Polar Interactions Research Articles

Activation of Alkenes via Chalcogen Bond: Chalcogen⋅⋅⋅π Bond Catalyzed the Diels‐Alder Reaction of 2‐Vinylindoles

AbstractChalcogen bond catalysis is a promising catalytic strategy, characterized by its environmental friendliness, relatively inexpensive cost, and similar reactivity to transition metal catalysis. Experimental results suggest that the S⋅⋅⋅π and Se⋅⋅⋅π bonds can efficiently drive the vinyl‐indole‐based Diels‐Alder reaction (Angew. Chem. Int. Ed. 2021, 60, 9395–9400). In this work, chalcogen bond catalysis in the Diels‐Alder reaction between 2‐vinylindoles is investigated based on density functional theory. For this reaction, the Te⋅⋅⋅π bond catalysis is as an alternative catalytic strategy. The Diels‐Alder reaction catalyzed by chalcogen bond is stepwise and involves two steps: the carbon–carbon bond formation process and the cyclization process. The cyclization process is the rate‐determining step. From the perspective of energy decomposition analysis, the electrostatic interaction is the main factor to cause Se⋅⋅⋅π bond catalysis, the polarization interaction is the main factor to cause Te⋅⋅⋅π bond catalysis. Additionally, this catalytic reaction involves the endo pathway and exo pathway. The Gibbs free energy barrier values of the endo pathway are lower than those of the exo pathway, which facilitates the formation of the endo product. This study will provide a valuable perspective on the application of chalcogen bond in the activation of alkenes.

Mineral-associated organic matter is heterogeneous and structured by hydrophobic, charged, and polar interactions

The formation of mineral-associated organic matter (MAOM) is a key phenomenon that may explain the slow turnover rates of carbon in soil organic matter (SOM). Despite this, important details pertaining to the structure and dynamics of MAOM remain unknown. In the present study, we use replica-exchange molecular dynamics simulations to gain insight into the structure of MAOM on the surface of prototypical phyllosilicate clay and Fe-oxide minerals, montmorillonite and goethite, fine-grained minerals that strongly impact soil carbon dynamics in temperate and tropical regions, respectively. We examine the impact of aqueous chemistry through the presence of either Na[Formula: see text] or Ca[Formula: see text] charge balancing counterions. Our results are consistent with the hypothesized multilayer sorption ("onion-skin") model of MAOM and help to explain previous observations regarding the patchy distribution of SOM on mineral surfaces. In particular, the SOM coatings are partial and laterally heterogeneous, and water retains extensive access to mineral surfaces even when significant SOM sorption occurs. Low molecular weight neutral SOM molecules ([Formula: see text]200 Da) infrequently interact with the mineral surfaces nor their sorbed organic matter coatings and are increasingly labile with decreasing molecular weight. This observation is inconsistent with a central feature of the predominant soil continuum model of SOM and suggests that further iterations of the conceptual model may be required.

Discovery of Potent Benzothiazole Inhibitors of Oxidoreductase NQO2, a Target for Inflammation and Cancer

Inhibitors of NQO2 (NRH: quinone oxidoreductase) have potential application in several areas of medicine and pharmacology, including cancer, neurodegeneration (PD and AD), stroke, and diabetes. Here, resveratrol, a known inhibitor of NQO2, was used as the lead by replacing the double bond in resveratrol with a benzothiazole scaffold. Fifty-five benzothiazoles were designed as NQO2 inhibitors and synthesized, comprising five benzothiazole series with 3,5-dimethoxy, 2,4-dimethoxy, 2,5-dimethoxy, 3,4-dimethoxy, and 3,4,5-trimethoxy substituents, the key synthetic step being a Jacobson cyclisation with the appropriate thiobenzamide. All compounds were evaluated in an NQO2 enzyme inhibition assay, with four compounds having IC50 values of <100 nM. The most active (IC50 25 nM) was 6-hydroxy-2-(3’,5’-dihydroxyphenyl)benzo[d]thiazole (15), a good mimetic of resveratrol. Three of the 3’,4’,5’-trimethoxybenzothiazole analogues, with 6-methoxy (40, IC50 51 nM), 6-amino (48, IC50 79 nM), and 6-acetamide (49, IC50 31 nM) substituents, were also potent inhibitors of NQO2. Computational modelling indicated the most active compounds exhibited good shape complementarity and polar interactions with the NQO2 active site. Through the inhibition of NQO2, benzothiazole-based compounds may have the potential to enhance the efficiency of cancer therapies or minimise oxidative damage in neuroinflammation.

