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Hydrogen Bonding Inside Anionic Polymeric Brush Layer: Machine Learning-Driven Exploration of the Relative Roles of the Polymer Steric Effect, Charging, and Type of Screening Counterions
This paper employs a combination of all-atom molecular dynamics (MD) simulations and unsupervised machine learning (ML) for studying the water-water hydrogen bonds (HBs) inside the anionic poly-acrylic acid (PAA) brushes modeled using all-atom MD simulations. PAA brush layer with different charge fraction (f), namely f=0, f=0.25, and f=1, is considered. Water-water interactions, both inside and outside the brush layer, are represented through distinct clusters of tupules of variables representing distances associated with the interacting water molecules. While clusters representing the HBs are present for water inside and outside the brushes, several clusters representing the long-range water-water interactions are missing for the water molecules inside the highly charged (f=1) PAA brushes. More importantly, inside highly charged brushes, the edge of the clusters representing the water-water HBs is progressively shortened, as compared to that in the bulk. Both these results stem from the presence of the PAA brushes imparting the steric effect and the charge effect, or the effect associated with enhanced interactions of water molecules with PE charges and counterions, thereby disrupting the water connectivity. This water-charged-species interaction also increases the water-water HB angle, i.e., makes the water-water HBs less stable inside the highly charged PAA brush layer. The narrowing of the clusters representing the HBs and the alteration of the angle characterizing the HBs confirm that the conditions defining the water-water HBs change inside the PAA brush layer as a function of the charges on the PAA brush layer. Furthermore, we show that the use of the generic definition of HBs, as compared to using our simulation-motivated modified definition of water-water HBs, overpredict the number of water-water HBs inside the PAA brush layer. Finally, we employ this all-atom-MD-ML framework to quantify the effect of other types of screening counterions (Li + , Ca 2+ , and Y 3+ ions) in determining the water-water interactions and water-water HB properties inside the PAA brush layer. Furthermore, the findings of the present study, confirming the weakening of water-water HBs inside the PAA brush layer, points to the possibility that the water molecules will be more available for hydrating the brush layer and counterions, thereby leading to a more pronounced wetting of the PAA brush layer.
Combined Machine Learning and Molecular Dynamics Reveal Two States of Hydration of a Single Functional Group of Cationic Polymeric Brushes
The state of hydration of a macromolecular system regulates a plethora of different properties of such a system. In this article, we develop a novel machine learning (ML) approach, based on the unsupervised clustering algorithm, for probing the hydration behavior of the {N(CH 3 ) 3 } + functional group of the PMETAC [Poly(2-(methacryloyloxy)ethyl trimethylammonium chloride] polyelectrolyte (PE) brush system. The PE brushes and the brush-supported water molecules and counterions (chloride ions) are first described using all-atom molecular dynamics (MD) simulations. The simulation data is subsequently used in our ML framework to identify that (1) the {N(CH 3 ) 3 } + functional groups of the PMETAC brushes have two distinct hydration states with one state (state 1) being characterized by less structured water molecules and the other state (state 2) being characterized by more structured water molecules and (2) an enhancement in the brush grafting density leads to the progressive dissapparenace of state 2. An increase in the grafting density increases the number of chloride counterions in a given volume around the {N(CH 3 ) 3 } + functional group and increases the number of shared water molecules between the {N(CH 3 ) 3 } + and Cl - . The chloride counterions are associated with a hydration layer with much less structured water molecules. Therefore, with an increase in the grafting density, an increase in the percentage of shared water molecules leads to the prevalence of the hydration state [of the {N(CH 3 ) 3 } + moiety] with less structured water molecules. Finally, we explain how the present findings are commensurate with two key previous related results, namely a significantly large chloride ion mobility inside the PMETAC brush layer and the {N(CH 3 ) 3 } + -Cl - average distance remaining independent of the PMETAC brush grafting density. Furthermore, we anticipate that the combined ML-MD-simulation approach proposed in this study can be adapted to probe other soft matter systems to reveal new insights of the underlying mechanisms of emergent phenomenon.
Systematic Variation of Fluorinated Diketopyrrolopyrrole Low Bandgap Conjugated Polymers: Synthesis by Direct Arylation Polymerization and Characterization and Performance in Organic Photovoltaics and Organic Field-Effect Transistors
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Dynamics of polydots: Soft luminescent polymeric nanoparticles
The conformation and dynamics of luminescent polymers collapsed into nanoparticles or polydots were studied using fully atomistic molecular dynamics (MD) simulations, providing a first insight into their internal dynamics. Controlling the conformation and dynamics of confined polymers is essential for realization of the full potential of polydots in nanomedicine and biotechnology. Specifically, the shape and internal dynamics of polydots that consist of highly rigid dialkyl p-phenylene ethynylene (PPE) are probed as a function of temperature. At room temperature, the polydots are spherical without any correlations between the aromatic rings on the PPE backbone. With increasing temperature, they expand and become slightly aspherical; however, the polymers remain confined. The coherent dynamic structure factor reveals that the internal motion of the polymer backbone is arrested, and the side chains dominate the internal dynamics of the polydots. Lastly, these new soft nanoparticles retain their overall shape and dynamics over an extended temperature range, and their conformation is tunable via their degree of expansion.
