Organic electronics is emerging for large-area applications such as photovoltaic cells, rollable displays or electronic paper. Its future development and integration will require a simple, low-power organic memory, that can be written, erased and readout electrically. Here we demonstrate a non-volatile memory in which the ferroelectric polarisation state of an organic tunnel barrier encodes the stored information and sets the readout tunnel current. We use high-sensitivity piezoresponse force microscopy to show that films as thin as one or two layers of ferroelectric poly(vinylidene fluoride) remain switchable with low voltages. Submicron junctions based on these films display tunnel electroresistance reaching 1,000% at room temperature that is driven by ferroelectric switching and explained by electrostatic effects in a direct tunnelling regime. Our findings provide a path to develop low-cost, large-scale arrays of organic ferroelectric tunnel junctions on silicon or flexible substrates.

In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem -the task of finding k seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solutiondependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.

High-throughput continuous hydrothermal flow synthesis has been used as a rapid and efficient synthetic route to produce a range of crystalline nanopowders in the CeZn oxide binary system. High-resolution powder X-ray diffraction data were obtained for both as-prepared and heat-treated (850• C for 10 h in air) samples using the new robotic beamline I11, located at Diamond Light Source. The influence of the sample composition on the crystal structure and on the optical and physical properties was studied. All the nanomaterials were characterized using Raman spectroscopy, UV-visible spectrophotometry, Brunauer-Emmett-Teller surface area and elemental analysis (via energy-dispersive X-ray spectroscopy). Initially, for 'as-prepared' Ce 1−x Zn x O y , a phasepure cerium oxide (fluorite) structure was obtained for nominal values of x = 0.1 and 0.2. Biphasic mixtures were obtained for nominal values of x in the range of 0.3-0.9 (inclusive). High-resolution transmission electron microscopy images revealed that the phase-pure nano-CeO 2 (x = 0) consisted of ca 3.7 nm well-defined nanoparticles. The nanomaterials produced herein generally had high surface areas (greater than 150 m 2 g −1 ) and possessed combinations of particle properties (e.g. bandgap, crystallinity, size, etc.) that were unobtainable or difficult to achieve by other more conventional synthetic methods.

We report on the commissioning experimental run of the rapid automated materials synthesis instrument (RAMSI), a combinatorial robot designed to manufacture, clean, and print libraries of nanocrystal precursor solid compositions. The first stage of RAMSI, parallel synthesis, uses a fully automated high throughput continuous hydrothermal (HiTCH) flow reactor for automatic metal salt precursor mixing, hydrothermal flow reaction, and sample slurry collection. The second stage of RAMSI provides integrated automated cleanup, and the third section is a ceramic printing function. Nanocrystal precursor solid ceramics were synthesized from precursor solutions and collected into 50 mL centrifuge tubes where they were cleaned by multiple centrifugation and redispersion cycles (monitored by intelligent scanning turbidimetry) and printed with an automated pipette. Eight unique compositions of a model phosphor library comprising pure nano-Y(OH)(3) and Eu(3+) doped-yttrium hydroxide, Y(OH)(3):Eu(3+) nanocrystal precursor solid were synthesized (with 2 centrifuge tubes' worth collected per composition), processed, and printed in duplicate as 75, 100, and 125 microL dots in a 21.6 ks (6 h) experiment (note: the actual time for synthesis of each sample tube was only 12 min so up to 60 compositions could easily be synthesized in 12 h if one centrifuge tube per composition was collected instead). The Y(OH)(3):Eu(3+) samples were manually placed in a furnace and heat-treated in air for 14.4 ks (4 h) in the temperature range 200-1200 at 100 degrees C intervals (giving a total of 84 samples plus one as-prepared pure Y(OH)(3) sample). The as-prepared and heat-treated ceramic samples were affixed to 4 mm wide hemispherical wells in a custom-made aluminum well-plate and analyzed using a fluorescence spectrometer. When the library was illuminated with a 254 nm light source (and digitally imaged and analyzed), the 3 mol % Eu(3+) sample heat-treated at 1200 degrees C gave the most intense fluorescence (major red peak at 612 nm); however, an identical nanocrystal precursor heat-treated at only 500 degrees C (identified as Y(2)O(3):Eu(3+) after heat treatment) was the brightest phosphor under illumination of the samples heat-treated at or below 1000 degrees C.

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Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks (referred collectively as items in this paper) are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods for these topics to avoid redoing influence maximization for each item from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.

antecedents and effects of career adaptability: an integration of career construction theory and event system theory").

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