The technological importance of cost-effective zinc-doped nickel oxide (Zn:NiO) coatings is demonstrated by applying them to energy saving-electrochromic devices (ECDs) as well as energy storing-asymmetric supercapacitors (ASCs). These devices encompass an inexpensive nonaqueous gel electrolyte of ZnCl 2 /γ-butyrolactone/ ferrocene (Fc)/poly(vinyl butyral), where Fc serves as a redox mediator and accelerates charge transfer and transport kinetics, enhancing both ion-storage and optical modulation responses. This report showcases the prowess of a simple doping and a redox-gel in an unprecedented manner, wherein the improved dc and ac electrical conduction by an order of magnitude, the star-like morphology of Zn:NiO compared to NiO microflowers, and the high conductivity of the gel (33 mS cm −1 ) come to the fore in an ASC [with activated carbon (AC) derived from corncobs as the anode, Zn:NiO//AC], which delivers a very high specific capacitance (SC) of 460 F g −1 (at 1.25 A g −1 ), comparable to expensive rare oxides, a battery like energy density of 164 W h kg −1 (at 1 kW kg −1 ), and a cycle life of 10,000 cycles with 95% SC retention. The potential of Zn doping is further showcased through the panchromatic visible light modulation of the Zn:NiO//WO 3 ECD, switching reversibly between the colorless and the dark black−brown states, accompanied by a remarkably high integrated transmission modulation (ΔT 400−900nm ) of 60.2%, sustained over 3500 cycles with no performance loss, and color/bleach times of 10 s/5 s, most suitable for smart window applications.

Pure alumina and zirconia powders were sintered separately with increasing amount of TiC up to ~ 65 vol.%, as a conducting second phase with an aim to prepare conducting structural ceramics which can be precisely machined by EDM technique. TiC did not help in sintering the parent phase but it decreased the d.c. resistivity of the composite to 1 ohm.cm at ~ 30 vol.% loading. The conductivity is explained by the effective media and percolation theories.

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As practitioners of machine learning in the area of bioinformatics we know that the quality of the results crucially depends on the quality of our labeled data. While there is a tendency to focus on the quality of positive examples, the negative examples are equally as important. In this opinion paper we revisit the problem of choosing negative examples for the task of predicting protein-protein interactions, either among proteins of a given species or for host-pathogen interactions and describe important issues that are prevalent in the current literature. The challenge in creating datasets for this task is the noisy nature of the experimentally derived interactions and the lack of information on non-interacting proteins. A standard approach is to choose random pairs of non-interacting proteins as negative examples. Since the interactomes of all species are only partially known, this leads to a very small percentage of false negatives. This is especially true for host-pathogen interactions. To address this perceived issue, some researchers have chosen to select negative examples as pairs of proteins whose sequence similarity to the positive examples is sufficiently low. This clearly reduces the chance for false negatives, but also makes the problem much easier than it really is, leading to over-optimistic accuracy estimates. We demonstrate the effect of this form of bias using a selection of recent protein interaction prediction methods of varying complexity, and urge researchers to pay attention to the details of generating their datasets for potential biases like this.