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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Methane Generation from CO 2 with a Molecular Rhenium Catalyst

The atomic-level tunability of molecular structures is a compelling reason to develop homogeneous catalysts for challenging reactions such as the electrochemical reduction of carbon dioxide to valuable C 1 –C n products. Of particular interest is methane, the largest component of natural gas. Herein, we report a series of three isomeric rhenium tricarbonyl complexes coordinated by the asymmetric diimine ligands 2-(isoquinolin-1-yl)-4,5-dihydrooxazole ( quin-1-oxa ), 2-(quinolin-2-yl)-4,5-dihydrooxazole ( quin-2-oxa ), and 2-(isoquinolin-3-yl)-4,5-dihydrooxazole ( quin-3-oxa ) that catalyze the reduction of CO 2 to carbon monoxide and methane, albeit the latter with a low efficiency. To our knowledge, these complexes are the first examples of rhenium(I) catalysts capable of converting carbon dioxide into methane. Re(quin-1-oxa)(CO) 3 Cl ( 1 ), Re(quin-2-oxa)(CO) 3 Cl ( 2 ), and Re(quin-3-oxa)(CO) 3 Cl ( 3 ) were characterized and studied using a variety of electrochemical and spectroscopic techniques. In bulk electrolysis experiments, the three complexes reduce CO 2 to CO and CH 4 . When the controlled-potential electrolysis experiments are performed at -2.5 V (vs Fc +/0 ) and in the presence of the Brønsted acid 2,2,2-trifluoroethanol, methane is produced with turnover numbers that range from 1.3 to 1.8. Isotope labeling experiments using 13 CO 2 atmosphere produce 13 CH 4 ( m / z = 17) confirming that methane originates from CO 2 reduction. Theoretical calculations are performed to investigate the mechanistic aspects of the 8e – /8H + reduction of CO 2 to CH 4 . Overall, a ligand-assisted pathway is proposed to be an efficient pathway in the formation of CH 4 . Delocalization of the electron density on the (iso)quinoline moiety upon reduction stabilizes the key carbonyl intermediate leading to additional reactivity of this ligand. These results should aid the development of more robust catalytic systems that produce CH 4 from CO 2 .

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

Sample Dependence of Magnetism in the Next-Generation Cathode Material LiNi 0.8 Mn 0.1 Co 0.1 O 2

We present a structural and magnetic study of two batches of polycrystalline LiNi 0.8 Mn 0.1 Co 0.1 O 2 (commonly known as Li NMC 811), a Ni-rich Li ion battery cathode material, using elemental analysis, X-ray and neutron diffraction, magnetometry, and polarized neutron scattering measurements. We find that the samples, labeled S1 and S2, have the composition Li 1–x Ni 0.9+x–y Mn y Co 0.1 O 2 , with x = 0.025(2), y = 0.120(2) for S1 and x = 0.002(2), y = 0.094(2) for S2, corresponding to different concentrations of magnetic ions and excess Ni 2+ in the Li + layers. Both samples show a peak in the zero-field-cooled (ZFC) dc susceptibility at 8.0(2) K, but the temperature at which the ZFC and FC (field-cooled) curves deviate is substantially different: 64(2) K for S1 and 122(2) K for S2. The ac susceptibility measurements show that the transition for S1 shifts with frequency whereas no such shift is observed for S2 within the resolution of our measurements. Our results demonstrate the sample dependence of magnetic properties in Li NMC 811, consistent with previous reports on the parent material LiNiO 2 . Here, we further establish that a combination of experimental techniques is necessary to accurately determine the chemical composition of next-generation battery materials with multiple cations.

