Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method. We use the short-time Fourier transform on 30-s EEG windows to extract information in both the frequency domain and the time domain. The algorithm automatically generates optimized features for each patient to best classify preictal and interictal segments. The method can be applied to any other patient from any dataset without the need for manual feature extraction. The proposed approach achieves sensitivity of 81.4%, 81.2%, and 75% and a false prediction rate of 0.06/h, 0.16/h, and 0.21/h on the Freiburg Hospital intracranial EEG dataset, the Boston Children's Hospital-MIT scalp EEG dataset, and the American Epilepsy Society Seizure Prediction Challenge dataset, respectively. Our prediction method is also statistically better than an unspecific random predictor for most of the patients in all three datasets.

Mercury pollution threatens the environment and human health across the globe. This neurotoxic substance is encountered in artisanal gold mining, coal combustion, oil and gas refining, waste incineration, chloralkali plant operation, metallurgy, and areas of agriculture in which mercury‐rich fungicides are used. Thousands of tonnes of mercury are emitted annually through these activities. With the Minamata Convention on Mercury entering force this year, increasing regulation of mercury pollution is imminent. It is therefore critical to provide inexpensive and scalable mercury sorbents. The research herein addresses this need by introducing low‐cost mercury sorbents made solely from sulfur and unsaturated cooking oils. A porous version of the polymer was prepared by simply synthesising the polymer in the presence of a sodium chloride porogen. The resulting material is a rubber that captures liquid mercury metal, mercury vapour, inorganic mercury bound to organic matter, and highly toxic alkylmercury compounds. Mercury removal from air, water and soil was demonstrated. Because sulfur is a by‐product of petroleum refining and spent cooking oils from the food industry are suitable starting materials, these mercury‐capturing polymers can be synthesised entirely from waste and supplied on multi‐kilogram scales. This study is therefore an advance in waste valorisation and environmental chemistry.

Here we report on the synthesis of a graphene/polyaniline (PANI) nanocomposite and its application in the development of a hydrogen (H2) gas sensor. Using a chemical synthetic route, graphene was prepared and ultrasonicated with a mixture of aniline monomer and ammonium persulfate to form PANI on its surface. The developed material was characterized by scanning electron microscopy (SEM), transmission electron microscopy, Raman spectroscopy, and X-ray photoemission spectroscopy. The SEM study revealed that the PANI in the composite has a nanofibrillar morphology. We investigated the H2 gas sensing performance of this material and compare it with that of the sensors based on only graphene sheets and PANI nanofibers. We found that the graphene/PANI nanocomposite-based device sensitivity is 16.57% toward 1% of H2 gas, which is much larger than the sensitivities of sensors based on only graphene sheets and PANI nanofibers.

Two dimensional molybdenum disulfide (MoS(2)) has recently become of interest to semiconductor and optic industries. However, the current methods for its synthesis require harsh environments that are not compatible with standard fabrication processes. We report on a facile synthesis method of layered MoS(2) using a thermal evaporation technique, which requires modest conditions. In this process, a mixture of MoS(2) and molybdenum dioxide (MoO(2)) is produced by evaporating sulfur powder and molybdenum trioxide (MoO(3)) nano-particles simultaneously. Further annealing in a sulfur-rich environment transforms majority of the excess MoO(2) into layered MoS(2). The deposited MoS(2) is then mechanically exfoliated into minimum resolvable atomically thin layers, which are characterized using micro-Raman spectroscopy and atomic force microscopy. Furthermore Raman spectroscopy is employed to determine the effect of electrochemical lithium ion exposure on atomically thin layers of MoS(2).

A conductometric H 2 , NO 2 , and hydrocarbon gas sensor based on single-crystalline zinc oxide (ZnO) nanobelts has been developed. The nanobelt sensitive layer was deposited using a radio frequency (RF) magnetron sputterer. The microcharacterization study reveals that the nanobelts have a single crystal hexagonal structure with average thickness and width of about 10 and 50 nm, respectively. The sensor was exposed to H 2 , NO 2 and propene gases at operating temperatures between 150 C and 450 C. The study showed that optimum operating temperatures for the sensor are in the range of 300 C-400 C for H 2 , 300 C-350 C for NO 2 , and 350 C-420 C for propene sensing.

We demonstrate a simple electrochemical route to produce uniformly sized gold nanospikes without the need for a capping agent or prior modification of the electrode surface, which are predominantly oriented in the {111} crystal plane and exhibit promising electrocatalytic and SERS properties.

Tetragonal BaTiO spheroids synthesized by a facile hydrothermal route using Tween 80 were observed to be polydispersed with a diameter in the range of ∼15-75 nm. Thereon, BaTiO spheroids were decorated with different percentages of Ag@CuO by wet impregnation, and their affinity toward carbon dioxide (CO) gas when employed as sensitive layers in a microsensor was investigated. The results revealed that the metal nanocomposite-based sensor had an exceptional stability and sensitivity toward CO gas (6-fold higher response), with appreciable response and recovery times (<10 s) and higher repeatability (98%) and accuracy (96%) at a low operating temperature of 120 °C, compared to those of pure BaTiO and CuO. Such improved gas-sensing performances even at a very low concentration (∼700 ppm) is attributable to both the chemical and electrical contributions of Ag@CuO forming intermittent nanointerfaces with BaTiO spheroids, exhibiting unique structural stability. The CO-sensing mechanism of CuO/BaTiO nanocomposite was studied by the diffuse reflectance infrared Fourier transform spectroscopy technique that established the reaction of CO with BaO and CuO to form the respective carbonate species that is correlated with the change in material resistance consequently monitored as sensor response.

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