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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Intrinsic Li Distribution in Layered Transition-Metal Oxides Using Low-Dose Scanning Transmission Electron Microscopy and Spectroscopy

Understanding Li distribution in layered lithium transition-metal oxide (LiTMO) cathodes in Li-ion batteries has been a major challenge at the atomic scale and nanoscale. Li is extremely difficult to study by transmission electron microscopy (TEM) because the high-energy electrons impart significant energy and cause massive migration. Here, we directly map the intrinsic spatial distribution and bonding of Li in LiNiO 2 -layered cathode materials using low-dose and low-loss electron energy loss spectroscopy (EELS). EELS spectra of the Li–K edge are measured simultaneously with O–K and Ni–L, M 3,2 edges from layered, cation-mixed, and rock-salt phases and directly matched with atomic-resolution scanning TEM images to correlate the changes in peak intensities and positions to the stoichiometry changes with continual loss of Li and O. Changes in the Li content in the LiNiO 2 particles as a function of electron beam dose are studied by sequential Li spectroscopic mapping. We show that the “intrinsic” Li distribution can be observed using a total dose of less than ~1.5 × 10 8 e – nm –2 at an accelerating voltage of 80 kV. The method of nanoscale mapping of Li distribution introduced in this study is applicable to high-Ni LiTMO cathode materials (>89% of Ni) as well as LiNiO 2 . Further study on the extra peaks of the Li–K edge reveals that the peak at ~59 eV is from the Li ions intercalated in between NiO 2 layers barely interacting with each other with less Li K shell electrons pulled to the L shell electrons in NiO 2 . Furthermore, the results shown here provide improved low-loss TEM characterization approaches that can be used to understand the intrinsic fundamental behaviors in Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2026

A Proof for the Unbiased Nature of Range-Doppler Measurements in Coarse-Resolution Dechirp-on-Receive Feedback Synthetic Aperture Radar Navigation

In feedback synthetic aperture radar (SAR) navigation, observables extracted from SAR range-Doppler images correct position and velocity errors accumulated within an associated navigation system. Unlike most other sensors, which produce measurements without input from a navigation system, SARs require a prior estimate of the radar’s position and velocity to adjust the radar’s matched filter during range-Doppler image formation. Consequently, it is possible for position and velocity errors within a navigation system to manifest as additional errors (biases) in the range-Doppler measurement observables. Prior work has not tackled this possibility in the context of feedback SAR navigation with a dechirp-on-receive radar. This paper offers a proof demonstrating that range-Doppler observables extracted from coarse-resolution vertical SAR images formed with a dechirp-on-receive radar may be safely modeled as unbiased measurements of the radar’s true position and velocity despite the presence of moderate navigation errors.

dechirp-on-receive↗
DOE OSTI2022

Towards performing high-resolution inelastic X-ray scattering measurements at hard X-ray free-electron lasers coupled with energetic laser drivers

High-resolution inelastic X-ray scattering is an established technique in the synchrotron community, used to investigate collective low-frequency responses of materials. When fielded at hard X-ray free-electron lasers (XFELs) and combined with high-intensity laser drivers, it becomes a promising technique for investigating matter at high temperatures and high pressures. This technique gives access to important thermodynamic properties of matter at extreme conditions, such as temperature, material sound speed, and viscosity. The successful realization of this method requires the acquisition of many identical laser-pump/X-ray-probe shots, allowing the collection of a sufficient number of photons necessary to perform quantitative analyses. Here, a 2.5-fold improvement in the energy resolution of the instrument relative to previous works at the Matter in Extreme Conditions (MEC) endstation, Linac Coherent Light Source (LCLS), and the High Energy Density (HED) instrument, European XFEL, is presented. Some aspects of the experimental design that are essential for improving the number of photons detected in each X-ray shot, making such measurements feasible, are discussed. A careful choice of the energy resolution, the X-ray beam mode provided by the XFEL, and the position of the analysers used in such experiments can provide a more than ten-fold improvement in the photometrics. The discussion is supported by experimental data on 10 µm-thick iron and 50 nm-thick gold samples collected at the MEC endstation at the LCLS, and by complementary ray-tracing simulations coupled with thermal diffuse scattering calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhanced Monte Carlo Simulations for Electron Energy Loss Mitigation in Real-Space Nanoimaging of Thick Biological Samples and Microchips