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

Amidinate Supporting Ligands Influence Molecularity in Formation of Uranium Nitrides

Uranium nitride complexes are attractive targets for chemists as molecular models for the bonding, reactivity, and magnetic properties of next-generation nuclear fuels, but these molecules are uncommon and can be difficult to isolate due to their high reactivity. Here, we describe the synthesis of three new multinuclear uranium nitride complexes, [U(BCMA) 2 ] 2 (μ-N)(μ-κ 1 :κ 1 -BCMA) (7), [(U(BIMA) 2 ) 2 (μ-N)(μ-N i Pr)(K 2 (μ-η 3 :η 3 -CH 2 CHN i Pr)] 2 (8), and [U(BIMA) 2 ] 2 (μ-N)(μ-κ 1 :κ 1 -BIMA) (9) (BCMA = N,N-bis(cyclohexyl)methylamidinate, BIMA = N,N-bis(iso-propyl)methylamidinate), from U(III) and U(IV) amidinate precursors. By varying the amidinate ligand substituents and azide source, we were able to influence the composition and size of these nitride complexes. 15 N isotopic labeling experiments confirmed the bridging nitride moieties in 7–9 were formed via two-electron reduction of azide. The tetra-uranium cluster 8 was isolated in 99% yield via reductive cleavage of the amidinate ligands; this unusual molecule contains nitrogen-based ligands with formal 1–, 2–, and 3– charges. Additionally, chemical oxidation of the U(IV) precursor U(N 3 )(BCMA) 3 yielded the cationic U(V) species [U(N 3 )(BCMA) 3 ][OTf]. Magnetic susceptibility measurements confirmed a U(IV) oxidation state for the uranium centers in the three nitride-bridged complexes and provided a comparison of magnetic behavior in the structurally related U(III)-U(IV)-U(V) series U(BCMA) 3 , U(N 3 )(BCMA) 3 , and [U(N 3 )(BCMA) 3 ][OTf]. At 240 K, the magnetic moments in this series decreased with increasing oxidation state, i.e., U(III) > U(IV) > U(V); this trend follows the decreasing number of 5f valence electrons along this series.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Synthesis and Reactivity of Heteroleptic U 4+ Alkyl, Benzyl, and Hydride Imidophosphorane Complexes

A series of heteroleptic U 4+ benzyl, neopentyl, and methyl complexes supported by the imidophosphorane ligand, [N = P(N,N′-ditert-butylethylenediamide)(diethylamide)] 1− (NP*), were synthesized from the monoiodide precursor, [UI(NP*) 3 ]. These heteroleptic complexes were synthesized through the selective formation of [UI(NP*) 3 ] under transmetalation conditions in the reaction between [UI 4 (1,4-dioxane) 2 ] and K[NP*]. Formation of the homoleptic complex [U(NP*) 4 ] was not observed even in the presence of excess K[NP*]. The oxidation and hydrogenolysis reactivity of the neopentyl complex, [U(Npt)- (NP*) 3 ] (Npt = neopentyl) was explored. While cyclic voltammetry indicates a potentially isolable U5+ alkyl cation, chemical oxidation of the neopentyl complex results in the isolation of a cationic U 4+ complex with a bound diethyl ether in the primary coordination sphere, [U4+(NP*)) 3 (Et 2 O)][(BArF 24 )] (BArF 24 = tetrakis(3,5-bis(trifluoromethyl)phenyl)borate). Notably, hydrogenolysis of [U(Npt)(NP*) 3 ] with H2 gas at −20 °C results in the formation of a terminal hydride intermediate confirmed by in situ NMR spectroscopy and deuterium labeling with D 2 . The connectivity and structural parameters of this hydride intermediate, [UH(NP*) 3 ], which rapidly thermally decomposes to the homoleptic complex, [U(NP*) 4 ], can be confirmed by single-crystal X-ray diffraction studies of a crystal grown by chilling the reaction mixture. The identity of [U(NP*) 4 ] was confirmed by its direct, bulk synthesis from [U(Me)(NP*) 3 ] and HNP* in a protonolysis reaction.

