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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Network Based Estimation of Wind Farm Power and Velocity Data Under Changing Wind Direction

This paper describes an estimation algorithm for velocity and power output signals in a wind farm under changing wind direction. A graph-theoretic definition describes the wind farm as a collection of nodes (turbines) and time-varying weighted edges (inter-turbine wake propagation) that change as a function of incoming wind direction. The velocity at each turbine is determined through a discrete input-output model. Changes in wind direction serve as the input and the output is defined in terms of a time-varying weighted adjacency matrix that depends on the time-delay of information propagation between turbines. These delays, which are defined in terms of the advection speed of the wind and the distance between the turbines, capture the delayed effect of wind direction changes on the inter-connectivity of the graph as the wind conditions at the farm inlet propagate through the turbine array. An event-based update framework is employed to capture time-dependent topology changes due to shifts in wind direction. Simulation results for dynamically changing wind inlet directions to a circular wind farm are compared to predictions from both the static and dynamic versions of the FLOw Redirection and Induction in Steady State (FLORIS) model. The approach is shown to enable real-time tracking of dynamic changes to wind farm power output within a framework that can be easily integrated into real-time, horizon-based, control strategies that typically do not account for wind direction changes.

distributed↗
DOE OSTI2023

Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations

We introduce and investigate, for finite groups G, G-invariant deep neural network (GDNN) architectures with ReLU activation that are densely connected- i.e., include all possible skip connections. In contrast to other G-invariant architectures in the literature, the preactivations of theG-DNNs presented here are able to transform by signed permutation representations (signed perm-reps) of G. Moreover, the individual layers of the G-DNNs are not required to be G-equivariant; instead, the preactivations are constrained to be G-equivariant functions of the network input in a way that couples weights across all layers. The result is a richer family of G-invariant architectures never seen previously. We derive an efficient implementation of G-DNNs after a reparameterization of weights, as well as necessary and sufficient conditions for an architecture to be "admissible"- i.e., nondegenerate and inequivalent to smaller architectures. We include code that allows a user to build a G-DNN interactively layer-by-layer, with the final architecture guaranteed to be admissible. We show that there are far more admissible G-DNN architectures than those accessible with the "concatenated ReLU" activation function from the literature. Finally, we apply G-DNNs to two example problems--(1) multiplication in --1, 1} (with theoretical guarantees) and (2) 3D object classification--finding that the inclusion of signed perm-reps significantly boosts predictive performance compared to baselines with only ordinary (i.e., unsigned) perm-reps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The DESI-Lensing Mock Challenge: large-scale cosmological analysis of 3x2-pt statistics

The current generation of large galaxy surveys will test the cosmological model by combining multiple types of observational probes. Realising the statistical promise of these new datasets requires rigorous attention to all aspects of analysis including cosmological measurements, modelling, covariance and parameter likelihood. In this paper we present the results of an end-to-end simulation study designed to test the analysis pipeline for the combination of the Dark Energy Spectroscopic Instrument (DESI) Year 1 galaxy redshift dataset and separate weak gravitational lensing information from the Kilo-Degree Survey, Dark Energy Survey and Hyper-Suprime-Cam Survey. Our analysis employs the 3x2-pt correlation functions including cosmic shear and galaxy-galaxy lensing, together with the projected correlation function of the spectroscopic DESI lenses. We build realistic simulations of these datasets including galaxy halo occupation distributions, photometric redshift errors, weights, multiplicative shear calibration biases and magnification. We calculate the analytical covariance of these correlation functions including the Gaussian, noise and super-sample contributions, and show that our covariance determination agrees with estimates based on the ensemble of simulations. We use a Bayesian inference platform to demonstrate that we can recover the fiducial cosmological parameters of the simulation within the statistical error margin of the experiment, investigating the sensitivity to scale cuts. This study is the first in a sequence of papers in which we present and validate the large-scale 3x2-pt cosmological analysis of DESI-Y1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Angular Correlation Function Estimators Accounting for Contamination from Probabilistic Distance Measurements

