Guiding of emulsion droplets in microfluidic chips along shallow tracks defined by laser ablation

We demonstrate controlled guiding of nanoliter emulsion droplets of polar liquids suspended in oil along shallow hydrophilic tracks fabricated at the base of microchannels located within microfluidic chips. The tracks for droplet guiding are generated by exposing the glass surface of polydimethylsiloxane (PDMS)-coated microscope slides via femtosecond laser ablation. The difference in wettability of glass and PDMS surfaces together with the shallow step-like transverse topographical profile of the ablated tracks allows polar droplets wetting preferentially the glass surface to follow the track. In this study, we investigate guiding of droplets of two different polar liquids (water/ethylene glycol) with and without surfactant suspended in an oil medium along surface tracks of different depths of 1, 1.5, and 2 \(\upmu\)m. The results of experiments are also verified with computational fluid dynamics simulations. Guiding of droplets along the tracks as a function of the droplet composition and size and the surface profile depth is evaluated by analyzing the trajectories of moving droplets with respect to the track central axis, and conditions for stable guiding are identified. The experiments and numerical simulations indicate that while the track topography plays a role in droplet guiding using 1.5- and 2-\(\upmu\)m deep tracks, for the case of the smallest track depth of 1 \(\upmu\)m, droplet guiding is mainly caused by surface energy modification along the track rather than the presence of a topographical step on the surface. Our results can be exploited to sort passively different microdroplets mixed in the same microfluidic chip, based on their inherent wetting properties, and they can also pave the way for guiding of droplets along reconfigurable tracks defined by surface energy modifications obtained using other external control mechanisms such as electric field or light.     

A new fault tolerant control approach for the three-tank system using data mining

In this study, we propose a knowledge-based approach for detection and isolation of sensor faults in fault tolerant control (FTC) of the three-tank system. Farthest first traversal algorithm (FFTA) of data mining is used first-time for the classification of faults in an FTC system. The sliding window is used to detect signal changes, which contain possible transients due to faults. The variance-changing ratio is calculated to extract the features of the sensor signal in each window. Then, FFTA is utilized for the isolation of sensor faults. In order to demonstrate the efficiency of the proposed method, seven types of artificial faults were applied to closed-loop fault tolerant control system in certain periods. All faults were detected and isolated immediately after they occurred. Moreover, fault isolation was achieved when multiple faults occurred simultaneously.     

XChange: A Universal Mechanism for Asset Exchange between Permissioned Blockchains

Permissioned blockchains are increasingly being used as a solution to record transactions between companies. Several use cases that leverage permissioned blockchains focus on the representation and management of real-world assets. Since the number of incompatible blockchains is quickly growing, there is an increasing need for a universal mechanism to exchange, or trade, digital assets between these isolated platforms. There currently is no universal mechanism for inter-blockchain asset exchange without a requirement for trusted authorities that coordinate the trade. We address this shortcoming and present XChange, a universal mechanism for asset exchange between permissioned blockchains. To achieve universality and to avoid trusted authorities that coordinate a trade, XChange does not provide atomic guarantees but leverages risk mitigation strategies to reduce value at stake. Our mechanism records the specifications and progression of each trade within records on a distributed log. XChange reduces the economic gains of adversaries by bounding the total amount of fraud they can commit at any time. After having committed fraud, an adversary is forced to finish its ongoing trades before it can engage in new trades. We first present a four-phased protocol that coordinates an asset exchange between two traders. We then outline how trade records can be stored on TrustChain, which is a lightweight distributed ledger specifically built for the tamper-proof storage of data elements. We implement XChange and conduct experiments. Our experiments demonstrate that XChange is capable of reducing the economic gains of adversaries by more than 99.9% when replaying a real-world trading dataset. A deployment on low-resource devices reveals that the latency added to a trade by XChange is only 493 milliseconds. Finally, our scalability evaluation shows that XChange achieves over 1’000 trades per second and that its throughput, in terms of trades per second, scales linearly with the system load.

