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At least 253 records · Page 14

Quantitative SANS and multi-model analysis of spacer-dependent micellization of urea-based gemini surfactants

The micellization behavior of urea-based cationic gemini surfactants was investigated using small-angle neutron scattering (SANS) with multi-model form factor analysis. A homologous series of surfactants with urea group included in the hydrophobic tail and polymethylene spacers consisting of two to ten methylene units was analyzed using three form factor models: a core–shell ellipsoid and two variants of homogeneous ellipsoids. The results from all models show a consistent trend of the micelle structures, confirming that the spacer length critically influences micellar geometry, aggregation number, and hydration. The surfactant with four CH 2 groups in the spacer formed the largest micelles with the highest aggregation number, while longer spacers led to progressively smaller, more compact aggregates. The shell hydration—quantified as the volume fraction of heavy water within the hydrophilic region—decreased systematically with increasing spacer length due to enhanced hydrophobicity of the headgroup-spacer region. Intermicellar interactions, modeled as screened Coulomb interaction using the rescaled mean spherical approximation (RMSA), revealed the strongest electrostatic repulsion for the case of four methylene groups in the spacer, corresponding to the highest micellar charge and largest interparticle spacing. The observed spacer-dependent trends were robust across all modeling approaches, demonstrating that the spacer length serves as a key structural determinant of self-assembly in this type of urea-based gemini systems. These findings provide insight into the design of gemini surfactants with tailored aggregation behavior for applications in drug delivery, nanostructure templating, and solubilization technologies.

Core–shell ellipsoid model↗

Multi-Source Data Aggregation and Real-Time Anomaly Classification and Localization in Power Distribution Systems

This paper proposes a real-time anomaly location and classification framework for power distribution systems to simultaneously determine the type of anomaly (i.e., short-circuit fault, cyber attack, DER switching) and its location. The proposed framework employs the data aggregation module to collect the measurement data from multiple field devices operating at different sampling rates, such as protection relays and D-PMUs. The output of the data aggregation is then fed into a multi-task learning-based long-based short-term memory (MTL-LSTM) to classify the type of anomaly and the location in two separate tasks. The proposed MTL-LSTM approach can be utilized in real-time operation in order to distinguish between normal and several anomalous operations and locate the anomaly. The proposed framework is tested on a modified IEEE 33-bus test feeder benchmark that integrates solar generation and energy storage. Furthermore, the results show that the proposed framework can locate and classify anomalies for several operation conditions with more than 96% accuracy. Further experiments highlight the impact of aggregating multiple sources of data on the performance of the proposed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Polyethylene Glycol Crowder’s Effect on Enzyme Aggregation, Thermal Stability, and Residual Catalytic Activity

Protein stability and performance in various natural and artificial systems incorporating many other macromolecules for therapeutic, diagnostic, sensor, and biotechnological applications attract increasing interest with the expansion of these technologies. Here we address the catalytic activity of lysozyme protein (LYZ) in the presence of a polyethylene glycol (PEG) crowder in a broad range of concentrations and temperatures in aqueous solutions of two different molecular mass PEG samples (Mw = 3350 and 10000 g/mol). The phase behavior of PEG–protein solutions is examined by using dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS), while the enzyme denaturing is monitored by using an activity assay (AS) and circular dichroism (CD) spectroscopy. Molecular dynamic (MD) simulations are used to illustrate the effect of PEG concentration on protein stability at high temperatures. The results demonstrate that LYZ residual activity after 1 h incubation at 80 °C is improved from 15% up to 55% with the addition of PEG. The improvement is attributed to two underlying mechanisms. (i) Primarily, the stabilizing effect is due to the suppression of the enzyme aggregation because of the stronger PEG–protein interactions caused by the increased hydrophobicity of PEG and lysozyme at elevated temperatures. (ii) The MD simulations showed that the addition of PEG to some degree stabilizes the secondary structures of the enzyme by delaying unfolding at elevated temperatures. The more pronounced effect is observed with an increase in PEG concentration. This trend is consistent with CD and AS experimental results, where the thermal stability is strengthened with increasing of PEG concentration and molecular mass. Overall, the results show that the highest stabilizing effect is approached at the critical overlap concentration of PEG.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Enabling Grid-Aware Market Participation of Aggregate Flexible Resources

