Search for: All records

Under the$\mathrm{AdS}/\mathrm{CFT}$correspondence, asymptotically anti–de Sitter geometries with backreaction can be viewed as conformal field theory states subject to a renormalization group (RG) flow from an ultraviolet (UV) description toward an infrared (IR) sector. For black holes, however, the IR point is the horizon, so one way to interpret the interior is as an analytic continuation to a “trans-IR” imaginary-energy regime. In this paper, we demonstrate that this analytic continuation preserves some imprints of the UV physics, particularly near its “end point” at the classical singularity. We focus on holographic phase transitions of geometric objects in round black holes. We first assert the consistency of interpreting such black holes, including their interiors, as RG flows by constructing a monotonic$a$function. We then explore how UV phase transitions of entanglement entropy and scalar two-point functions, each of which are encoded by bulk geometry under the holographic mapping, are related to the structure of the near-singularity geometry, which is quantified by Kasner exponents. Using 2D holographic flows triggered by relevant scalar deformations as test beds, we find that the 3D bulk’s near-singularity Kasner exponents can be viewed as functions of the UV physics precisely when the deformation is nonzero.

Published by the American Physical Society2024 

Biomass-derived polymer materials are emerging as sustainable and low-carbon footprint alternatives to the current petroleum-based commodity plastics. In the past decade, the ring-opening metathesis polymerization (ROMP) technique has been widely used for the polymerization of cyclic olefin monomers derived from biorenewable resources, giving rise to a diverse set of biobased polymer materials. However, most synthetic biobased polymers made by ROMP are nondegradable because of their all-carbon backbones. Herein, we present a modular synthetic strategy to acid-degradable poly(enol ether)s via ring-opening metathesis copolymerization of biorenewable oxanorbornenes and 3,4-dihydropyran (DHP). 1H NMR analysis reveals that the percentage of DHP units in the resulting copolymers gradually increases as the feed ratio of DHP to oxanorbornene increases. The composition of the copolymers plays a pivotal role in governing their thermal properties. Thermogravimetric analysis shows that an increasing percentage of DHP results in a decrease in the decomposition temperatures, suggesting that the incorporation of enol ether groups in the polymer backbone reduces the thermal stability of the copolymers. Moreover, a wide range of glass transition temperatures (16–165 °C) can be achieved by tuning the copolymer composition and the oxanorbornene structure. Critically, all of the poly(enol ether)s developed in this study are degradable under mildly acidic conditions. A higher incorporation of DHP in the copolymer leads to enhanced degradability, as evidenced by smaller final degradation products. Altogether, this study provides a facile approach for synthesizing biorenewable and degradable polymer materials with highly tunable thermal properties desired for their potential industrial applications. 

Bottlebrush polymers, macromolecules consisting of dense polymer side chains grafted from a central polymer backbone, have unique properties resulting from this well-defined molecular architecture. With the advent of controlled radical polymerization techniques, access to these architectures has become more readily available. However, synthetic challenges remain, including the need for intermediate purification, the use of toxic solvents, and challenges with achieving long bottlebrush architectures due to backbone entanglements. Herein, we report hybrid bonding bottlebrush polymers (systems integrating covalent and noncovalent bonding of structural units) consisting of poly(sodium 4-styrenesulfonate) (p(NaSS)) brushes grafted from a peptide amphiphile (PA) supramolecular polymer backbone. This was achieved using photoinitiated electron/energy transfer-reversible addition–fragmentation chain transfer (PET-RAFT) polymerization in water. The structure of the hybrid bonding bottlebrush architecture was characterized using cryogenic transmission electron microscopy, and its properties were probed using rheological measurements. We observed that hybrid bonding bottlebrush polymers were able to organize into block architectures containing domains with high brush grafting density and others with no observable brushes. This finding is possibly a result of dynamic behavior unique to supramolecular polymer backbones, enabling molecular exchange or translational diffusion of monomers along the length of the assemblies. The hybrid bottlebrush polymers exhibited higher solution viscosity at moderate shear, protected supramolecular polymer backbones from disassembly at high shear, and supported self-healing capabilities, depending on grafting densities. Our results demonstrate an opportunity for novel properties in easily synthesized bottlebrush polymer architectures built with supramolecular polymers that might be useful in biomedical applications or for aqueous lubrication. 

