Tunable solid-state fluorescent materials for supramolecular encryption

Tunable solid-state fluorescent materials are ideal for applications in security printing technologies. A document possesses a high level of security if its encrypted information can be authenticated without being decoded, while also being resistant to counterfeiting. Herein, we describe a heterorotaxane with tunable solid-state fluorescent emissions enabled through reversible manipulation of its aggregation by supramolecular encapsulation. The dynamic nature of this fluorescent material is based on a complex set of equilibria, whose fluorescence output depends non-linearly on the chemical inputs and the composition of the paper. By applying this system in fluorescent security inks, the information encoded in polychromic images can be protected in such a way that it is close to impossible to reverse engineer, as well as being easy to verify. This system constitutes a unique application of responsive complex equilibria in the form of a cryptographic algorithm that protects valuable information printed using tunable solid-state fluorescent materials

Single-Crystalline Hydrogen-Bonded Crosslinked Organic Frameworks and Their Dynamic Guest Sorption

Porous organic framework materials constructed by periodically aligned molecular entities offer chemically tailored microenvironments to absorb molecules and ions for various applications. Fundamentally understanding the microenvironments of these porous organic materials─from pore size, shape, and dynamics to potential substrate binding sites─is critical for the rational design of porous organic materials. The solid-state structures of these porous organic materials, such as covalent organic frameworks (COFs), can provide unambiguous atomic-level structural details. However, it remains challenging to synthesize these materials as single crystals that can be fully characterized by single-crystal X-ray diffraction (SCXRD) or rotational electron diffraction (RED) analysis. In addition, the balance of single crystallinity, permanent porosity, and good chemical stability requires delicate control of the assembly of the molecular building blocks and covalent crosslinking during synthesis. In this Account, we discuss the development of hydrogen-bonded crosslinked organic frameworks (HCOFs) possessing balanced single crystallinity and high chemical stability. HCOFs are obtained through covalently crosslinking molecular crystals that are preorganized via hydrogen bonding. Due to the dual hydrogen-bonded network and covalent crosslinking, HCOFs can deform upon guest adsorption by breaking the hydrogen bonds and subsequently restore their original form through the desorption of guests by re-establishing the hydrogen-bonded networks. Thus, HCOFs can dynamically adjust their pore sizes according to the framework–substrate interactions. In the discussion, we link HCOFs with COFs and single-crystalline 2D polymers by comparing their synthetic approaches to accessing high crystallinity. The method to synthesize HCOFs allows for the employment of various flexible building blocks and linking motifs that are largely avoided in the current design regimes of COFs and 2D polymers. We also draw the connections between HCOFs and hydrogen-bonded organic frameworks (HOFs) by highlighting their shared design principles for constructing hydrogen-bonding networks with large voids. Compared to their hydrogen-bonded precursor crystals, reinforcing the hydrogen-bonded networks with covalent linkages endows HCOFs with enhanced chemical and structural stability. In addition, we emphasize that the structure elucidation of HCOFs often requires combined SCXRD analysis and experimental evidence, with the methods and challenges thoroughly discussed. The details are presented in the following sequence: (1) synthesizing single-crystalline COFs by matching the polycondensation rate to the nucleation rate and their subsequent analyses by SCXRD/RED; (2) obtaining single-crystalline polymers and networks through topochemical reactions; (3) constructing HOFs with designed voids using highly directional hydrogen bonding building blocks; and (4) developing HCOFs via monomer crystal engineering followed by single-crystal to single-crystal (SCSC) synthesis and studying their unique dynamic guest sorption behaviors. We hope this Account will inspire researchers to expand the synthetic methods for advancing HCOFs with detailed solid-state structures, as well as designing porous organic framework materials with dynamic sorption capabilities to enhance their performance for applications in molecular storage, separation, catalysis, etc

Advanced Polymer Designs for Direct‐Ink‐Write 3D Printing

The rapid development of additive manufacturing techniques, also known as three-dimensional (3D) printing, is driving innovations in polymer chemistry, materials science, and engineering. Among current 3D printing techniques, direct ink writing (DIW) employs viscoelastic materials as inks, which are capable of constructing sophisticated 3D architectures at ambient conditions. In this perspective, polymer designs that meet the rheological requirements for direct ink writing are outlined and successful examples are summarized, which include the development of polymer micelles, co-assembled hydrogels, supramolecularly cross-linked systems, polymer liquids with microcrystalline domains, and hydrogels with dynamic covalent cross-links. Furthermore, advanced polymer designs that reinforce the mechanical properties of these 3D printing materials, as well as the integration of functional moieties to these materials are discussed to inspire new polymer designs for direct ink writing and broadly 3D printing