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. With the new FERC 2222 order, DER aggregations are allowed to participate in energy market. To enable market participation, these virtual power plants need to provide their generation cost curves. This paper proposes efficient optimization formulations and solution approaches for the characterization of hourly as well as multi-time-step generation cost curves for a distribution system with high penetration of DERs. Network and DER constraints are taken into account when deriving these cost curves, and they enable active distribution systems to bid into the electricity market. The problems of deriving linear and quadratic cost curves are formulated as robust optimization problems and tractable reformulation/solution algorithm are developed to facilitate efficient calculations. The proposed formulations and solution algorithm are validated on a realistic test feeder with high penetration of flexible resources.

aggregated distributed energy resources↗

Ionic Liquid Facilitated Solvent‐Phase Polymerization of Ultrasmooth Coatings of Polycatecholamines

Mussel-inspired polycatecholamine coatings and films have proven to be versatile materials for surface modification. However, the progress of chemically modified coatings with tailored properties has been slow due to the lack of definitive evidence of their polymeric structure. Polycatecholamines form aggregates during polymerization, and their formation mechanism has been a subject of debate owing to their insolubility in traditional molecular solvents. Exploiting the capability of ionic liquids (ILs) to exhibit strong electrostatic interactions, herein, the first solution-phase polymerization approach is reported to achieve polymerization of four catecholamines – dopamine (DA), norepinephrine (NE), L-3,4-dihydroxyphenylalanine (L-DOPA), and adrenaline (AD). Kinetic control over the polymerization process in the solution phase enabled it to follow the process with 1 H NMR spectroscopy, revealing the formation of structural features of resulting complex heterogeneous and soluble polymers. The polymers are both covalent and associative in nature. Solution phase polymerization prevented the formation of large aggregates in the solution, thus facilitating the formation of coatings that are ultra-smooth and have root mean square roughness of the order of sub-nm to nm scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NeuralCubes: Deep Representations for Visual Data Exploration

Visual exploration of large multi-dimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are some of the most promising approaches. However, these techniques usually require a large memory footprint for large datasets. To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes. NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Our models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network connection. Here, we demonstrate the effectiveness of NeuralCubes through extensive experiments on a variety of datasets and discuss how NeuralCubes opens up opportunities for new types of visualization and interaction.

97 MATHEMATICS AND COMPUTING↗

Overcoming the Technical Challenges of Coordinating Distributed Load Resources at Scale (Final Report)

Significant recent research has investigated the potential for loads to provide balancing services to the grid. However, this research has not addressed key issues that may arise when such schemes are applied at scale including: 1. Distribution Network Issues. Coordination of large numbers of loads could result in power flows that violate distribution network constraints; 2. Stability Issues. Certain strategies to control loads can exhibit nonlinearity in the form of period-adding bifurcations and chaos. Other control strategies can potentially synchronize the behavior of large numbers of loads. In both cases, the outcome can be power oscillations and instability; 3. Communication Network Issues. Bidirectional low-latency communication channels between a central controller (or several distributed controllers) and each resource are expensive and likely not necessary for effective coordination. Our research questions were: What network, stability, and communication issues might arise in practice when we coordinate large aggregations of loads? How can we coordinate loads to achieve performance objectives in a cost effective manner while avoiding these issues? The ultimate technical goal of the project was the development of network-aware, communication-constrained, non-disruptive load control strategies with stability guarantees that achieve the performance requirements of typical balancing services at a sufficiently low cost to enable the load aggregator and customer to profit. The overall goal was to establish credibility for load control at scale and contribute to U.S. energy security and environmental goals. The team succeeded in answering these research questions and developing these control strategies. The overall approach was based on the development of three testing environments: a simulation testbed, an experimental testbed (20 physical model houses with window-box air conditioners) coupled with the simulation testbed, and a field testbed (100 actual homes in Austin, TX) coupled with the simulation testbed, which enabled controller testing, identification of issues, controller development, and controller validation. The resulting controller was used to demonstrate fast timescale grid balancing (frequency regulation) by aggregations of physical and virtual air conditioners, with sufficient quality to participate in the electricity market. Cost benefit analysis showed overall benefits to the participating households, load aggregators, and the grid, especially if the control technology was integrated directly into existing programmable communicating thermostats. The project provides a variety of wider benefits. Our technology transfer and outreach activities lead us to choose an open-source licensing commercialization pathway, enabling the project results to be available to researchers, industry, and the public. Furthermore, new grid balancing technologies will increase grid flexibility and will enable higher penetrations of intermittent renewable energy resources, such as wind and solar, to be connected to the grid, reducing its environmental impact, and mitigating climate change to the benefit of society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Tuning Organic Semiconductor Packing and Morphology through Non-equilibrium Solution Processing (Final Report)