Tracking Water Dissociation on RuO2(110) Using Atomic Force Microscopy and First-Principles Simulations.

The interaction between interfacial water and transition metal oxides is a primary enabling step for the oxygen evolution reaction (OER). RuO2 is a prototypical OER electrocatalyst whose ability to activate interfacial water molecules is essential to its OER activity. We image the dissociation of surface water into OH* and O* on RuO2(110), where * denotes adsorbed species, using atomic force microscopy. Starting from the surface-bound water molecules, which form a one-dimensional network along the rows of Ru surface sites, increasing the oxidative potential strips hydrogen away and transforms the water molecules into OH* and O*. This oxidative step changes the pattern of the adsorbates from one- to two-dimensional. First-principles calculations with interfacial polarization, capacitive charging, and adsorbate interactions attribute this evolution to the cooperative dehydrogenation of adsorbed water and OH* on RuO2. We use these results to map the surface phase diagram of RuO2(110) and provide a quantitative interpretation of its cyclic voltammetry. Our result provides the visualization of the water dissociation on a conductive oxide surface, a critical step in the OER, and demonstrates that the water activation is a collective phenomenon at RuO2(110) electrodes.

Identification of the degree of aging and adsorption behaviors of the naturally aged microplastics

Microplastics (MPs) inevitably experienced various aging processes in nature may exhibit varied and complex interfacial interactions with adjacent species. Therefore, clarifying the possible interfacial interactions between naturally aged MPs and organic pollutants is of great significance to assess the actual behaviors of MPs in the environment. Here several plastic packaging materials after use were employed as the raw materials and representatives of naturally aged MPs, the alteration of surface characteristics, especially the degree of aging and the adsorption properties of MPs for anionic and cationic dyes were investigated. The types and the degree of aging of MPs were identified, and the variation of oxygen-containing functional groups (carbonyl, hydroxyl, and ester groups), the hydrophilicity and surface charge character were characterized. The fitting results of kinetics and isotherm models indicated that the adsorption was mainly multi-layer on heterogeneous surfaces, with hydrogen bonding, electrostatic attraction, polar interaction, and hydrophobic partitioning possibly involving. The hydrogen bond interaction was further confirmed by FTIR spectra. The increased temperature promoted the adsorption of cationic dyes on MPs, and the increased salinity of the solution enhanced the uptake of most of the tested dyes by MPs. This research deepened the understanding on the aging degree of MPs and their interfacial interactions with hydrophilic pollutants, and provided vital information for MPs as pollutant carriers.

Extraction of molecular information from experimental data on liquids using manifold learning

This study proposes that microscopic information can be extracted from experimental data on liquid substances using a manifold learning technique. Here, 98 liquid substances could be classified based on their functional groups by applying the isomap method, a manifold learning technique, to nine thermal properties of the substances. Moreover, information related to their intermolecular interactions, which could be divided into the contributions of van der Waals and polar interactions, molecular weight, and number of carbon atoms could be extracted by the proposed method. This study could guide future research on the utilization of manifold learning techniques in liquid-substance characterization and development.