Monitoring Depolymerization in Mesopores Using Dynamic Properties of Polymeric Melt Accessed via Dielectric Spectroscopy
Traditional design principles for heterogeneous catalysis guide the use of catalytic particles with mesosized (∼2–50 nm) pores to increase the number of surface-active sites by way of an increased surface area. However, the entry of long-chain polymers into such pores may be significantly limited by the size and entanglement of polymers in the melt state, thereby decreasing the number of accessible sites. Assessment of catalyst performance from traditional reactor-based studies averages over intrapore reaction events as well as reactions on the surface of a particle, resulting in an inability to distinguish between differences in site accessibility and activity. Techniques that assess the intrapore performance can inform the design of future heterogeneous catalysts for polymer upcycling. In this work, we demonstrate the use of broadband dielectric spectroscopy to monitor depolymerization of a polymer melt within mesopores via changes in the segmental relaxation time scale of amorphous polymer chains. In particular, we highlight the use of an anodic aluminum oxide (AAO) membrane as a readily available model for catalyst pores with a well-characterized pore morphology. The decrease in the segmental relaxation (α-relaxation) time of the melt with increasing chain scission emerges as a measure of the extent of polymer deconstruction inside mesopores. To demonstrate the utility of this technique, we demonstrate the decomposition of two commercial poly(propylene carbonate) polymers with different decomposition rates within mesopores. As the polymers depolymerize, their segmental relaxation time decreases as the molecular weight decreases (as predicted by the Fox–Flory equation). The BDS-measured change in segmental relaxation time mirrors the expected trend based on change in molecular weight measured by size exclusion chromatography.
Selective and Orthogonal Post-Polymerization Modification using Sulfur(VI) Fluoride Exchange (SuFEx) and Copper-Catalyzed Azide–Alkyne Cycloaddition (CuAAC) Reactions
Functional polystyrenes and polyacrylamides, containing combinations of fluorosulfate, aromatic silyl ether, and azide side chains, were used as scaffolds to demonstrate the postpolymerization modification capabilities of sulfur(VI) fluoride exchange (SuFEx) and CuAAC chemistries. Fluorescent dyes bearing appropriate functional groups were sequentially attached to the backbone of the copolymers, quantitatively and selectively addressing their reactive partners. Furthermore, this combined SuFEx and CuAAC approach proved to be robust and versatile, allowing for a rare accomplishment: triple orthogonal functionalization of a copolymer under essentially ambient conditions without protecting groups.
Interaction between Two Active Sites of the Same Catalyst for Macromonomer Enchained Olefin Polymerization
Not provided.
Ion Transport and Interfacial Dynamics in Disordered Block Copolymers of Ammonium-Based Polymerized Ionic Liquids
Abstract not provided
Nitrogen-Defective Polymeric Carbon Nitride Nanolayer Enabled Efficient Electrocatalytic Nitrogen Reduction with High Faradaic Efficiency
Identifying highly selective catalysts and accurately measuring NH 3 yield without false-positives from contaminations remain two challenges in electrochemical nitrogen reduction reaction (NRR). Here, we report N-defective carbon nitride grown on carbon paper (CN/C) as a highly selective electrocatalyst. The NH 3 yield was determined reliably by the slope of mNH 3 -time plot rather than averaging the accumulated amount over time. Results showed the as-synthesized CN/ C 600 (synthesized at 600 °C) with a higher density of C=N-C N2C vacancies achieved an NH 3 production of 2.9 μg mgcat. -1 h -1 at -0.3 V (versus RHE), ~5.7-fold higher than CN/C 500 . The Faradaic efficiency for CN/ C 600 is among the highest of 62.1%, 33.9%, and 16.8% at -0.1 V, -0.2 V, and -0.3 V, respectively. The NH 3 production was verified by isotope 15 N 2 experiments. Further increase of N-defects on CN/C 600 using plasma etching led to higher NH 3 yield than comparably larger current, pointing to N-defects sites for promoting NRR.
Pd–Methyl Bond Energy─Property Correlations, Noncorrelations, Machine Learning Models, and Application to Polymerization Catalysis
Metal–carbon bonds are a key intermediate in a variety of homogeneous organometallic transformations and often determine the critical thermodynamics and kinetics of catalytic processes. Surprisingly, the influence of different ligands on metal–carbon bond strengths has been largely overlooked. Here, in this study, we evaluated nearly 700 experimental Pd–methyl complexes by calculating their bond dissociation energies using density functional theory (DFT) and compared these bond strengths to several fundamental molecular properties, and this revealed several surprising correlations and noncorrelations. Most surprising was that several fundamental properties, such as the bond length, bond force constant, and bond electron density, have no correlation with bond strength, despite these correlations often holding for main-group compounds. We were indeed able to identify key ligand-dependent chemical features/descriptors that provided a highly accurate machine learning model and provided insight into the general factors that control the Pd–carbon bond strength, such as radical delocalization and nucleophilicity. Insights gained from the Pd–Me bond energy analysis were then applied to CO migratory insertion steps that are part of copolymerization reactions.