Organic semiconductors (OSCs) are a promising candidate to produce low-cost, large area, and flexible electronics. There has previously been successful development of organic field effect transistors (OFETs), photovoltaics (OPV), and bioelectronics using OSCs. Solution processing of these materials allows for the fabrication of large-area devices in the kinetic crystallization regime. The charge transport capabilities have been shown to depend on the morphology and molecular packing of the OSC within thin films. Our hypothesis is that solution processing conditions may significantly impact OSC morphology and as a result the charge transport. Therefore, this work has focused on obtaining a fundamental understanding of the morphology and molecular packing of OSCs for optimal device performance. Through our work, we have gained a better understanding of how these structural conditions were influenced by the solution processing conditions. Our studies have led to a more systematic understanding of the various parameters that impact OSC morphology. Solution processing leads to non-equilibrium films, thus allowing for the formation of diverse morphologies that are inaccessible by other fabrication methods. Previously, our group has focused on tuning the morphology of small-molecular OSCs [53, 55-57]. However, little work had been devoted to polymer OSCs. There was a lack of detailed studies characterizing the solution-state of polymer OSCs in terms of their conformation, degree of entanglement, polymer aggregation, and chain relaxation dynamics. The solution state properties are also likely to be influenced by the rigidity and molecular weight of the polymer OSCs investigated. Thus, we have focused our attention on gaining a better insight of polymer OSC films and solution processing methods. Through this proposal, our approach is to investigate the correlation between solution-state properties and the morphology of the resulting polymer OSC films. We worked three specific aims: investigate the effects of 1) polymer OSC solution-state properties on final film morphology; 2) molecular additives on the solution-state and final film properties; 3) controlled pre-aggregation in the solution-state in the final film morphology. More rigid and planar polymer backbones should promote interchain charge transport and more efficient interchain hopping between polymer chains. Through tailoring the polymer backbone, polymer sidechains, and molecular weight, we expected the altered solution-state properties to affect the final film morphology. In addition, reducing the entanglements and promoting chain alignment will likely prevent charge carrier trapping through conformation disorders. We thus studied different mechanisms, such as molecular additives, to reduce entanglements in solution. Devices fabricated from these solutions were expected to have improved charge transport abilities. In addition to tailoring the solution-state characteristics of the polymer OSCs, we investigated the effect of solution processing methods on the molecular packing and morphology of polymer OSC films. Two main solution processing techniques, e.g., spin-coating and solution shearing, were employed to fabricate OFETs. Spin coating was employed to prepare OSC films, which produce isotropic films. This technique creates several parameters to tune such as spin coating speed, acceleration, and time. Additionally, our research group developed the solution shearing method, which consists of the solution initially sandwiched between two plates. By sliding the top plate, the solution front is exposed and drying begins. This technique allows for the creation of aligned large crystalline domains. Solution shearing consists of different processing parameters which can affect the final morphology, such as shearing speed, substrate temperature and temperature gradient, distance between both plates, and the tilt angle of the top plate. Due to the complexity of the polymer systems investigated, numerous techniques were employed to characterize the polymer OSC solution state and films. All the materials were characterized using the DOE supported synchrotron X-ray scattering facilities at the Stanford Synchrotron Radiation Lightsource (SSRL). Using grazing incidence X-ray scattering (GIXS) and Near Edge X-ray Absorption Fine Structure (NEXAFS) techniques, the crystalline structure and molecular orientations of the thin films were measured. Optical absorption (UV-Vis) spectroscopy was employed to determine the aggregation state in solution and films of the polymer systems. Polarized UV-Vis also allowed for the determination of the relative degree of polymer chain alignment for solution sheared films. Additionally, various other techniques were used to investigate other properties within the film, such as atomic force microscopy (AFM) and solution rheology. Finally, the device performance is quantified through the fabrication and characterization of OFETs, which will highlight the effects of morphology on charge transport.