High-resolution imaging using Transmission Electron Microscopy (TEM) is essential for applications such as grain boundary analysis, microchip defect characterization, and biological imaging. However, TEM images are often compromised by electron energy spread and other factors. In TEM mode, where the objective and projector lenses are positioned downstream of the sample, electron–sample interactions cause energy loss, which adversely impacts image quality and resolution. This study introduces a simulation tool to estimate the electron energy loss spectrum (EELS) as a function of sample thickness, covering electron beam energies from 300 keV to 3 MeV. Leveraging recent advances in MeV-TEM/STEM technology, which includes a state-of-the-art electron source with 2-picometer emittance, an energy spread of 3 × 10 -5 , and optimized beam characteristics, we aim to minimize energy spread. By integrating EELS capabilities into the BNL Monte Carlo (MC) simulation code for thicker samples, we evaluate electron beam parameters to mitigate energy spread resulting from electron–sample interactions. Based on our simulations, we propose an experimental procedure for quantitively distinguishing between elastic and inelastic scattering. The findings will guide the selection of optimal beam settings, thereby enhancing resolution for nanoimaging of thick biological samples and microchips.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Understanding the causes of satellite–model discrepancies in aerosol–cloud interactions using near-LES simulations of marine boundary layer clouds

Aerosol–cloud interactions (ACI) remain the largest source of uncertainty in model estimates of anthropogenic radiative forcing, primarily because of deficiencies in representing aerosol–cloud microphysical processes that lead to inconsistent cloud liquid water path (LWP) responses to aerosol perturbations between observations and models. To investigate this discrepancy, we conducted a series of large-eddy-scale simulations driven by realistic meteorology over the eastern North Atlantic, and evaluated LWP susceptibility, precipitation processes, and boundary layer thermodynamics using satellite and ground-based observations. Simulated LWP responses show a strong dependence on cloud state. Non-precipitating thin clouds exhibit a modest LWP decrease with increasing cloud droplet number concentration (N d ), consistent in sign but weaker in magnitude than satellite estimates, reflecting enhanced turbulent mixing and evaporation. The largest model-observation discrepancy occurs in non-precipitating thick clouds, where simulated LWP susceptibilities are strongly positive (+0.32) while observations indicate large negative values (−0.69). This discrepancy stems from excessive precipitation driven by underestimated entrainment, overly active accretion, and overly broad drop-size distributions in polluted conditions. While our high-resolution setup mitigates the excessive drizzling common in coarser models and captures key regime transitions, these biases persist – highlighting that improved parameterizations of cloud-top processes, precipitation, and aerosol effects are needed beyond simply increasing model resolution. Additionally, misrepresented moisture inversions in reanalysis introduce a moist bias in cloud-top relative humidity, further amplifying positive LWP susceptibility. Our results also suggest that large negative N d –LWP relationships in observations may reflect internal cloud processes rather than true ACI effects.

Aerosol-cloud interaction↗
DOE OSTI2020

Performance of 8 × 8 × 32 and 10 × 10 × 32 mm 3 CdZnTe position-sensitive virtual Frisch-grid detectors for high-energy gamma ray cameras