Degradable polymers made via ring-opening metathesis polymerization (ROMP) hold tremendous promise as eco-friendly materials. However, most of the ROMP monomers are derived from petroleum resources, which are typically considered less sustainable compared to biomass. Herein, we present a synthetic strategy to degradable polymers by harnessing alternating ROMP of biomass-based cyclic olefin monomers including exo-oxanorbornenes and cyclic enol ethers. A library of well-defined poly(enol ether)s with modular structures, tunable glass transition temperatures, and controlled molecular weights was achieved, demonstrating the versatility of this approach. Most importantly, the resulting copolymers exhibit high degrees of alternation, rendering their backbones fully degradable under acidic conditions. 

The current insufficient recycling of commodity polymer waste has resulted in pressing environmental and human health issues in our modern society. In the quest for next-generation polymer materials, chemists have recently shifted their attention to the design of chemically recyclable polymers that can undergo depolymerization to regenerate monomers under mild conditions. During the past decade, ring-closing metathesis reactions have been demonstrated to be a robust approach for the depolymerization of polyolefins, producing low-strain cyclic alkene products which can be repolymerized back to new batches of polymers. In this review, we aim to highlight the recent advances in chemical recycling of polyolefins enabled by ring-closing metathesis depolymerization (RCMD). A library of depolymerizable polyolefins will be covered based on the ring size of their monomers or depolymerization products, including five-membered, six-membered, eight-membered, and macrocyclic rings. Moreover, current limitations, potential applications, and future opportunities of the RCMD approach will be discussed. It is clear from recent research in this field that RCMD represents a powerful strategy towards closed-loop chemical recycling of novel polyolefin materials. 

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations.

 

In order to evaluate urban earthquake resilience, reliable structural modeling is needed. However, detailed modeling of a large number of structures and carrying out time history analyses for sets of ground motions are not practical at an urban scale. Reduced-order surrogate models can expedite numerical simulations while maintaining necessary engineering accuracy. Neural networks have been shown to be a powerful tool for developing surrogate models, which often outperform classical surrogate models in terms of scalability of complex models. Training a reliable deep learning model, however, requires an immense amount of data that contain a rich input-output relationship, which typically cannot be satisfied in practical applications. In this paper, we propose model-informed symbolic neural networks (MiSNN) that can discover the underlying closed-form formulations (differential equations) for a reduced-order surrogate model. The MiSNN will be trained on datasets obtained from dynamic analyses of detailed reinforced concrete special moment frames designed for San Francisco, California, subject to a series of selected ground motions. Training the MiSNN is equivalent to finding the solution to a sparse optimization problem, which is solved by the Adam optimizer. The earthquake ground acceleration and story displacement, velocity, and acceleration time histories will be used to train 1) an integrated SNN, which takes displacement and velocity states and outputs the absolute acceleration response of the structure; and 2) a distributed SNN, which distills the underlying equation of motion for each story. The results show that the MiSNN can reduce computational cost while maintaining high prediction accuracy of building responses. 

Many surveys collect information on discrete characteristics and continuous variables, that is, mixed-type variables. Small-area statistics of interest include means or proportions of the response variables as well as their domain means, which are the mean values at each level of a different categorical variable. However, item nonresponse in survey data increases the complexity of small-area estimation. To address this issue, we propose a multivariate mixed-effects model for mixed-type response variables subject to item nonresponse. We apply this method to two data structures where the data are missing completely at random by design. We use empirical data from two separate studies: a survey of pet owners and a dataset from the National Resources Inventory. In these applications, our proposed method leads to improvements relative to a direct estimator and a predictor based on a univariate model.