36 MATERIALS SCIENCE↗

Impact of Transition Metal Doping on the Structural and Optical Properties of Halide Perovskites

Metal ion doping into halide perovskite materials is a promising approach to enhance long-term film stability and improve solar cell performance. Moreover, the idea of doping metals into halide perovskites is also driven by applications in heterogeneous catalysis. Here, we investigate the incorporation of the transition metals Pt and Pd into mixed-halide perovskite materials. We find that Pt aggregates in large clusters in iodine- and bromine-rich perovskite film compositions, whereas Pd incorporation depends on the underlying bromine to iodine ratio. Furthermore, by using optical and structural characterization methods, we observe that phase segregation in the perovskite layer is suppressed upon doping of either of these metals into the material. This approach further shows the potential to obtain catalytically active halide perovskite films in which the perovskite can serve as a host matrix.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Anion chemical composition of poly(ethylene oxide)-based sulfonylimide and sulfonate lithium ionomers controls ion aggregation and conduction

Maximizing ion conduction in single-ion-conducting ionomers is essential for their application in energy-related technologies such as Li-ion batteries. Understanding the anion chemical composition impacts on ion conduction offers new perspectives to maximize ion transport, since the current approach of lowering T g has apparently reached a limit (lowest T g ~ 190 K, highest conductivity ~10 -5 –10 -4 S cm -1 ). Here, a series of random ionomers are synthesized by copolymerizing poly(ethylene glycol)methacrylate with either sulfonylimide lithium methacrylate (MTLi) or sulfonate lithium methacrylate (MSLi) using reversible addition–fragmentation chain transfer (RAFT) polymerization. Li-Ion conduction and self-diffusion coefficients (DLi+) of the ionomers are characterized with dielectric relaxation spectroscopy (DRS) and pulsed-field-gradient (PFG) NMR diffusometry, respectively. Increasing ion content decreases the Li-ion conductivity and D Li+ , as expected from the increased T g . Moreover, a considerably lower ionic conductivity and D Li+ are observed for MSLi compared to MTLi at constant ion content and T g /T. As revealed from X-ray scattering, strong ion aggregation in MSLi results in much lower conductivity and DLi+ compared with less aggregated MTLi based on the more delocalized sulfonylimide anion. These results emphasize the detrimental and molecularly specific role of ion aggregation in Li-ion conductivity, and highlight the necessity for minimizing ion aggregation via the rational choice of anion chemical composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up

The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of ground-based GW detectors. Within this context, the radio band of the EM spectrum presents unique challenges. Sensitive radio facilities capable of detecting the faint radio afterglow seen in GW170817, and with sufficient angular resolution, have small fields of view compared to typical GW localization areas. Additionally, theoretical models predict that the radio emission from binary neutron star mergers can evolve over weeks to years, necessitating long-term monitoring to probe the physics of the various postmerger ejecta components. These constraints, combined with limited radio observing resources, make the development of more coordinated follow-up strategies essential—especially as the next generation of GW detectors promises a dramatic increase in detection rates. Here, we present RADAR, a framework designed to address these challenges by promoting community-driven information sharing, federated data analysis, and system resilience, while integrating AI methods for both GW signal identification and radio data aggregation. We show that it is possible to preserve data rights while sharing models that can help design and/or update follow-up strategies. We demonstrate our approach through a case study of GW170817, and discuss future directions for refinement and broader application.