CdZnTe (CZT) crystals with thicknesses up to 20 mm and diameters up to 75 mm have become available from Redlen Technologies, Inc. for making large-volume gamma-ray detectors. The modified growth and optimized annealing conditions allowed Redlen to minimize the dark current and improve the uniformity of CZT crystals. Two detector designs, pixelated (H3D) and position-sensitive virtual Frisch-grid (VFG), have greatly benefitted from the availability of such big crystals. Encouraging results from testing of 40×40×15 mm 3 pixelated detectors were recently reported by the University of Michigan. In this work, we evaluated the spectroscopic properties of 8×8×32 and 10×10×32 mm 3 crystals configured as position-sensitive VFG detectors. Both digitized waveforms and an analog ASIC were used to read and process the signals from the detectors and test their spectral- and spatial-resolution. The VFG design provides the flexibility to scale-up the dimensions of the detectors for the desired efficiency, while the position information allows for correcting the detectors' response non-uniformity caused by crystal defects and devices geometry, thereby reducing the instrument cost and making them more feasible for emerging applications in gamma-ray astronomy, nonproliferation, portal screening and nuclear safeguards, where large detector arrays are often required.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Search for $\mu^- \rightarrow e^+$ conversion: what can be learned from the SINDRUM-II positron data on a gold target

In their 2006 paper setting the current limit on \mumemconv\ conversion search on a gold target \cite{sindrum_ii:Bertl2006}, the SINDRUM-II collaboration published, along with the electron momentum distribution, the momentum distribution of reconstructed positrons. Near the positron spectrum endpoint, there is a statistically significant excess of observed events over the expected background. We estimate that in the region 88 MeV/c < p < 95 MeV/c there are 13 events with an expected background of about 1-1.5 event, which has not been discussed by the authors. Those 13 events form a bump with a width consistent with the experimental resolution, making one think of a $\mu^- \rightarrow e^+$ conversion signal. However, the reconstructed position of the bump is about 1 MeV/c, or $\sim\ 4\sigma_p$, lower than the expected $\mu^- Au \rightarrow e^+ Ir $ signal, which strongly discourages the exotic interpretation. The excess, however, could be due to an exclusive dipole radiative muon capture (RMC) transition $^{197}Au(GS) ~\rightarrow~ ^{197}Pt(GS)$ with the branching fraction of about $2\cdot 10^{-4}$. Such a transition would not be resolved by the existing RMC measurements. We conclude that the exclusive RMC transitions could significantly modify the positron spectrum near the kinematic endpoint, to fully exploit the physics potential of the upcoming experiments such as Mu2e and COMET, a better theoretical understanding of the RMC spectrum on nuclei is needed. A high-resolution measurement of the RMC photon spectra has to be carried out. Without that, the sensitivity of the searches for $\mu^- \rightarrow e^+$ and $\mu^- \rightarrow e^-$ might be severely limited by unknown probabilities of RMC transitions to the exclusive low-lying states of the daughter nuclei.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions

Many imaging techniques for biological systems—like fixation of cells coupled with fluorescence microscopy—provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. In this study, these snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Machine learning reveals strong grid-scale dependence in the satellite N d –LWP relationship

The relationship between cloud droplet number concentration ( N d ) and liquid water path (LWP) is highly uncertain yet crucial for determining the impact of aerosol-cloud interactions (ACI) on Earth's radiation budget. The N d -LWP relationship is examined using a machine learning (ML) random forest model applied to five years of satellite data at grid resolutions ranging from 10° to 0.05° in 12 distinct regions. In the subtropics, the shape of the N d -LWP relationship switches from an inverted-V at 1° grid-resolution to an “M” shape at 0.1° resolution with decreased $\frac{\textrm{dln⁡LWP}}{\textrm{dln⁡}N_d}$ sensitivity. Tropical and midlatitude regions generally show a more positive sensitivity. Cloud sampling and filtering also influence this slope, wherein the exclusion of thin clouds, as commonly performed to reduce retrieval uncertainty, leads to strongly negative sensitivity across all regions. Precipitation is primarily responsible for driving the strength of the sensitivity, with strong positive slopes in raining clouds and negative and/or neutral responses found in non-raining clouds. A new method to compute radiative forcing from the ML model shows a robust Twomey radiative forcing across all regions and grid resolutions. However, LWP and cloud fraction adjustments to the radiative forcing, which are ∼50 % or smaller than the Twomey effect, decrease to negligible values with higher spatial resolution data. As Earth system models move toward higher spatial resolutions in the future, evaluating the LWP and CF adjustment contributions to the radiative forcing budget at these finer resolutions will be essential for evaluation and model development.