Microenvironment regulation to synthesize sub-3 nm Pt-based high-entropy alloy nanoparticles enabling compressed lattice to boost electrocatalysis

Platinum (Pt) is inevitably employed to facilitate various electrocatalysis including hydrogen oxidation/evolution and oxygen reduction reactions (HOR/HER/ORR), the foundation for renewable energy conversion and storage. Pt-based high-entropy-alloy (HEA) catalysts have been recently presenting promises to improve intrinsic activity of unit mass Pt. However, their current synthesis methods always produce large particles—some can control small size but have to use complicated recipe and/or highly toxic and expensive chemicals. Alternatively, a facile microenvironment regulation strategy is herein proposed to tune solvent polarity and nanoparticle-support interactions within the precursors for carbon-thermal shock pyrolysis. We found that the decreased solvent polarity and enhanced particle-support affinity can jointly control the nanoparticle size ultimately to ~2.68nm with ~10wt % Pt loading. The compressed lattice fringe in such HEA particles leads to electron accumulation at Pt atoms and build a moderate charge density rearrangement, showing superior multifunctional HER/HOR/ORR activity to Pt/C in acidic media.

Computational Analysis of Interactions Between Drugs and Human Serum Albumin.

Drug molecules exist as complexed with serum proteins such as human serum albumin (HSA) and/or unbound free form in the blood circulation. Drugs can be effective only when they are free. Thus, it is important to understand aspects that are important for interaction between drugs and interacting proteins. In this study, interactions among 2990 FDA approved drugs and HSA were computational analyzed to unravel principles that are critical for drug-HSA interactions. Docking results showed that drugs have higher affinity toward cavity-1 (C1) than cavity-2 (C2). A total of 1131 drug molecules have docking score greater than 60 while 768 molecules have docking score greater than 60 when they are docked in C2. In addition, three solvent channels have potential to direct solvent to C1 cavity while C2 does not have any effective channel. The post MD analyses demonstrated that drugs are making polar interactions with basic amino acids in the binding cavities. Verbscoside and ceftazidime both have stable low RMSD values throughout MD simulation with 2 Å on average in C1 cavity. The ligand RMSD shows less stability for verbscoside, which is around 4 Å when it is in complex with HSA in C1. The individual contribution of the residues K192, K196, R215, and R254 to ceftazidime are -1.92 ± 0.18, -3.09 ± 0.09, -2.17 ± 0.17, and - 2.32 ± 0.098, respectively. These residues contribute the binding energy of the verbscoside by -6.06 ± 0.08, -2.10 ± 0.06, and - 1.57 ± 0.03 kcal/mol individually in C1 cavity. C2 is making polar interactions with drug via R469, K472, and K488 residues and their contribution to the two drugs are -3.13 ± 0.21 kcal/mol for R469, -1.94 ± 0.18 kcal/mol for K472, and -1.96 ± 0.11 kcal/mol for K488 to total binding energy of ceftazidime. The binding energy of verbscoside is 57.17 ± 7.00 kcal/mol and Arg-407 has the highest contribution this bind energy individually with -4.29 ± 0.12 kcal/mol. Drugs with hydrogen bond donor/acceptor chemical adducts such as verbscoside involve higher hydrogen bond formation in C1 pocket. Ceftazidime makes interaction with HSA toward hydrophobic residues, L384, L404, L487, and L488 in the C2 cavity.

Computational Docking studies of Phenyl Acetic Acid Derivatives with Biological Targets, DNA, Protein and Enzyme

Phenyl acetic acid (PAA) is a well-known biomolecule used in antibiotics and drugs. To elucidate the binding modes of PAA and its derivatives with DNA, Pim kinase protein, and urease enzymes, molecular docking studies were conducted. The results revealed that PAA and its derivatives intercalate with DNA, disrupting its structure and potentially affecting replication, transcription, and repair processes. The 3-chloro-PAA showed the highest docking score (-7.809) and significant polar interactions with DNA residues. For Pim kinase, the compounds exhibited polar interactions with key residues, with 2-propyl PAA demonstrating notable inhibitory effects. Similarly, PAA derivatives interacted effectively with urease enzymes, with the 4-propyl-PAA showing a strong docking score (-8.5250). Overall, the meta-substituted PAAs exhibited superior binding interactions compared to ortho and para-substituted derivatives, suggesting their potential as effective inhibitors for these biological targets.