Gravitational waves↗

Improving parameterization of ice microphysical processes in Arctic clouds using a synergistic modeling and observational approach (Final Report)

The primary goals of the project were to: 1) Improve the fundamental quantitative models of ice growth processes using ARM radar observations. 2) Better understand the growth of ice particle populations through vapor deposition, riming, and aggregation in Arctic clouds using a new particle-property microphysical model. 3) Robustly estimate and constrain key unknown model parameters using ARM radar observations combined with the particle-property model to develop an improved parameterization of ice growth processes for traditional bulk parameterizations used in cloud and climate models.

58 GEOSCIENCES↗

A meta-learning based distribution system load forecasting model selection framework

This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. Furthermore, the framework includes the following processes: feature extraction, candidate model preparation and labeling, offline training, and online model recommendation. Using load forecasting needs and data characteristics as input features, multiple metalearners are used to rank the candidate load forecast models based on their forecasting accuracy. Then, a scoring-voting mechanism is proposed to weights recommendations from each meta-leaner and make the final recommendations. Heterogeneous load forecasting tasks with different temporal and technical requirements at different load aggregation levels are set up to train, validate, and test the performance of the proposed framework. Simulation results demonstrate that the performance of the meta-learning based approach is satisfactory in both seen and unseen forecasting tasks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Estimating Sediment Settling Velocities from a Theoretically Guided Data-Driven Approach

Sediment settling velocities are commonly estimated from analytical or process-based approaches. These approaches have theoretical constraints due to the incompletely resolved settling physics. A parametric data-driven approach was recently proposed without theoretical constraints, but it is limited by its mathematical assumptions. To overcome these limitations, here we apply a machine learning algorithm to an aggregated sediment settling experimental database and develops a nonparametric data-driven model to estimate the noncohesive sediment settling velocity in water. A cross-comparison against five process-based equations and a parametric data-driven equation demonstrates the higher accuracy and better consistency of the new model in estimating sediment settling velocities under various physical regimes. The new model also shows an easily implemented self-update capability by assimilating theoretical data derived from the process-based equations. The updated model, incorporating experimental and theoretical data of sediment settling processes, further improves the accuracy and reduces the uncertainty in estimating sediment settling velocities. This study demonstrates the capability of machine learning in sediment transport study and illustrates an alternative framework for other hydraulic engineering challenges.

42 ENGINEERING↗

Federated Machine Learning-Based Anomaly Detection System for Synchrophasor Network Using Heterogeneous Data Sets: Preprint

Synchrophasor technology is widely deployed in the energy management system to monitor the grid health at micro level and perform necessary corrective actions in real time; however, integrated phasor devices and data aggregators are exposed to several cybersecurity threats. This paper proposes a federated ML(FML)-based ADS to detect several data integrity attacks in the synchrophasor network. The proposed approach integrates the horizontal FML technique and consists of substation-based local models and a control center-based global model. The proposed methodology includes training local models using heterogeneous data sets that include network and grid information and updating the global model through multiple iterations by sharing model gradients. Finally, the trained global model is applied to identify cyberattacks, normal operation, and physical events. To validate the proof of concept, we used synthetic data sets generated by Mississippi State University and Oak Ridge National Laboratory for training and testing the classification models using the National Renewable Energy Laboratory's high performance computing resources. Our experimental results, computed through several performance measures, reveal that the proposed approach shows consistent performance during the binary, three-class, and multiclass classifications while ensuring privacy of synchrophasor data.

anomaly detection system↗

How to Design and Implement an Equitable Building Performance Standard: Lessons from the Building Performance Standards Technical Assistance Network