Alkyls↗
DOE OSTI2020

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, here we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ~35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump

Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. We assessed the performance effects of different methods of chemical featurization, data set partitioning, and class labeling and identified the methods producing models that generalized best to novel chemical entities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Ice Phase Classification Made Easy with Score-Based Denoising

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. Further, the denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Extracting Material Property Measurements from Scientific Literature with Limited Annotations

Extracting material property data from scientific text is pivotal for advancing data-driven research in chemistry and materials science; however, the extensive annotation effort required to produce training data for named entity recognition (NER) models for this task often makes it a barrier to extracting specialized data sets. Here, in this work, we present a comparative study of the conventional, supervised NER methodology to alternative few-shot learning architectures and large language model (LLM)-based approaches that mitigate the need to label large training data sets. We find that the best-performing LLM (GPT-4o) not only excels in directly extracting relevant material properties based on limited examples but also enhances supervised learning through data augmentation. We supplement our findings with error and data quality assessments to provide a nuanced understanding of factors that impact property measurement extraction.

36 MATERIALS SCIENCE↗
DOE OSTI2020

DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory

We propose a general machine learning-based framework for building an accurate and widely applicable energy functional within the framework of generalized Kohn–Sham density functional theory. To this end, we develop a way of training self-consistent models that are capable of taking large datasets from different systems and different kinds of labels. Here, we demonstrate that the functional that results from this training procedure gives chemically accurate predictions on energy, force, dipole, and electron density for a large class of molecules. It can be continuously improved when more and more data are available.

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

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.

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

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn–Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. Further, this scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Molecular Cage Reports on Its Contents: Spectroscopic Signatures of Cryo-Cooled K + - and Ba 2+ -Benzocryptand Complexes

UV photofragment spectroscopy and IR–UV double resonance methods are used to determine the structure and spectroscopic responses of a three-dimensional [2.2.2]-benzocryptand cage to the incorporation of a single K + or Ba 2+ imbedded inside it (labeled as K + -BzCrypt, Ba 2+ -BzCrypt). We studied the isolated ion-cryptand complex under cryo-cooled conditions, brought into the gas phase by nano-electrospray ionization. Incorporation of a phenyl ring in place of the central ethyl group in one of the three N-CH 2 -CH 2 -O-CH 2 -CH 2 -O-CH 2 -CH 2 -N chains provides a UV chromophore whose S 0 –S 1 transition we probe. K + -BzCrypt and Ba 2+ -BzCrypt have their S 0 –S 1 origin transitions at 35,925 and 36,446 cm –1 , respectively, blue-shifted by 174 and 695 cm –1 from that of 1,2-dimethoxybenzene. These origins are used to excite a single conformation of each complex selectively and record their IR spectra using IR–UV dip spectroscopy. The alkyl CH stretch region (2800–3000 cm –1 ) is surprisingly sensitive to the presence and nature of the encapsulated ion. We carried out an exhaustive conformational search of cage conformations for K + -BzCrypt and Ba 2+ -BzCrypt, identifying two conformations (A and B) that lie below all others in energy. Here, we extend our local mode anharmonic model of the CH stretch region to these strongly bound ion-cage complexes to predict conformation-specific alkyl CH stretch spectra, obtaining quantitative agreement with experiment for conformer A, the gas-phase global minimum. The large electrostatic effect of the charge on the O- and N-lone pairs affects the local mode frequencies of the CH 2 groups adjacent to these atoms. The localized CH 2 scissors modes are pushed up in frequency by the adjacent O/N-atoms so that their overtones have little effect on the alkyl CH stretch region. However, the localized CH 2 wags are nearly degenerate and strongly coupled to one another, producing an array of delocalized wag normal modes, whose highest frequency members reach up above 1400 cm –1 . As such, their overtones mix significantly with the CH stretch modes, most notably involving the CH 2 symmetric stretch fundamentals of the central ethyl groups in the all-alkyl chains and the CH stretches adjacent to the N-atoms and antiperiplanar to the nitrogen lone pair.