With the advent of surveys containing millions to billions of galaxies, it is imperative to develop analysis techniques that utilize the available statistical power. In galaxy clustering, even small sample contamination arising from distance uncertainties can lead to large artifacts, which the standard estimator for two-point correlation functions does not account for. We first introduce a formalism, termed decontamination, that corrects for sample contamination by utilizing the observed cross-correlations in the contaminated samples; this corrects any correlation function estimator for contamination. Using this formalism, we present a new estimator that uses the standard estimator to measure correlation functions in the contaminated samples but then corrects for contamination. We also introduce a weighted estimator that assigns each galaxy a weight in each redshift bin based on its probability of being in that bin. We demonstrate that these estimators effectively recover the true correlation functions and their covariance matrices. Our estimators can correct for sample contamination caused by misclassification between object types as well as photometric redshifts; they should be particularly helpful for studies of galaxy evolution and baryonic acoustic oscillations, where forward modeling the clustering signal using the contaminated redshift distribution is undesirable.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The cosmological bootstrap: inflationary correlators from symmetries and singularities

Scattering amplitudes at weak coupling are highly constrained by Lorentz invariance, locality and unitarity, and depend on model details only through coupling constants and the particle content of the theory. For example, four-particle amplitudes are analytic for contact interactions and have simple poles with appropriately positive residues for tree-level exchange. In this paper, we develop an understanding of inflationary correlators which parallels that of flat-space scattering amplitudes. Specifically, we study slow-roll inflation with weak couplings to extra massive particles, for which all correlation functions are controlled by an approximate conformal symmetry on the boundary of the spacetime. After systematically classifying all possible contact terms in de Sitter space, we derive an analytic expression for the four-point function of conformally coupled scalars mediated by the tree-level exchange of massive scalars. Conformal symmetry implies that the correlator satisfies a pair of differential equations with respect to spatial momenta, encoding bulk time evolution in purely boundary terms. The absence of unphysical singularities (and the correct normalization of physical ones) completely fixes this correlator. Moreover, a “spin-raising” operator relates it to the correlators associated with the exchange of particles with spin, while “weight-shifting” operators map it to the four-point function of massless scalars. We explain how these de Sitter four-point functions can be perturbed to obtain inflationary three-point functions. Using our formalism, we reproduce many classic results in the literature, such as the three-point function of slow-roll inflation, and provide a complete classification of all inflationary three- and four-point functions arising from weakly broken conformal symmetry. Remarkably, the inflationary bispectrum associated with the exchange of particles with arbitrary spin is completely characterized by the soft limit of the simplest scalar-exchange four-point function of conformally coupled scalars and a series of contact terms. Finally, we demonstrate that the inflationary correlators contain flat-space scattering amplitudes via a suitable analytic continuation of the external momenta, which can also be directly connected with the signals for particle production seen in the squeezed limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Battery Degradation Modeling in Hybrid Power Plants: An Island System Unit Commitment Study: Preprint

As hybrid power plants (HPPs), such as photovoltaic (PV) and battery combinations, become increasingly important in power systems with high renewable energy penetration to address PV variability and ensure grid stability. This paper focuses on the urgent need to model the coordination between PV and battery systems in HPPs while accounting for battery degradation. We present a generation scheduling model that explicitly incorporates PV-battery hybridization in the unit commitment problem. Moreover, the cost function of the HPP scheduling problem endogenously considers battery degradation with adjustable weights to strike a balance between minimizing production costs and prolonging battery life, particularly when providing energy arbitrage and ancillary services. Using a realistic island system simulation, we demonstrate that accounting for battery degradation in the scheduling problem can significantly extend battery life with only minor additional production costs.

battery degradation↗
DOE OSTI2022

Integrands of less-supersymmetric Yang-Mills at one loop

We construct a prescriptive, bubble power-counting basis of one-loop integrands suitable for representing amplitude integrands in less-supersymmetric (1 ≤ $\mathcal{N}$ ≤ 4) Yang-Mills theory. With the exception of massless bubbles, all integrands have unambiguous, leading singularities as coefficients defined in field theory; for the massless bubbles on external legs, we find two natural choices which lead to different integrands that highlight distinct aspects of field theory. For concreteness, we give the all-multiplicity integrands for MHV amplitudes, and the split-helicity amplitude integrand for six-particle NMHV. The basis we construct is mostly pure and is divided into to separately UV- and IR-finite sectors of fixed transcendental weight, resulting in UV- and IR-finite ratio functions of n-particle helicity amplitudes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Dynamic Performance Enhancement for Nonlinear Stochastic Systems Using RBF Driven Nonlinear Compensation with Extended Kalman Filter

