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  1. Abstract

    All‐solid‐state batteries have the potential for enhanced safety and capacity over conventional lithium ion batteries, and are anticipated to dominate the energy storage industry. As such, strategies to enable recycling of the individual components are crucial to minimize waste and prevent health and environmental harm. Here, we use cold sintering to reprocess solid‐state composite electrolytes, specifically Mg and Sr doped Li7La3Zr2O12with polypropylene carbonate (PPC) and lithium perchlorate (LLZO−PPC−LiClO4). The low sintering temperature allows co‐sintering of ceramics, polymers and lithium salts, leading to re‐densification of the composite structures with reprocessing. Reprocessed LLZO−PPC−LiClO4exhibits densified microstructures with ionic conductivities exceeding 10−4 S/cm at room temperature after 5 recycling cycles. All‐solid‐state lithium batteries fabricated with reprocessed electrolytes exhibit a high discharge capacity of 168 mA h g−1at 0.1 C, and retention of performance at 0.2 C for over 100 cycles. Life cycle assessment (LCA) suggests that recycled electrolytes outperforms the pristine electrolyte process in all environmental impact categories, highlighting cold sintering as a promising technology for recycling electrolytes.

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    Free, publicly-accessible full text available March 19, 2025
  2. Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the context of various applications, e.g., next-word prediction and eHealth. FL involves various clients participating in the training process by uploading their local models to an FL server in each global iteration. The server aggregates these models to update a global model. The traditional FL process may encounter bottlenecks, known as the straggler problem, where slower clients delay the overall training time. This paper introduces the Latency-awarE Semi-synchronous client Selection and mOdel aggregation for federated learNing (LESSON) method. LESSON allows clients to participate at different frequencies: faster clients contribute more frequently, therefore mitigating the straggler problem and expediting convergence. Moreover, LESSON provides a tunable trade-off between model accuracy and convergence rate by setting varying deadlines. Simulation results show that LESSON outperforms two baseline methods, namely FedAvg and FedCS, in terms of convergence speed and maintains higher model accuracy compared to FedCS.

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    Free, publicly-accessible full text available November 1, 2024
  3. Cold sintering enabled the upcycling of polypropylene with gypsum (CaSO4) into a fully recyclable composite, paving the way for the integration of waste into high-performance, recyclable composites.

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    Free, publicly-accessible full text available January 1, 2025
  4. Free, publicly-accessible full text available September 1, 2024
  5. Abstract

    The rise in smart water technologies has introduced new cybersecurity vulnerabilities for water infrastructures. However, the implications of cyber‐physical attacks on the systems like urban drainage systems remain underexplored. This research delves into this gap, introducing a method to quantify flood risks in the face of cyber‐physical threats. We apply this approach to a smart stormwater system—a real‐time controlled network of pond‐conduit configurations, fitted with water level detectors and gate regulators. Our focus is on a specific cyber‐physical threat: false data injection (FDI). In FDI attacks, adversaries introduce deceptive data that mimics legitimate system noises, evading detection. Our risk assessment incorporates factors like sensor noises and weather prediction uncertainties. Findings reveal that FDIs can amplify flood risks by feeding the control system false data, leading to erroneous outflow directives. Notably, FDI attacks can reshape flood risk dynamics across different storm intensities, accentuating flood risks during less severe but more frequent storms. This study offers valuable insights for strategizing investments in smart stormwater systems, keeping cyber‐physical threats in perspective. Furthermore, our risk quantification method can be extended to other water system networks, such as irrigation channels and multi‐reservoir systems, aiding in cyber‐defense planning.

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  6. Abstract

    This work is to demonstrate a low cost and time-conserving technique to create nano-trenches by transferring nano-scale polymeric sidewalls into substrate. The polymeric sidewall is a vertically spreading layer deposited by spin-coating a polymer solution on a vertical template. By varying processing parameters such as the solution concentration or the spin-coating speed, the dimension of the sidewall can be changed, which, after pattern transfer, also changes the nano-trench dimension. In this work, high-resolution trenches of about 15 nm have been achieved after transferring straight line sidewalls into substrate. Other than straight line sidewall patterns, this method also fabricates ring-shaped patterns including circles, squares, and concentric squares. With various shapes of sidewall patterns, this technique has a potential to implement other practical applications such as fabricating high-resolution nanoimprint molds of 15 nm.

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