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

Proton Transfer Dynamics in the Aprotic Proton Accepting Solvent 1-Methylimidazole

The dynamics of proton transfer to the aprotic solvent 1-methylimidazole (MeIm, proton acceptor) from the photoacid 8-hydroxypyrene-1,3,6-trisulfonic acid trisodium salt (HPTS) were investigated using fast fluorescence measurements. The closely related molecule, 8- methoxypyrene-1,3,6-trisulfonic acid trisodium salt (MPTS), which is not a photoacid, was also studied for comparison. Following optical excitation, the wavelength-dependent population dynamics of HPTS in MeIm resulting from the deprotonation process were collected over the entire fluorescence emission window. Analysis of the time-dependent fluorescence spectra revealed four distinct fluorescence bands that appear and decay on different time scales. We label these four states as: protonated (P), associated I (A I ), associated II (A II ), and deprotonated (D). We find that the simple kinetic scheme of P → A I → A II → D is not consistent with the data. Instead, the kinetic scheme that describes the data has P decaying into A I , which mainly goes on to deprotonation (D), but AI can also feed into A II . A II can return to A I or decay to the ground state, but does not deprotonate within experimental error. Finally, quantum chemistry and excitedstate QM/MM Born-Oppenheimer molecular dynamics simulations indicate that A I and A II are two H-bonding conformations of MeIm to the HPTS hydroxyl, axial and equatorial, respectively.

14 SOLAR ENERGY↗
DOE OSTI2023

Characterization of mApple as a Red Fluorescent Protein for Cryogenic Single-Molecule Imaging with Turn-Off and Turn-On Active Control Mechanisms

Single-molecule superresolution microscopy is a powerful tool for the study of biological structures on size scales smaller than the optical diffraction limit. Imaging samples at cryogenic temperatures (77 K) reduces the quantum yield of photobleaching for many fluorescent labels, yielding localization precisions below 10 nm. Cryogenic imaging further enables correlation with cryogenic electron tomography. A key limitation in applying methods such as PALM and STORM to samples maintained at 77 K is the limited number of fluorophores known to undergo efficient turn-on and turn-off mechanisms necessary to control the sparsity of active emitters. Here we find that mApple, a red-emitting fluorescent protein, undergoes a novel turn-off mechanism in response to simultaneous illumination with two colors of light. This turn-off mechanism enables localization of many individual molecules in initially bright samples, but the final density of localizable emitters is limited by relatively inefficient turn-on (photoactivation). Bulk excitation and emission spectroscopy shows that mApple has access to two distinct emissive states as well as dark states accessible optically or through changes in pH. The bright and stable emission of mApple enables widefield collection of single-molecule emission spectra, which highlight the complex nature and environmental sensitivity of states observed in red fluorescent proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Molecular Dynamical and Quantum Mechanical Exploration of the Site-Specific Dynamics of Cy3 Dimers Internally Linked to dsDNA

Performing spectroscopic measurements on biomolecules labeled with fluorescent probes is a powerful approach to locating the molecular behavior and dynamics of large systems at specific sites within their local environments. The indocarbocyanine dye Cy3 has emerged as one of the most commonly used chromophores. The incorporation of Cy3 dimers into DNA enhances experimental resolution owing to the spectral characteristics influenced by the geometric orientation of excitonically coupled monomeric units. Various theoretical models and simulations have been utilized to aid in the interpretation of the experimental spectra. In this study, we employ all-atom molecular dynamics simulations to study the structural dynamics of Cy3 dimers internally linked to the dsDNA backbone. We used quantum mechanical calculations to derive insights from both the linear absorption spectra and the circular dichroism data. Furthermore, we explore potential limitations within a commonly used force field for cyanine dyes. The molecular dynamics simulations suggest the presence of four possible Cy3 dimeric populations. The spectral simulations on the four populations show one of them to agree better with the experimental signatures, suggesting it to be the dominant population. Furthermore, the relative orientation of Cy3 in this population compares very well with previous predictions from the Holstein–Frenkel Hamiltonian model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Conformational Dynamics of Bacteriochlorophyll c in Chlorosomes from the bchQ Mutant of Chlorobaculum tepidum