In this paper, a novel hybrid control method is proposed to enhance the control performance of the PI based control system for a class of nonlinear and non-Gaussian stochastic dynamic processes with unmeasurable states. Firstly, to enhance the tracking performance of the PI controller where the PI parameters are fixed in many actual control systems, the compensative signal is formed using the extended Kalman filter(EKF) based state estimator and driven by the radial basis function (RBF) neural network based compensator. In addition, the weights of RBF is trained to minimize the entropy criterion of tracking error as the process is subjected to non-Gaussian disturbances. Meanwhile, since the precise statistical property of noises is hard to obtain for many industrial processes, the kernel density estimation (KDE) technique is employed in this paper to estimate the entropy. The convergence of RBF network is discussed and the stability of the resulted closed-loop hybrid control system is analyzed in mean square sense. Finally, a numerical example and a practical system testing are given to illustrate the effectiveness of the proposed control method.

stochastic systems, PI Controller, Entropy, Extend↗
DOE OSTI2021

Linking Perfluorosulfonic Acid Ionomer Chemistry and High-Current Density Performance in Fuel-Cell Electrodes

Transport phenomena are key in controlling the performance of electrochemical energy-conversion technologies and can be highly complex, involving multiple length scales and materials/phases. Material designs optimized for one reactant species transport however may inhibit other transport processes. We explore such trade-offs in the context of polymer-electrolyte fuel-cell electrodes, where ionomer thin films provide the necessary proton conductivity but retard oxygen transport to the Pt reaction site and cause interfacial resistance due to sulfonate/Pt interactions. We examine the electrode overall gas-transport resistance and its components as a function of ionomer content and chemistry. Low-equivalent-weight ionomers allow better dissolved-gas and proton transport due to greater water uptake and low crystallinity but also cause significant interfacial resistance due to the high density of sulfonic acid groups. These effects of equivalent weight are also observed via in situ ionic conductivity and CO displacement measurements. Of critical importance, the results are supported by ex situ ellipsometry and X-ray scattering of model thin-film systems, thereby providing direct linkages and applicability of model studies to probe complex heterogeneous structures. Structural and resultant performance changes in the electrode are shown to occur above a threshold sulfonic-group loading, highlighting the significance of ink-based interactions. Furthermore, our findings and methodologies are applicable to a variety of solid-state energy-conversion devices and material designs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Polariton relaxation under vibrational strong coupling: Comparing cavity molecular dynamics simulations against Fermi’s golden rule rate

Under vibrational strong coupling (VSC), the formation of molecular polaritons may significantly modify the photo-induced or thermal properties of molecules. In an effort to understand these intriguing modifications, both experimental and theoretical studies have focused on the ultrafast dynamics of vibrational polaritons. Here, following our recent work [Li et al., J. Chem. Phys. 154, 094124 (2021)], we systematically study the mechanism of polariton relaxation for liquid CO 2 under a weak external pumping. Classical cavity molecular dynamics (CavMD) simulations confirm that polariton relaxation results from the combined effects of (i) cavity loss through the photonic component and (ii) dephasing of the bright-mode component to vibrational dark modes as mediated by intermolecular interactions. The latter polaritonic dephasing rate is proportional to the product of the weight of the bright mode in the polariton wave function and the spectral overlap between the polariton and dark modes. Both these factors are sensitive to parameters such as the Rabi splitting and cavity mode detuning. Compared to a Fermi’s golden rule calculation based on a tight-binding harmonic model, CavMD yields a similar parameter dependence for the upper polariton relaxation lifetime but sometimes a modest disagreement for the lower polariton. We suggest that this disagreement results from polariton-enhanced molecular nonlinear absorption due to molecular anharmonicity, which is not included in our analytical model. Finally, we also summarize recent progress on probing nonreactive VSC dynamics with CavMD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Effect of Natural Fermentation of Sorghum on Resistant Starch Molecular Structure and Fermentation Property