Impacting Non-Covalent Interactions through Vibrational Strong Coupling.

Light-matter strong coupling, especially Vibrational Strong Coupling (VSC), has become a significant research focus due to its potential to alter materials' inherent physical and chemical properties. Remarkably, VSC operates even in the absence of light, harnessing subtle quantum fluctuations to influence material characteristics. Vibro-polaritonic states, which are half photonic and half material, are introduced in the molecular/material energy ladder under VSC conditions. Although the underlying mechanism remains elusive, it is proposed that these hybrid states may modify chemical reactivity and other properties by altering factors such as polarity, polarizability, and Van der Waals interactions. This evolving field, vibro-polaritonic chemistry, holds vast potential for deeper exploration, particularly within molecular sciences. This Review examines VSC's observed effects on non-bonding interactions, including hydrogen bonding and π-π interactions, typically governed by dispersive forces.

Effect of Solvent and Cholesterol on Liposome Production by the Reverse-Phase Evaporation (RPE) Method.

Liposomal drug delivery is a promising approach for delivering therapeutics effectively. While most liposomes are designed to be nanometer-sized for efficient cellular uptake, micron-sized liposomes are gaining interest due to their larger drug-loading capacity. When combined with macroscale structures, such as implants and hydrogels, they offer prolonged therapeutic delivery. This study investigates how solvents affect the production of micron-sized liposomes, with or without cholesterol, using the reverse-phase evaporation (RPE) method. Although the RPE method is established for producing micron-sized liposomes, the influence of solvents on liposome size and uniformity is not well understood. The study explores whether controlling the size of inverse micelles, an intermediate product, through the use of different organic solvents affects the final liposome size. Three solvents─diethyl ether, methanol, and acetone─were tested for their effect on the formation of inverse micelles and liposomes and their sizes. Results showed that without cholesterol, diethyl ether produced uniform inverse micelles, leading to mostly nanosized liposomes. Methanol and acetone resulted in phase separation, preventing uniform liposome formation, although some micron-sized liposomes appeared. The acetone sample yielded mostly oil droplets. The results showed that forming inverse micelles lead to nanosized liposomes. With cholesterol, phase separation was dominant in methanol, but micron-sized liposomes still formed. Across all cases, cholesterol reduced the liposome size. The findings reveal that inverse micelles are not always reliable predictors of the final liposome size and that the RPE method is highly sensitive to solvent polarity and lipid-solvent interactions. Overall, the findings of this study provide valuable insights into how the choice of solvent and lipid composition can influence the production of liposomes via the RPE method. These insights are critical for optimizing liposome production and influencing future designs of liposomal drug delivery systems.

Mechanistic insights into β-glucuronidase inhibition by isoprenylated flavonoids from Centaurea scoparia: Bridging experimental and computational approaches

Exploring the intricate mechanisms underlying the inhibition of β-glucuronidase, particularly the enzyme produced by gastrointestinal bacteria, is essential for the development of novel therapeutic interventions and pharmaceutical advancements. Inhibiting bacterial β-glucuronidase can prevent the reactivation of harmful drug metabolites in the gut, thereby reducing associated toxicities. The inhibitory potential of four isoprenylated flavonoids from Centaurea scoparia against β-glucuronidase was intensively investigated by combined in vitro and in silico tools. The results of in vitro inhibitory activity showed that Compounds 1 and 3 exhibited the highest inhibitory activity, as evidenced by their low IC50 values of 3.65 ± 0.28 and 3.97 ± 0.11 μM, respectively. The enzyme kinetics analysis revealed that Compound 1 and the positive control drug (EGCG) follow a mixed inhibition mechanism. In contrast, compounds 3 and 4 exhibited a noncompetitive inhibition mode, as suggested by the intersection patterns observed in the Lineweaver-Burk plots. The docking studies corroborated the in vitro inhibitory assay results, with Compounds 1 and 3 demonstrating the lowest binding affinities and the highest extent of polar and hydrophobic interactions with residues in the enzyme's binding site. Utilizing a 200 ns molecular dynamics (MD) simulation, we examined the interaction dynamics between isolated flavonoids and β-glucuronidase. The analysis of multiple MD parameters revealed that compounds 1 and 3 maintained consistent trajectories and demonstrated significant energy stabilization when interacting with β-glucuronidase. These computational findings align with experimental results, highlighting the potential of compounds 1 and 3 as potent inhibitors for β-glucuronidase.