An Integrated Design of a Polypseudorotaxane‐Based Sea Cucumber Mimic

The development of integrated systems that mimic the multi-stage stiffness change of marine animals such as the sea cucumber requires the design of molecularly tailored structures. Herein, we used an integrated biomimicry design to fabricate a sea cucumber mimic using sidechain polypseudorotaxanes with tunable nano-to-macroscale properties. A series of polyethylene glycol (PEG)-based sidechain copolymers were synthesized to form sidechain polypseudorotaxanes with α-cyclodextrins (α-CDs). By tailoring the copolymers’ molecular weights and their PEG grafting densities, we rationally tuned the sizes of the formed polypseudorotaxanes crystalline domain and the physical crosslinking density of the hydrogels, which facilitated 3D printing and the mechanical adaptability to these hydrogels. After 3D printing and photo-crosslinking, the obtained hydrogels exhibited large tensile strain and broad elastic-to-plastic variations upon α-CD (de)threading. These discoveries enabled a successful fabrication of a sea cucumber mimic, demonstrating multi-stage stiffness changes

A Crosslinked Ionic Organic Framework for Efficient Iodine and Iodide Remediation in Water

Iodine is widely used as an antimicrobial reagent for water disinfection in the wilderness and outer space, but residual iodine and iodide need to be removed for health reasons. Currently, it is challenging to remove low concentrations of iodine and iodide in water (~5 ppm). Furthermore, the remediation of iodine and iodide across a broad temperature range (up to 90 °C) has not previously been investigated. In this work, we report a nitrate dimer-directed synthesis of a single-crystalline ionic hydrogen-bonded crosslinked organic framework (HCOF-7). HCOF-7 removes iodine and iodide species in water efficiently through halogen bonding and anion exchange, reducing the total iodine concentration to 0.22 ppm at room temperature. Packed HCOF-7 columns were employed for iodine/iodide breakthrough experiments between 23 and 90 °C, and large breakthrough volumes were recorded (≥18.3 L/g). The high iodine/iodide removal benchmarks recorded under practical conditions make HCOF-7 a promising adsorbent for water treatment.This is the peer-reviewed version of the following article: Zhang, Mingshi, Jayanta Samanta, Benjamin Atterberry, Richard Staples, Aaron J. Rossini, and Chenfeng Ke. "A Crosslinked Ionic Organic Framework for Efficient Iodine and Iodide Remediation in Water." Angewandte Chemie International Edition (2022). It has been published in final form at DOI: 10.1002/anie.202214189. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Copyright 2022 Wiley-VCH. Posted with permission. DOE Contract Number(s): AC02-07CH11358; DMR-1844920; 191956

An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors

Time history testing using a shaking table is one of the most widely used methods for assessing the dynamic response of structures. In shaking-table experiments and on-site monitoring, acceleration sensors are facing problems of missing data due to the fact of measurement point failures, affecting the validity and accuracy of assessing the structural dynamic response. The original measured signals are decomposed by ensemble empirical mode decomposition (EEMD), and the widely used deep neural networks (DNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTMs) are used to predict the subseries of the decomposed original measured signal data to help model and recover the irregular, periodic variations in the measured signal data. The raw acceleration data of a liquefied natural gas (LNG) storage tank in shaking-table experiments were used as an example to compare and discuss the method’s performance for the complementation of missing measured signal data. The results of the measured signal data recovery showed that the hybrid method (EEMD based) proposed in this paper had a higher complementary performance compared with the traditional deep learning methods, while the EEMD-LSTM exhibited the best missing data complementary accuracy among all models. In addition, the effect of the number of prediction steps on the prediction accuracy of the EEMD-LSTM model is also discussed. This study not only provides a method to fuse EEMD and deep learning models to predict measured signal’ missing data but also provides suggestions for the use of EEMD-LSTM models under different conditions