Relevant research results have suggested that fermentation can increase the content of sorghum amylose chains and their retrogradation value. Therefore, this study explored the effect of fermentation pretreatment on the yield, digestibility, molecular structure, and in vitro fermentation property of sorghum-resistant starch by conducting fermentation pretreatment of sorghum and extracting the resistant starch from fermented sorghum with pressure-heat compound enzyme method. The results were as follows. After fermentation pretreatment, the yield of sorghum-resistant starch increased, the digestibility of sorghum-resistant starch reduced, the laminated structure size on the surface of the particles became more uniform, and the stacking mode became more neat and denser. The sorghum-resistant starch prepared before and after fermentation did not produce new chemical groups, and its functional group peak remained unchanged. After fermentation, the weight-average molecular weight of sorghum-resistant starch was elevated, and the percentage content of high- and low-molecular substances increased and decreased, respectively, compared with that of the unfermented sorghum-resistant starch. The percentage content of short-chain branches in the branched chain increased, whereas that of the long-chain branches decreased; the crystallinity of sorghum-resistant starch after fermentation decreased, and the intensity of X-diffraction peak changed slightly before and after fermentation. According to the results of the in vitro fermentation experiments, the fermentation broth of sorghum-resistant starch had the highest content of butyric acid and short-chain fatty acid. Research results reveal that, after fermentation pretreatment, sorghum-resistant starch presented increased yield, more complex molecular structure, heavier molecular weight and more uniform surface morphology, more efficient butyric acid generation, and greater fermentation rate than unfermented sorghum-resistant starch.

Ge, YunFei↗
DOE OSTI2022

Prioritizing ICS Beachhead Systems for Cyber Vulnerability Testing

Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS Prioritization Process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS Prioritization Process was premised largely upon the impact which could result to an industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. The worldwide compromise of the SolarWinds Orion platform, first reported in December 2020, through malicious interference with the digital patching cycle was a watershed event in cyber supply chain security. The SolarWinds compromised demonstrated the strategic importance of certain types of ubiquitous software, and the ability to generate widespread cybersecurity effects. To address this challenge and as a part of the Department of Energy’s response to the SolarWinds compromise, DOE’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER) directed the National Laboratories to evolve the CyTRICS Prioritization Process methodology to encompass additional factors related to the strategic importance of digital components. CESER directed CyTRICS researchers to identify, characterize, and append strategic factors to the CyTRICS Prioritization Process to provide additional weight to these characteristics. National Laboratory expert researchers identified functionality, distribution, and platform characteristics for digital components in ICS and OT that they assessed would be likely targeted in strategic initial-access cyber attack. CyTRICS has termed these factors “ICS Beachhead Systems,” leveraging a definition first advanced by Schneider Electric, which is intended as a blanket term to encompass digital components, products, and systems in OT. This paper describes the ICS Beachhead Systems identified and the rationale for inclusion. As a next step in the research and refinement process, the National Laboratories will validate this initial set of characteristics against digital components evaluated by the CyTRICS program and current implementation of the CyTRICS Prioritization Process. After validation, CyTRICS researchers will then develop a scoring methodology to generate a quantitative score to assess the degree to which a digital component is characterized as an ICS Beachhead System. Finally, the National Laboratories will append this scoring to the existing CyTRICS Prioritization Process algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Improving Heat Conduction of Insulated Metal Substrate with Thermal Pyrolytic Graphite Core for SiC Power Module Packaging

TPG-core IMS concept was jointly explored in this study. Integrating high thermally conductive TPG graphite core into IMS is expected to simultaneously achieve high thermal conductivity from TPG and electrical functionalities from IMS. Nearly 2x thermal conductivity and 30% weight saving was demonstrated on TPG-core IMS compared to conventional Cu-core IMS. Significant junction temperature reduction (11°C) in steady state and power cycling was revealed by the thermal analysis as the result of improved thermal spreading in plane and through the thickness. The study also proved the manufacturability and compatibility of this TPG-core IMS structure to the existing IMS production and power module assembling. Finally, the integration of TPG and IMS paves a new packaging route to increase heat load, improve reliability, simplify module design and reduce assembling cost and number of steps.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, in this work we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction (≈ O(-6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Graphene Oxide Nanoribbons for High Early-Strength Cement Concrete

