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We report the use of fluorinated polymer zwitterions to build hybrid systems for efficient CO₂electroreduction. 

Transaction fee mechanism design is a new decentralized mechanism design problem where users bid for space on the blockchain. Several recent works showed that the transaction fee mechanism design fundamentally departs from classical mechanism design. They then systematically explored the mathematical landscape of this new decentralized mechanism design problem in two settings: in the plain setting where no cryptography is employed, and in a cryptography-assisted setting where the rules of the mechanism are enforced by a multi-party computation protocol. Unfortunately, in both settings, prior works showed that if we want the mechanism to incentivize honest behavior for both users as well as miners (possibly colluding with users), then the miner revenue has to be zero. Although adopting a relaxed, approximate notion of incentive compatibility gets around this zero miner-revenue limitation, the scaling of the miner revenue is nonetheless poor. In this paper, we show that if we make a mild reasonable-world assumption that there are sufficiently many honest users, we can circumvent the known limitations on miner revenue, and design auctions that generate asymptotically optimal miner revenue. We also systematically explore the mathematical landscape of transaction fee mechanism design under the new reasonable-world assumptions, and demonstrate how such assumptions can alter the feasibility and infeasibility landscape. 

We report a facile method to prepare polymer-grafted plasmonic metal nanoparticles (NPs) that exhibit pH-responsive surface-enhanced Raman scattering (SERS). The concept is based on the use of pH- responsive polymers, such as poly(acrylic acid) (PAA) and poly(allylamine hydrochloride) (PAH), as multi- dentate ligands to wrap around the surface of NPs instead of forming polymer brushes. Upon changing the solvent quality, the grafted pH-responsive polymers would drive reversible aggregation of NPs, leading to a decreased interparticle distance. This creates numerous hot spots, resulting in a secondary enhancement of SERS as compared to the SERS from discrete NPs. For negatively charged PAA-grafted NPs, the SERS response at pH 2.5 showed a secondary enhancement of up to 104-fold as compared to the response for discrete NPs at pH 12. Similarly, positively charged PAH-grafted AuNPs showed an oppo- site response to pH. We demonstrated that enhanced SERS with thiol-containing and charged molecular probes was indeed from the pH-driven solubility change of polymer ligands. Our method is different from the conventional SERS sensors in the solid state. With pH-responsive polymer-grafted NPs, SERS can be performed in solution with high reproducibility and sensitivity but without the need for sample pre-con- centration. These findings could pave the way for innovative designs of polymer ligands for metal NPs where polymer ligands do not compromise interparticle plasmon coupling. 

We report a generalized platform for synthesizing a polymer nanoweb with a high specific surface area via a bicellar template, composed of 1,2-dipalmitoyl phosphocholine (DPPC), 1,2-dihexanoyl phosphocholine (DHPC), and 1,2-dipalmitoyl phosphoglycerol (DPPG). The pristine bicelle (in the absence of monomer or polymer) yields a variety of well-defined structures, including disc, vesicle, and perforated lamella. The addition of styrene monomers in the mixture causes bicelles to transform into lamellae. Monomers are miscible with DPPC and DPPG initially, while polymerization drives polymers to the DHPC-rich domain, resulting in a polymer nanoweb supported by the outcomes of small angle neutron scattering, differential scanning calorimetry, and transmission electron microscopy. 

We report the hydrophobicity-enhanced reactivity of Cu²⁺ions as an ester hydrolase. 

Hypothesis: A well-defined discoidal bicelle composed of three lipids, specifically zwitterionic long-chain 1,2 dipalmitoyl phosphocholine (DPPC) and short-chain 1,2 dihexanoyl phosphocholine (DHPC) doped with anionic 1,2 dipalmitoyl phosphoglycerol (DPPG) provides a generalized template for the synthesis of hydrophobic polymer nano-rings. The lipid molar ratio of DPPC/DHPC/DPPG is 0.71/0.25/0.04. The detailed investigation and discussion were based on styrene but tested on three other vinyl monomers. Experiments: The structure of nano-rings is identified through the detailed analysis of small angle X-ray/ neutron scattering (SAXS and SANS) data and transmission electron micrographs (TEM), supported by the differential scanning calorimetric (DSC) data before and after polymerization. The investigation covers samples with a styrene-to-lipid ratio ranged varied from 1:50 to 1:10. Findings: The styrene monomers are initially located at both the discoidal planar (long-chain lipid rich) and rim (short-chain lipid rich) regions. During polymerization, they migrate to the more fluid rim regionsection. The formation mechanism involves the interplay of hydrophobic interaction, mismatched miscibility of polystyrene between the ordered and disordered phases, and crystallinity of the long lipid acyl chains. This facile synthesis is proven applicable for several hydrophobic monomers. The welldefined nano-rings greatly enhance the interfacial area and have the potential to be the building blocks for functional materials, if monomers are incorporated with desirable functions, for future applications. 

Abstract We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledge of the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From in-distribution tests, the predictivity of our approach presents good generalizability to different loading conditions and outperforms the conventional constitutive modeling at approximately one order of magnitude. When tested on out-of-distribution loading ratios, the neural operator learning approach becomes less effective. To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge. This method is shown to improve the model's extrapolative performance in the small-deformation regime. Our results demonstrate that with sufficient data coverage and/or guidance from partial physics constraints, the data-driven approach can be a more effective method for modeling biological materials than the traditional constitutive modeling.