Molecularly Modified Electrodes for Efficient Triboelectric Nanogenerators

The integration of organic molecules into monolayers on triboelectric layers and electrodes has significantly improved the performance of triboelectric nanogenerators (TENGs) in recent years. By modifying surfaces at the molecular level, one can enhance durability, power density, and cost‐efficiency, leading to flexible, lightweight, and more efficient devices. A simple chemistry of organic monolayer formation allows a precise control over orientation, coverage, consistency, and functionality. These monolayers boost surface charge density and output voltage while influencing surface polarization and dipole interactions. This review focuses on recent advances in chemical modification of electrodes for controlling surface charge density and altering surface dipoles, emphasizing the significance of organic monolayers. A new concept of Schottky‐based TENGs is also introduced that explores chemically modified sliding surfaces. Furthermore, the importance of flexoelectricity and its contribution to triboelectricity is discussed. By addressing current challenges and outlining future directions, this review underscores the crucial role of surface chemistry in advancing TENGs.

Biomimetic Multi-Interface Design of Raspberry-like Absorbent: Gd-doped FeNi3@Covalent Organic Framework Derivatives for Efficient Electromagnetic Attenuation.

Structural design and interface regulation are useful strategies for achieving strong electromagnetic wave absorption (EMWA) and broad effective absorption bandwidth (EAB). Herein, a monomer-mediated strategy is employed to control the growth of covalent organic framework (COF) wrapping flower-shaped Gd-doped FeNi3 (GFN), and a novel raspberry-like absorbent based on biomimetic design is fabricated by thermal catalysis. Further, a unique dielectric-magnetic synergistic system is constructed by utilizing the COF-derived nitrogen-doped porous carbon (NPC) as the shell and anisotropic GFN as the core. The electromagnetic parameters of the GFN@NPC composites can be tuned by adjusting the proportions of GFN and NPC. Off-axis electron holography results further clarify the interface polarization and microscale magnetic interactions affecting the EMW loss mechanism. As a result, the GFN@NPC samples exhibit broad EMWA performance. The EAB values of all GFN@NPC composites reach up to 6.0 GHz, with the GFN@NPC-2 sample showing a minimum reflection loss (RLmin) of -69.6 dB at 1.68 mm. In addition, GFN@NPC-2 achieves a maximum radar cross-section (RCS) reduction of 29.75 dB·m2. A multi-layer gradient structure is also constructed using metamaterial simulation to achieve an ultra-wide EAB of 12.24 GHz. Overall, this work provides a novel bio-inspired design strategy to develop high-performance EMWA materials.

Dilational rheological properties of extended anionic surfactants at decane-water interface