Longitudinal oxidative unzipping of the outer walls of multiwalled carbon nanotubes (MWCNTs) yields graphene oxide nanoribbons (GONRs), which exhibit greater open surface area and functional edge content than MWCNTs. This paper presents a study of the nano-amendment of Portland cement concrete with GONRs in concentrations between 0.05% (in weight of cement, wt%) and 0.0005 wt%, thus up to two orders of magnitude lower than that reported as lower-bound in the archival literature. The dispersibility in aqueous solution as a function of GONR concentration and oxygen weight content (O%) was assessed through dynamic light scattering (DLS) and zeta potential analysis. The results indicated that less effective suspensions were obtained for 0.05 wt% of GONRs and 22.7 O%. Therefore, GONR water suspensions with 30-40% O% were used to manufacture 50 mm × 100 mm cylindrical concrete specimens. After 7 days of curing, results from uniaxial compression tests using four specimens per configuration (MWCNT concentration and O%) showed that the incorporation of GONRs resulted in an average increase in compressive strength up to 45%. Consistent with the DLS and compression test results, SEM micrographs showed well-dispersed GONRs together with accelerated and preferential formation of calcium silicate hydrates (C-S-H) for all GONR concentrations. The results indicate, for the first time, that the incorporation of very small concentrations (as low as 0.0005 wt%) of well-dispersed GONR amendments can significantly enhance the early-age concrete strength. However, such enhancement became insignificant after 28 days of curing.

cement↗
DOE OSTI2025

Bidirectional Suzuki Catalyst Transfer Polymerization of Poly( p -phenylene)

Suzuki catalyst transfer polymerization (SCTP) has emerged as an effective method for accessing length-controlled π-conjugated poly(p-phenylene). Regio-controlled functional group sequencing along the backbone and the chain ends of synthetic polymers remains a challenge for the successful integration of organic semiconductors in sensors and electronic devices. Here, we report a bidirectional SCTP system based on dinuclear palladium(II) initiators. Functional groups transferred during the initiation and termination steps unlock independently addressable synthetic handles at the polymer core and respective chain ends. These functional groups open opportunities for late-stage regio-controlled and chemoselective derivatizations. Control over key polymer parameters, including molecular weight (M n ), dispersity ( Đ = M w /M n ), degree of polymerization ( DP ), termination efficiency, and functional group interconversion, is corroborated by size exclusion chromatography (SEC) and NMR spectroscopy. The modular design of SCTP initiators, in combination with commercially available terminating groups, represents a highly flexible toolbox for late-stage polymer conjugation, e.g., chemoselective anchoring groups for integration with functional electronics or bioorthogonal conjugation for molecular sensing.

Aldehydes↗
DOE OSTI2023

Rapid Postgrafting Reaction to Prepare Quaternized Polycarbazoles

We report a rapid postgrafting reaction to prepare alkyl ammonium functionalized polycarbazoles from a commercially available monomer. This novel synthetic approach provides benefit to preparing the high molecular weight quaternized polycarbazoles within 1 h of Friedel–Crafts polycondensation, avoiding the synthesis and purification step to prepare a functionalized monomer. The postgrafting reaction produces hexyl alkyl ammonium functionalized polycarbazole with 100% grafting degree. However, the postgrafting reaction produced only 60% grafting with propyl alkyl ammonium due to the competitive elimination reaction because of the higher acidity of β-hydrogen in the propyl alkyl group resulting from the proximity of the bromide and ammonium groups. The hexyl alkyl ammonium functionalized polycarbazole has a high hydroxide conductivity of 103 mS cm –1 at 80 °C and showed excellent alkaline stability with less than 3% loss of ion group after 1 M NaOH treatment at 80 °C for 500 h. In conclusion, this study highlights that the postgrafting reaction provides a pathway for the scale-up synthesis of quaternized aryl ether-free polyaromatics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Synthesis of End-Functionalized Poly(norbornenes) and Poly(ethylidene norbornenes) Using a Pd(II) Catalyst in Combination with Chain Transfer Agents

Synthesis of end-functionalized poly(norbornene)s (PNBs) and poly(ethylidenenorbornene)s (PENBs) were prepared using the palladium catalyst t Bu 3 PPdMeCl/NaBAr f in combination with chain transfer agents (CTAs) 4-penten-1-ol and 10-undecenal. These CTAs produce polymers end-capped with -OH and -CHO groups, respectively, in high yields with M n values in the range of 6K to 17K, depending on the molar ratio of the CTA: monomer. End-functionalized PNBs and PENBs with precisely controlled Mn values and very narrow molecular weight distributions were accessed by using 4-penten-1-ol and 10-undecenal as chain-terminating agents following living polymerizations in which monomer is completely consumed. The utility of the aldehyde-functionalized polymers is illustrated via coupling to a dye molecule and by synthesis of ambiphilic diblock copolymers, PNB-b-PEG and PENB-b-PEG.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