     

Spectral feature projections that maximize Shannon mutual information with class labels

Determining optimal subspace projections that can maintain task-relevant information in the data is an important problem in machine learning and pattern recognition. In this paper, we propose a nonparametric nonlinear subspace projection technique that maintains class separability maximally under the Shannon mutual information (MI) criterion. Employing kernel density estimates for nonparametric estimation of MI makes possible an interesting marriage of kernel density estimation-based information theoretic methods and kernel machines, which have the ability to determine nonparametric nonlinear solutions for difficult problems in machine learning. Significant computational savings are achieved by translating the definition of the desired projection into the kernel-induced feature space, which leads to obtain analytical solution.     

Optimal deflection and stacking sequence prediction of curved composite structure using hybrid (FEM and soft computing) technique

Engineering with Computers - The bending deflections and the corresponding optimal fiber angle sequences of the subsequent layers have been predicted in this article using a hybrid technique. The...     

Effect of Applied Current Density on Morphological and Structural Properties of Electrodeposited Fe-Cu Films

A detailed study has been carried out to investigate the effect of applied current density on the composition, crystallographic structure, grain size, and surface morphology of Fe-Cu films. X-ray diffraction (XRD) results show that the films consist of a mixture of face-centered cubic (fcc) Cu and body centered cubic (bcc) α-Fe phases. The average crystalline size of both Fe and Cu particles decreases as the applied current density becomes more negative. Compositional analysis of Fe-Cu films indicates that the Fe content within the films increases with decreasing current density towards more negative values. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) have been used to investigate the surface morphology of Fe-Cu films. It is observed that the surface morphology of the films changes from dendritic structure to a cauliflower structure as the applied current density becomes more negative. The surface roughness and grain size of the Fe-Cu films decrease with decreasing applied current density towards more negative values.     

Deposition potential dependence of composition, microstructure, and surface morphology of electrodeposited Ni–Cu alloy films

Composition, microstructure, and surface morphology of Ni–Cu alloy films electrodeposited at different deposition potentials have been investigated. The microstructural analysis carried out by using X-ray diffraction (XRD) confirmed that all Ni–Cu films are polycrystalline in nature and possess face-centered cubic structure. XRD analysis also revealed that the (111) peak of the Ni–Cu alloy films splits into two as Cu-rich and Ni-rich peaks and the peak intensities change depending on the deposition potential and hence the film composition. Compositional analysis of Ni–Cu films carried out by energy dispersive X-ray spectroscopy showed that Ni content within the films increases as the deposition potential becomes more negative. The morphological analysis performed by using a scanning electron microscopy and an atomic force microscopy revealed that the surface morphology changes significantly with applied deposition potential. Furthermore, a direct correlation is observed between the surface roughness and lattice strain.     

Effect of Deposition Time on Properties of Ni–Cu Alloy Films Electrodeposited on ITO Coated Glass Substrates

The structural, magnetic, and surface morphological properties of Ni–Cu films electrodeposited on ITO (indium tin oxide) glass substrates at different deposition times ranging between 2 s and 600 s have been investigated. The structure of the films was studied using X-ray Diffraction (XRD). The XRD results showed that all samples have a face-centered cubic (FCC) structure. From the XRD patterns, it was also found that the crystallographic structure of the films strongly depends on the deposition time. Compositional analysis of Ni–Cu films carried out by energy dispersive X-ray spectroscopy (EDX) indicated that the Ni content within the films increases with increasing deposition time and then almost saturates at deposition time of 600 s. The result of the vibrating sample magnetometer (VSM) measurements revealed that the saturation magnetization increases with increasing Ni content within the film. Atomic force microscopy (AFM) was used to study the topographic properties of Ni–Cu films. It was found that the surface roughness of Ni–Cu alloy films increases with increasing deposition time. Furthermore, the surface texture was found to be isotropic for all films grown at different deposition times.