The dynamic interfacial dilational properties of extended surfactants octadecyl-(polypropylene oxide)m-(polyethylene oxide)n-carboxylic sodium (C18POmEOnC) with different numbers of polypropylene oxide (PO) and polyethylene oxide (EO) groups at the decane–water interface were investigated by means of oscillating drop method. The influences of interface ageing time, dilational frequency, bulk concentration and interfacial pressure on the dilational properties were investigated. The experimental results demonstrate that, at low concentrations, the adsorption film is predominantly influenced by diffusion exchange, while intermolecular interactions at the interface play a more significant role at high concentrations. Beyond the critical micelle concentration (CMC), the primary relaxation process in the adsorption film is driven by the formation of micellar structures. Additionally, the weak hydrophobic PO groups exhibit compressibility and extensibility, facilitating the formation of spring-like helical structures at the decane–water interface. As a result, the dilational modulus increases and the phase angle decreases in low concentration with the increase of PO number. With increasing concentration, the weak hydrophilic EO chains gradually transition from a partially flat state to an upright orientation toward the aqueous phase, forming sublayer structures through polar interactions. At this stage, the increase in EO group chain length has a more pronounced impact on the elastic modulus compared to PO groups. These experimental findings suggest the existence of a mechanistic model for the extended surfactant adsorption films.

Development of deep learning software to improve HPLC and GC predictions using a new crown-ether based mesogenic stationary phase and beyond

The application of AI to analytical and separative sciences is a recent challenge that offers new perspectives in terms of data prediction. In this work, we report an AI-based software, named Chrompredict 1.0, which based on chromatographic data of a novel mesogenic crown ether stationary phase (CESP). Its molecular design represents a significant advancement due to the unique combination of properties and binding capabilities, including the formation of a cavity, mesogenic behavior via mobile chains, and a range of polar and non-polar interactions (aromatic rings, N=N and O=O double bonds, alkyl chains, π–π interactions, and hydrogen bonding). The mesogenic phase is effective in both normal and reversed-phase chromatography, enhancing the software's adaptability across diverse datasets.Here we introduce for the first time an unprecedented scientific approach, integrating deep learning techniques with the novel CESP, which demonstrates exceptional thermal and analytical performance in both liquid chromatography modes, especially in the separation of complex hydrocarbon isomers. This ability enables the results obtained with CESP to extend across various types of stationary phases.Leveraging these insights, a comprehensive chromatographic dataset on a series of aromatic and polyaromatic molecules interacting with our CESP was used to train a Deep Learning Model (DLM). This model is embedded within a user-friendly software, Chrompredict 1.0, designed for predicting chromatographic parameters (MAE = 0.042, R² = 0.95) by selecting chemical descriptors directly from SMILES notation. It offers a deeper understanding of molecular structure and interactions through exploratory data analysis, identifying key factors affecting model accuracy and chromatographic behavior.Users can configure hyperparameters, choose from six machine learning models, and compare their performance with DLM. Chrompredict 1.0 excels in retention behavior prediction for compounds with known structures, and it accurately predicts chromatographic retention and thermal characteristics for different temperatures in HPLC and GC. The model has been successfully tested with METLIN database of 1,023 small molecules of diverse structures and polarities (R² > 0.75, error range ±7.8 s). Overall, the CESP, combined with Chrompredict 1.0, offers a robust tool for intelligent chromatographic analysis, encompassing chemo-informatics, statistical analysis, and graphical capabilities across a broad range of compounds and stationary phases.

Exploring the mechanisms of benzanilide crystal growth and morphology: Crystal surface structure, solvent diffusion and solvent-interface interactions

Flaky crystals are fragile, rendering them unsuitable for various applications including use, processing, storage and transportation. This work investigates the growth and morphology regulation mechanisms of flaky crystals using benzanilide as a model material. Initially, block-like crystals are prepared by solvent screening and cooling crystallization with its morphology greatly improved. Subsequently, growth kinetics of (111), (1–10) and (020) faces are established with crystal growth experiments. The crystal growth of (001) face is also determined to follow a two-dimensional nucleation model with the microscopic surface analysis using atomic force microscopy. The changes of the surface integration resistance and the growth step height are regarded as kinetic mechanisms of the growth behavior and crystal morphology modulation by solvent and supersaturation. Finally, the molecular mechanism of the modulation of the solvent in crystal morphology is revealed base on crystal surface structure analysis and molecular dynamics simulations. The weak solvent-interface polar interactions facilitate the incorporation of benzanilide molecules into the weak polarity (001) face, leading to the surface roughening and increased crystal thickness when employing strongly polar solvents such as acetonitrile. Unlike the former, the relatively weak hydrogen bonding interactions between solvents and strong polarity (020) face, and the higher diffusion ability of molecules promote the rapid growth and disappearance of (020) face in ethanol and acetonitrile with strong hydrogen bond formation ability. This study can contribute to a deeper understanding of the solvent effect on crystal morphology and provide guidance for optimizing crystallization conditions to obtain shape-tailored crystal products.

