. Here, a general approach is demonstrated for formulating conductive inkjet printable, additive-free aqueous Ti₃C₂T_x MXene inks for direct printing on various substrates. The rheological properties of the MXene inks are tuned by controlling the Ti₃C₂T_x flake size and concentration. Ti₃C₂T_x-based electrical conduits and microsupercapacitors (MSCs) are printed on textile and paper substrates by optimizing the nozzle geometry for high-resolution inkjet printing. The chemical stability and electrical properties of the printed devices are also studied after storing the devices for six months under ambient conditions. Current collector-free, textile-based MSCs show areal capacitance values up to 294 mF cm⁻² (2 mV s⁻¹) in poly(vinyl alcohol)/sulfuric acid gel electrolyte, surpassing reported printed MXene-based MSCs and inkjet-printed MSCs using other 2D nanomaterials. This work is an important step toward increasing the functional capacity of conductive inks and simplifying the fabrication of wearable textile-based electronics.     

DDoS Detection in SDN using Machine Learning Techniques

Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of the SDN that has a network-wide impact. Machine learning is now widely used for fast detection of these attacks. In this paper, some important feature selection methods for machine learning on DDoS detection are evaluated. The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller. A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks. The experimental results show that the Random forest (RF) classifier trains the more accurate model with 99.97% accuracy using features subset by the Recursive feature elimination (RFE) method.     

Distrust seed set propagation algorithm to detect web spam

Web spam uses numerous techniques to misguide Web search engines in exchange of financial profit. A myriad of semi-automatic propagation model has been proposed with the purpose of combating Web spam. In this paper, distrust propagation is used to detect Web spam. An automatic distrust seed set propagation algorithm (DSP), which acts as an extension to the seed set to propagate distrust further to detect more Web spam. Experiments are conducted on WEBSPAM-UK2006 and WEBSPAM-UK2007 dataset; the results have shown that DSP enhanced the baseline algorithms and detected 17.73 % more spam hosts in the former dataset and detected 8.59 % more spam hosts in later dataset.     

Analysis of direct immobilized recombinant protein G on a gold surface

For the immobilization of IgG, various techniques such as chemical linker, thiolated protein G methods, and fragmentation of antibodies have been reported [Y.M. Bae, B.K. Oh, W. Lee, W.H. Lee, J.W. Choi, Biosensors Bioelectron. 21 (2005) 103; W. Lee, B.K. Oh, W.H. Lee, J.W. Choi, Colloids Surf. B-Biointerfaces, 40 (2005) 143; A.A. Karyakin, G.V. Presnova, M.Y. Rubtsova, A.M. Egorov, Anal. Chem. 72 (2000) 3805]. Here, we modified the immunoglobulin Fc-binding B-domain of protein G to contain two cysteine residues at its C-terminus by a genetic engineering technique. The resulting recombinant protein, RPGcys, retained IgG-binding activity in the same manner as native protein G. RPGcys was immobilized on a gold surface by strong affinity between thiol of cysteine and gold. The orientations of both IgG layers immobilized on the base recombinant protein Gs were analyzed by fluorescence microscope, atomic force microscope (AFM), and surface plasmon resonance (SPR). Our data revealed that IgG-binding activity of RPGcys on gold surface significantly increased in comparison to wild type of protein G (RPGwild), which was physically adsorbed due to absence of cysteine residue. Immobilization of highly oriented antibodies based on cysteine-modified protein G could be useful for the fabrication of immunosensor systems.     

Integrating guanxi into technology acceptance: An empirical investigation of WeChat

Social influence is an important research topic in the technology acceptance literature, in particular for social media. Prior empirical studies have for the most part employed social influence theory to investigate user intentions to continue with social media, while culture driven theories have been neglected. Rather than using social influence theory, we introduced guanxi theory to explore how guanxi social mechanisms (or processes) influence Chinese users’ continuance intentions in WeChat. Specifically, we developed a model that examines the role of guanxi as manifested by renqing, mianzi and ganqing in perceived usefulness, perceived enjoyment and continuance intention in WeChat. A survey research method was adopted to test the proposed hypotheses. This study found that ganqing has a positive impact on perceived usefulness and continuance intention. Mianzi exerts a negative effect on continuance intention but exhibits a positive effect on perceived usefulness. Renqing was found to have no significant impact on perceived usefulness and continuance intention. Our study advances the Technology Acceptance Model (TAM) by introducing guanxi-based constructs in a Chinese mobile social-messaging application context. Our study also offers alternative insights on guanxi-based social influence processes in the Chinese technology acceptance literature.