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Ion transport is essential to energy storage, cellular signaling, and desalination. Polymers have been explored for decades as solid-state electrolytes by either adding salt to polar polymers or tethering ions to the backbone to create less flammable and more robust systems. New design paradigms are needed to advance the performance of solid polymer electrolytes beyond conventional systems. Here, the role of a helical secondary structure is shown to greatly enhance the conductivity of solvent-free polymer electrolytes using cationic polypeptides with a mobile anion. Longer helices lead to higher conductivity, and random coil peptides show substantially lower conductivity. The macrodipole of the helix increases with peptide length leading to larger dielectric constants. The hydrogen bonding of the helix also imparts thermal and electrochemical stability, while allowing for facile dissolution back to monomer in acid. Peptide polymer electrolytes present a promising platform for the design of next generation ion transporting materials. 

Biometric authentication systems are increasingly needed across a broad range of applications including in smart city environments (e.g., entering hotels), and in smart home environments (e.g., controlling smart devices). Traditional methods, such as face-based and fingerprint-based authentication, usually incur high costs to be installed in all this kind of environments. In this paper, we develop a ubiquitous low-effort user authentication approach, mmPalm, based on palm recognition using millimeter wave (mmWave) signals. mmWave technology has been adopted by WiGig and 5G, making mmPalm a low-cost solution that can be widely adopted in public places. In addition, the high resolution of mmWave signals allows mmPalm to extract detailed palm characteristics (e.g., palm geometry, skin thickness, and texture) that can assemble distinctive palmprints for user authentication. Our innovative virtual antennas design further increases the spatial resolution of a commercial mmWave device, enabling it to fully capture the comprehensive palmprint features. Moreover, to address the challenge of small-scale environmental changes (e.g., variations in palm-device distances and palm orientations), we design a novel palm profile augmentation method, utilizing a Conditional Generative Adversarial Network (cGAN) to generate synthetic palm profiles for mitigating palm instability. Furthermore, we design a cross-environment adaptation framework based on transfer learning to address the challenge of large-scale environmental changes, including multipath variations introduced by human bodies and nearby furniture. Extensive experiments with 30 participants through 6 months demonstrate that mmPalm achieves 99% authentication accuracy with resilience against different types of attacks, including random, impersonation, and counterfeit. 

Deep neural networks (DNNs) have been widely deployed in real-world, mission-critical applications, necessitating effective approaches to protect deep learning models against malicious attacks. Motivated by the high stealthiness and potential harm of backdoor attacks, a series of backdoor defense methods for DNNs have been proposed. However, most existing approaches require access to clean training data, hindering their practical use. Additionally, state-of-the-art (SOTA) solutions cannot simultaneously enhance model robustness and compactness in a data-free manner, which is crucial in resource-constrained applications. To address these challenges, in this paper, we propose Clean & Compact (C&C), an efficient data-free backdoor defense mechanism that can bring both purification and compactness to the original infected DNNs. Built upon the intriguing rank-level sensitivity to trigger patterns, C&C co-explores and achieves high model cleanliness and efficiency without the need for training data, making this solution very attractive in many real-world, resource-limited scenarios. Extensive evaluations across different settings consistently demonstrate that our proposed approach outperforms SOTA backdoor defense methods. 

Polymers that release small molecules in response to mechanical force are promising candidates as next-generation on-demand delivery systems. Despite advancements in the development of mechanophores for releasing diverse payloads through careful molecular design, the availability of scaffolds capable of discharging biomedically significant cargos in substantial quantities remains scarce. In this report, we detail a nonscissile mechanophore built from an 8-thiabicyclo[3.2.1]octane 8,8-dioxide (TBO) motif that releases one equivalent of sulfur dioxide (SO2) from each repeat unit. The TBO mechanophore exhibits high thermal stability but is activated mechanochemically using solution ultrasonication in either organic solvent or aqueous media with up to 63% efficiency, equating to 206 molecules of SO2 released per 143.3 kDa chain. We quantified the mechanochemical reactivity of TBO by single-molecule force spectroscopy and resolved its single-event activation. The force-coupled rate constant for TBO opening reaches ∼9.0 s–1 at ∼1520 pN, and each reaction of a single TBO domain releases a stored length of ∼0.68 nm. We investigated the mechanism of TBO activation using ab initio steered molecular dynamic simulations and rationalized the observed stereoselectivity. These comprehensive studies of the TBO mechanophore provide a mechanically coupled mechanism of multi-SO2 release from one polymer chain, facilitating the translation of polymer mechanochemistry to potential biomedical applications. 

The biological significance of self-assembled protein filament networks and their unique mechanical properties have sparked interest in the development of synthetic filament networks that mimic these attributes. Building on the recent advancement of autoaccelerated ring-opening polymerization of amino acid N-carboxyanhydrides (NCAs), this study strategically explores a series of random copolymers comprising multiple amino acids, aiming to elucidate the core principles governing gelation pathways of these purpose-designed copolypeptides. Utilizing glutamate (Glu) as the primary component of copolypeptides, two targeted pathways were pursued: first, achieving a fast fibrillation rate with lower interaction potential using serine (Ser) as a comonomer, facilitating the creation of homogeneous fibril networks; and second, creating more rigid networks of fibril clusters by incorporating alanine (Ala) and valine (Val) as comonomers. The selection of amino acids played a pivotal role in steering both the morphology of fibril superstructures and their assembly kinetics, subsequently determining their potential to form sample-spanning networks. Importantly, the viscoelastic properties of the resulting supramolecular hydrogels can be tailored according to the specific copolypeptide composition through modulations in filament densities and lengths. The findings enhance our understanding of directed self-assembly in high molecular weight synthetic copolypeptides, offering valuable insights for the development of synthetic fibrous networks and biomimetic supramolecular materials with custom-designed properties.