AlzDiscovery: A computational tool to identify Alzheimer's disease-causing missense mutations using protein structure information.

Alzheimer's disease (AD) is one of the most common forms of dementia and neurodegenerative diseases, characterized by the formation of neuritic plaques and neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, and missense mutations can alter the folding and functions of these proteins, significantly increasing the risk of AD. However, many methods to identify AD-causing variants did not consider the effect of mutations from the perspective of a protein three-dimensional environment. Here, we present a machine learning-based analysis to classify the AD-causing mutations from their benign counterparts in 21 AD-related proteins leveraging both sequence- and structure-based features. Using computational tools to estimate the effect of mutations on protein stability, we first observed a bias of the pathogenic mutations with significant destabilizing effects on family AD-related proteins. Combining this insight, we built a generic predictive model, and improved the performance by tuning the sample weights in the training process. Our final model achieved the performance on area under the receiver operating characteristic curve up to 0.95 in the blind test and 0.70 in an independent clinical validation, outperforming all the state-of-the-art methods. Feature interpretation indicated that the hydrophobic environment and polar interaction contacts were crucial to the decision on pathogenic phenotypes of missense mutations. Finally, we presented a user-friendly web server, AlzDiscovery, for researchers to browse the predicted phenotypes of all possible missense mutations on these 21 AD-related proteins. Our study will be a valuable resource for AD screening and the development of personalized treatment.

Molecule-Level Multiscale Design of Nonflammable Gel Polymer Electrolyte to Build Stable SEI/CEI for Lithium Metal Battery

HighlightsNonflammable gel polymer electrolyte (SGPE) is developed by in situ polymerizing trifluoroethyl methacrylate (TFMA) monomers with flame-retardant triethyl phosphate (TEP) solvents and LiTFSI–LiDFOB dual lithium salts.Molecular polarity interaction between TEP and PTFMA mitigates interfacial reactions and changes the solvation of Li+.SGPE forms stable inorganic-rich solid electrolyte interface/cathode electrolyte interface layer, exhibiting well compatibility with Li anode and LiCoO2-type high-voltage cathode.

From Bulk to Binding: Decoding the Entry of PET into Hydrolase Binding Pockets.

Plastic-degrading enzymes facilitate the biocatalytic recycling of poly(ethylene terephthalate) (PET), a significant synthetic polymer, and substantial progress has been made in utilizing PET hydrolases for industrial applications. To fully exploit the potential of these enzymes, a deeper mechanistic understanding followed by targeted protein engineering is essential. Through advanced molecular dynamics simulations and free energy analysis methods, we elucidated the complete pathway from the initial binding of two PET hydrolases-the thermophilic leaf-branch compost cutinase (LCC) and polyester hydrolase 1 (PES-H1)-to an amorphous PET substrate, ultimately leading to a PET chain entering the active site in a hydrolyzable conformation. Our findings indicate that initial PET binding is nonspecific and driven by polar and hydrophobic interactions. We demonstrate that the subsequent entry of PET into the active site can occur via one of three key pathways, identifying barriers related to both PET-PET and PET-enzyme interactions, as well as specific residues highlighted through in silico and in vitro mutagenesis. These insights not only enhance our understanding of the mechanisms underlying PET degradation and facilitate the development of targeted enzyme enhancement strategies but also provide a novel framework applicable to enzyme studies across various disciplines.