States and local governments seek to accomplish the intersecting goals of reducing greenhouse gas emissions, improving building operations, and bettering the daily lives of their communities. Building Performance Standards (BPS) have emerged as a critical policy lever to reach these intertwined climate and societal goals. These policies, if shaped and implemented well, have the chance to not only help reach our nation's climate, energy, and livability goals, but to do so with the active participation of those traditionally excluded from policy processes. This nascent policy movement provides jurisdictions across the country the opportunity to shape these policies from the outset to deliver comfort, health, and safety in our built environment for all. The US Department of Energy in partnership with our National Laboratories have been providing technical assistance for jurisdictions interested in, adopting, and implementing Building Performance Standards. Through this work, the BPS Technical Assistance Network (TA Network) has tracked and documented the innovative and equitable approaches to BPS across the country. The TA Network has crafted foundational technical analysis, such as building stock and emissions impacts, equity prioritization and peak load impacts, and aggregated cost benefit analysis. And by combining powerful technical analysis with dissemination of best practices resources to support equitable implementation, the TA Network provides jurisdictions with the tools and support necessary to embark on their ambitious policy goals.

building performance standards↗

Mechanistic Insights into Molecular Copper Hydride Catalysis: the Kinetic Stability of CuH Monomers toward Aggregation is a Critical Parameter for Catalyst Performance

The activity of molecular copper hydride (CuH) complexes towards the selective insertion of unsaturated hydrocarbons under mild conditions has contributed significantly to versatile methodologies for upgrading these feedstocks. However, these catalysts are particularly susceptible to deleterious aggregation, leading to the depletion of active CuH species. Little is known about the mechanisms of CuH aggregation, how it influences overall catalyst performance, and how it can be controlled. We address these challenges with mechanistic studies on a model reaction of unactivated alkene hydroboration catalyzed by (IPr*CPh 3 )CuH (LCuH). Here, we report a comprehensive mechanistic investigation of this system, identifying an aggregation pathway that continuously depletes catalytically active LCuH to form inactive CuH clusters during turnover. Deactivation of LCuH is controlled primarily by the competition between the kinetics of the initial LCuH dimerization step and that of alkene insertion. We therefore propose that a more comprehensive understanding of CuH catalyst performance must account for the kinetics of the initial LCuH dimerization step, revising a previously explored thermodynamic understanding of CuH aggregation, where the concentration of active species is controlled by equilibria established between CuH dimers and monomers. With a series of (NHC)CuH congeners (NHC = N-heterocyclic carbene), we demonstrate that ostensibly minor structural modifications to the ligand peripheries can drastically affect the LCuH dimerization kinetics, while maintaining reactivity towards on–cycle alkene insertion. We employed a computational approach based on molecular dynamics simulations to provide an in-depth understanding of how specific structural ligand modifications can substantially increase the kinetic stability of monomeric CuH catalysts. Our combined experimental and computational studies suggest strategies for rational ligand design that can be broadly applied to molecular catalyst systems that are susceptible to deactivation via aggregation pathways.

Ryan, David E. [Pacific Northwest National Laborat↗

Asymmetric side-chain engineering in semiconducting polymers: a platform for greener processing and post-functionalization of organic electronics

Organic semiconducting polymers are a powerful platform for the design of next-generation technologies due to their excellent optoelectronic properties and solution processability, allowing access to low-cost and scalable manufacturing techniques such as spin-coating, slot-die coating and roll-to-roll printing. However, their extended π-conjugation results in low solubility, requiring the use of toxic halogenated solvents to generate thin films and devices. Furthermore, accessible post-functionalization of semiconductors toward the development of multifunctional devices and sensors remains a challenge due to limited solid-state chemistry for alkyl side chains. In this work, an asymmetric side-chain engineering approach was used to introduce terminal hydroxyl moieties alongside traditional solubilizing branched alkyl chains into an isoindigo-based polymer. The hydroxyl moieties led to significantly improved processability in alcohol-based solvents without sacrificing electronic performance in thin film organic field-effect transistors. Solid state morphologies of the thin films processed from both alcohol-based and traditional halogenated solvents were further characterized using atomic force microscopy and grazing incidence wide angle X-ray scattering. Additionally, Cryo-EM was utilized in order to characterize the role of asymmetric side-chain functionality in solution state aggregation. The versatility of this design was further probed using fluorescein isothiocyanate to directly functionalize the asymmetric polymer in thin film. Furthermore, this facile solid-state post-functionalization further demonstrates asymmetric side-chain engineering to be a viable approach toward the development of sustainably manufactured multifunctional electronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