In contrast to the common viewpoint that bacteriochlorophyll (BChl) motion is largely absent within the chlorosome assembly, physics-based modeling points to a crucial role of the nanoscale librational motion of the macrocycle for the transfer of excitons. To elucidate this motion experimentally, compositional uniformity and high sensitivity are required. We focused on uniformly 13 C labeled chlorosome preparations from the bchQ mutant Chlorobaculum tepidum with significantly enhanced structural homogeneity. The librational motion is characterized using Rotational Echo DOuble Resonance (REDOR), and in addition, the impact of temperature on specific functionalities within BChl molecules is studied with 1-dimensional and 2-dimensional dipolar and scalar-based MAS NMR measurements. Results show the gradual freezing of the tails and side chains of the BChls with decreasing temperature. However, the librational motion analyzed by measuring the 5C–H dipolar coupling strength obtained from REDOR data sets persists at different temperatures. REDOR simulations show a close match to the experimental dephasing frequency of oscillation for a dipolar coupling strength of 17.5 ± 0.5 kHz which is considerably less than the dipolar coupling strength of 22.7 kHz in the rigid limit. Following a two-site jump model, we arrive at an estimate for BChl libration sampling at an angle of θ = 48 ± 4°, corroborating that the macrocycle indeed experiences significant librational motion on a time scale that is short compared to the NMR measurement time. This finding is in full quantitative support of the dominant rotational motion exhibited by the BChl macrocycle estimated from early MD simulations.

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

Facile Decomposition of Organophosphonates by Dual Lewis Sites on a Fe 3 O 4 (111) Film

Dimethyl methylphosphonate (DMMP) is used as a simulant for toxic nerve agents and pesticides, rendering the understanding of surface chemistry requisite to design effective materials for organophosphonate (catalytic) decomposition at room temperature. In this work, DMMP surface chemistry is studied on an iron oxide surface in a very well-defined environment using temperature-programmed reaction, isotopic labeling, scanning tunneling microscopy, X-ray photoelectron spectroscopy, and density functional theory. DMMP, (CH 3 O) 2 P(O)(CH 3 ), dissociates to yield methoxy and methyl methylphosphonate, (CH 3 O)P(O) 2 (CH 3 ), on the surface at room temperature. At higher temperatures, dimethyl ether is formed via intramolecular reaction, followed by the formation of formaldehyde and methanol from adsorbed methoxy decomposition during temperature-programmed reaction. Ultimately, stochiometric combustion at 870 K produces CO, H 2 C=O, and CO 2 via reaction with lattice oxygen, with PO x remaining on the surface. Excess oxygen from the bulk is required to drive these higher temperature pathways. Neither hydrolysis nor a photoreaction is observed, when exposing the adsorbed DMMP to water or light above the band gap, respectively. No evolution of P-containing species is detected, indicating efficient trapping of this contaminant. The activity for DMMP decomposition at room temperature is reduced by the accumulation of PO x . However, a significant amount of reaction persists after multiple temperature-programmed reaction experiments.

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

Investigation of Hierarchical Structure Formation in Merocyanine Photovoltaics

Merocyanines (MCs) are a versatile class of small-molecule dyes. Their optoelectronic properties are easily tunable by chemically controlling their donor–acceptor strength, and their structural properties can be tuned by simple side-chain substitution. This paper demonstrates a novel series of MCs featuring an indoline donor with varying hydrocarbon side-chain length (from 6 to 12 carbons) and a tert-butyl-thiazole acceptor, labeled InTBT. Bulk heterojunction organic photovoltaics are fabricated with a [6,6]-phenyl-C 61 -butyric acid methyl ester (PCBM) acceptor and characterized. Films composed of I8TBT:PCBM and I9TBT:PCBM produced the highest power conversion efficiency of 4.5%, which suggests that the morphology is optimized by controlling the side-chain length. Hierarchical structure formation in InTBT:PCBM films is studied using grazing incidence X-ray diffraction (GIXRD), small-angle neutron scattering (SANS), and atomic force microscopy (AFM). When mixed with PCBM, InTBTs with ≤8 side-chain carbons form pure crystalline domains, while InTBTs with ≥9 side-chain carbons mix well with PCBM. SANS demonstrates that increasing side-chain length increases the InTBT-rich domain size. In addition, a branched hexyl–dodecyl side-chain IHDTBT:PCBM film was studied and found to exhibit the worst-performance organic photovoltaic (OPV) device. The large-branched side chain inhibited mixing between IHDTBT and PCBM resulting in large segregated phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