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  1. Abstract Paper-based electrochemical sensors provide the opportunity for low-cost, portable and environmentally friendly single-use chemical analysis and there are various reports of surface-functionalized paper electrodes. Here we report a composite paper electrode that is fabricated through designed papermaking using cellulose, carbon fibers (CF), and graphene oxide (GO). The composite paper has well-controlled structure, stable, and repeatable properties, and offers the electrocatalytic activities for sensitive and selective chemical detection. We demonstrate that this CF/GO/cellulose composite paper can be reduced electrochemically using relatively mild conditions and this GO reduction confers electrocatalytic properties to the composite paper. Finally, we demonstrate that this composite paper offers sensing performance (sensitivity and selectivity) comparable to, or better than, paper-based sensors prepared by small-batch surface-modification (e.g., printing) methods. We envision this coupling of industrialized papermaking technologies with interfacial engineering and electrochemical reduction can provide a platform for single-use and portable chemical detection for a wide range of applications.
    Free, publicly-accessible full text available December 1, 2023
  2. Free, publicly-accessible full text available June 1, 2023
  3. Here, in ionically conducting Na 0.5 Bi 0.5 TiO 3 (NBT), we explore the link between growth parameters, stoichiometry and resistive switching behavior and show NBT to be a highly tunable system. We show that the combination of oxygen ionic vacancies and low-level electronic conduction is important for controlling Schottky barrier interfacial switching. We achieve a large ON/OFF ratio for high resistance/low resistance ( R HRS / R LRS ), enabled by an almost constant R HRS of ∼10 9 Ω, and composition-tunable R LRS value modulated by growth temperature. R HRS / R LRS ratios of up to 10 4 and pronounced resistive switching at low voltages (SET voltage of <1.2 V without high-voltage electroforming), strong endurance (no change in resistance states after several 10 3 cycles), uniformity, stable switching and fast switching speed are achieved. Of particular interest is that the best performance is achieved at the lowest growth temperature studied (600 °C), which is opposite to the case of most other perovskite oxides for memristors, where higher growth temperatures are required for optimum performance. This is understood based on the oxygen vacancy control of interfacial switching in NBT, whereas a range of other mechanisms (including filamentary switching)more »occur in other perovskites. The study of NBT has enabled us to determine key parameters for achieving high performance memristors.« less
  4. Sampling based planning is an important step for long-range navigation for an autonomous vehicle. This work proposes a GPU-accelerated sampling based path planning algorithm which can be used as a global planner in autonomous navigation tasks. A modified version of the generation portion for the Probabilistic Road Map (PRM) algorithm is presented which reorders some steps of the algorithm in order to allow for parallelization and thus can benefit highly from utilization of a GPU. The GPU and CPU algorithms were compared using a simulated navigation environment with graph generation tasks of several different sizes. It was found that the GPU-accelerated version of the PRM algorithm had significant speedup over the CPU version (up to 78×). This results provides promising motivation towards implementation of a real-time autonomous navigation system in the future.
  5. Abstract. This paper studies how to improve the accuracy of hydrologic models using machine-learning models as post-processors and presents possibilities to reduce the workload to create an accurate hydrologic model by removing the calibration step. It is often challenging to develop an accurate hydrologic model due to the time-consuming model calibration procedure and the nonstationarity of hydrologic data. Our findings show that the errors of hydrologic models are correlated with model inputs. Thus motivated, we propose a modeling-error-learning-based post-processor framework by leveraging this correlation to improve the accuracy of a hydrologic model. The key idea is to predict the differences (errors) between the observed values and the hydrologic model predictions by using machine-learning techniques. To tackle the nonstationarity issue of hydrologic data, a moving-window-based machine-learning approach is proposed to enhance the machine-learning error predictions by identifying the local stationarity of the data using a stationarity measure developed based on the Hilbert–Huang transform. Two hydrologic models, the Precipitation–Runoff Modeling System (PRMS) and the Hydrologic Modeling System (HEC-HMS), are used to evaluate the proposed framework. Two case studies are provided to exhibit the improved performance over the original model using multiple statistical metrics.