A green, simple, and cost effective electrochemical method to synthesize pure graphene oxide (GO) and graphene nanosheets (GNs) using pencil in ionic liquid medium is reported. The morphology and microstructure of prepared GNs and GO are examined using scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), X‐ray diffraction (XRD), and Raman spectroscopy; the experiments confirm the formation of high quality graphene. The synthesized GO is used for the real‐time and label‐free surface plasmon resonance (SPR) sensing of the biological warfare agent Salmonella typhi.

Ionic liquids (ILs) are salt that exist in the liquid phase at and around 298 K and are comprised of a bulky, asymmetric organic cation and the anion usually inorganic ion but some ILs also with organic anion. ILs have attracted much attention as a replacement for traditional organic solvents as they possess many attractive properties. Among these properties, intrinsic ion conductivity, low volatility, high chemical and thermal stability, low combustibility, and wide electrochemical windows are few. Due to negligible or nonzero volatility of these solvents, they are considered “greener” for the environment as they do not evaporate like volatile organic compounds (VOCs). ILs have been widely used in electrodeposition, electrosynthesis, electrocatalysis, electrochemical capacitor, lubricants, plasticizers, solvent, lithium batteries, solvents to manufacture nanomaterials, extraction, gas absorption agents, and so forth. Besides a brief discussion of the introduction, history, and properties of ILs the major purpose of this review paper is to provide an overview on the advantages of ILs for the synthesis of conducting polymer and nanoparticle when compared to conventional media and also to focus on the electrochemical sensors and biosensors based on IL/composite modified macrodisk electrodes. Subsequently, recent developments and major strategies for enhancing sensing performance are discussed.

Background: High throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, the grain bearing organs of wheat, is critical for phenomics of a large set of germplasm and breeding lines in controlled and field conditions. It is also required for precision agriculture where the application of nitrogen, water, and other inputs at this critical stage is necessary. Further, counting of spikes is an important measure to determine yield. Digital image analysis and machine learning techniques play an essential role in non-destructive plant phenotyping analysis. Results:In this study, an approach based on computer vision, particularly object detection, to recognize and count the number of spikes of the wheat plant from the digital images is proposed. For spike identification, a novel deeplearning network, SpikeSegNet, has been developed by combining two proposed feature networks: Local Patch extraction Network (LPNet) and Global Mask refinement Network (GMRNet). In LPNet, the contextual and spatial features are learned at the local patch level. The output of LPNet is a segmented mask image, which is further refined at the global level using GMRNet. Visual (RGB) images of 200 wheat plants were captured using LemnaTec imaging system installed at Nanaji Deshmukh Plant Phenomics Centre, ICAR-IARI, New Delhi. The precision, accuracy, and robustness (F 1 score) of the proposed approach for spike segmentation are found to be 99.93%, 99.91%, and 99.91%, respectively. For counting the number of spikes, "analyse particles"-function of imageJ was applied on the output image of the proposed SpikeSegNet model. For spike counting, the average precision, accuracy, and robustness are 99%, 95%, and 97%, respectively. SpikeSegNet approach is tested for robustness with illuminated image dataset, and no significant difference is observed in the segmentation performance. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article' Conclusion: In this study, a new approach called as SpikeSegNet has been proposed based on combined digital image analysis and deep learning techniques. A dedicated deep learning approach has been developed to identify and count spikes in the wheat plants. The performance of the approach demonstrates that SpikeSegNet is an effective and robust approach for spike detection and counting. As detection and counting of wheat spikes are cl...

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