Aerosol-Cloud Interactions↗
DOE OSTI2021

Practical considerations for amino acid isotope analysis

Over the last few decades, isotopic analysis of amino acids at the compound- and position-specific levels has been rapidly advancing across diverse fields. As these techniques progress, evaluation of isotopic fractionation associated with sample workup is essential. This critical review of analytical methods through the lens of isotope geochemistry provides a benchmark for researchers across disciplines seeking to make compound- and position-specific amino acid isotope measurements. We focus on preparation, acid hydrolysis, clean-up, derivatization, separation, and C, H, N, and S isotope measurement. Despite substantial customizability across these steps, the following general recommendations should maximize recovery while minimizing isotopic fractionation. Samples should be freeze-dried and stored anoxically at ≤ –20 °C prior to conventional acid hydrolysis (6N HCl, 110 °C, 20–24 h, anoxic), which suffices for many residues. Both gas and liquid chromatographic (GC and LC, respectively) techniques are well-established and separate about 15 amino acids; LC bypasses the need for derivatization, while GC provides higher sensitivity. Furthermore, when derivatization is needed, n-acetyl and alkoxycarbonyl esters provide the most reproducible C isotope ratios. For compound-specific analyses, online GC–IRMS and LC–IRMS systems offer the easiest workflow, but EA–IRMS enables potential multi-element isotope analysis. Emerging techniques like high-resolution mass spectrometry are also promising for multi-element analysis and recover position-specific isotopic information. Looking forward to the next decade of innovation, isotope geochemists and ecologists can improve amino acid isotope analysis by focusing on streamlining multi-element analysis and standardizing calibration practices across laboratories.

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

Structure of a monomeric photosystem II core complex from a cyanobacterium acclimated to far-red light reveals the functions of chlorophylls $d$ and $f$

Far-red light (FRL) photoacclimation (FaRLiP) in cyanobacteria provides a selective growth advantage for some terrestrial cyanobacteria by expanding the range of photosynthetically active radiation to include far-red/near-infrared light (700 to 800 nm). During this photoacclimation process, photosystem II (PSII), the water:plastoquinone photooxidoreductase involved in oxygenic photosynthesis, is modified. The resulting FRL-PSII is comprised of FRL-specific core subunits and binds chlorophyll (Chl) d and Chl f molecules in place of several of the Chl a molecules found when cells are grown in visible light. These new Chls effectively lower the energy canonically thought to define the “red limit” for light required to drive photochemical catalysis of water oxidation. Changes to the architecture of FRL-PSII were previously unknown, and the positions of Chl d and Chl f molecules had only been proposed from indirect evidence. Here, we describe the 2.25-Å resolution cryo-EM structure of a monomeric FRL-PSII core complex from Synechococcus sp. PCC 7335 cells that were acclimated to FRL. We identify one Chl d molecule in the Chl D1 position of the electron transfer chain, and four Chl f molecules in the core antenna. We also make observations that enhance our understanding of PSII biogenesis, especially on the acceptor side of the complex where a bicarbonate molecule is replaced by a glutamate sidechain in the absence of the assembly factor Psb28. In conclusion, these results provide a structural basis for the lower energy limit required to drive water oxidation, which is the gateway for most solar energy utilization on Earth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Field surveying data of low-cost networked flood sensors in southeast Texas

Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are essential. Southeast Texas is still at significant risk of flooding, and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. Lamar University developed a survey program to measure elevation and coordinates at each sensor site location to make this data more useful for flood monitoring and mapping. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various flood stage thresholds at critical points that flood decision-makers can use for reference at each site. In the first phase of this program, these sensors were deployed throughout a 7-county region spanning nearly 6,000 square miles in Southeast Texas. The latitude and longitude of the sensors and their elevations were determined using survey-grade Global Navigation Satellite System (GNSS) technology. Various Continually Operating Reference Stations (CORS) were utilized for post-processing to achieve sub-inch resolution. The flood stage thresholds, water level sensors elevation, and the elevations and positions of other critical surrounding points are viewable to the public through two online repositories and a web-based sensor management dashboard. The data is used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High-resolution radiation detection using Ni/SiO 2 /n-4H-SiC vertical metal-oxide-semiconductor capacitor

In this article, we demonstrate the radiation detection performance of vertical metal-oxide-semiconductor (MOS) capacitors fabricated on 20 μm thick n-4H-SiC epitaxial layers with the highest energy resolution ever reported. The 100 nm SiO2 layer was achieved on the Si face of n-4H-SiC epilayers using dry oxidation in air. The Ni/SiO2/n-4H-SiC MOS detectors not only demonstrated an excellent energy resolution of 0.42% (ΔE/E 100) for 5.48 MeV alpha particles but also caused a lower enhancement in the electronic noise components of the spectrometer compared with that observed for the best high-resolution Schottky barrier detectors. The MOS detectors also exhibited a high charge collection efficiency (CCE) of 96% at the optimized operating bias despite the presence of the oxide layer. A drift-diffusion model applied to the CCE vs gate bias voltage data revealed a minority (hole) carrier diffusion length of 24 μm. Capacitance mode deep level transient spectroscopy (C-DLTS) scans in the temperature range 84–800 K were carried out to identify the resolution limiting electrically active defects. Interestingly, the C-DLTS spectra revealed both positive and negative peaks, indicating the simultaneous presence of electron (majority) and hole (minority) trap centers. It has been inferred that at the steady-state bias for the C-DLTS measurement, the MOS detector operates in the inversion mode at certain device temperatures, causing holes to populate the minority trap centers and, hence, manifests minority carrier peaks as well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Interior soft x-ray tomography with sparse global sampling

To investigate the feasibility of interior imaging reconstruction in soft X-ray tomography for higher-resolution cellular imaging, including whole-cell imaging, we develop an alignment and reconstruction algorithm that combines a small number of sparse whole-cell images with a high-resolution local interior scan. Based on numerical simulations, we demonstrate that combined reconstructions mitigate the depth-of-field limitation in high-resolution scans, enable radiation dose optimization, and yield quantitative X-ray absorption values with sparse sampling. We further validate our numerical approach using experimental data from two different cell types and show that the combined reconstruction reliably provides high spatial resolution within an interior region of interest of a whole cell. The resulting sparse reconstruction framework offers robust, faithful visualization of cellular organelles in soft X-ray tomography. This mesoscale imaging strategy allows one to ‘scout’ and zoom into selected subcellular volumes of interest, enabling increased spatial resolution without sacrificing larger-volume imaging and providing information on the relative positions of all organelles within a cell.

3D imaging↗
DOE OSTI2020

Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

42 ENGINEERING↗
DOE OSTI2024

Ligand‐Mediated Quantum Yield Enhancement in 1‐D Silver Organothiolate Metal–Organic Chalcogenolates

X-ray free electron laser (XFEL) microcrystallography and synchrotron single-crystal crystallography are used to evaluate the role of organic substituent position on the optoelectronic properties of metal–organic chalcogenolates (MOChas). MOChas are crystalline 1D and 2D semiconducting hybrid materials that have varying optoelectronic properties depending on composition, topology, and structure. While MOChas have attracted much interest, small crystal sizes impede routine crystal structure determination. A series of constitutional isomers where the aryl thiol is functionalized by either methoxy or methyl ester are solved by small molecule serial femtosecond X-ray crystallography (smSFX) and single crystal rotational crystallography. While all the methoxy examples have a low quantum yield (0-1%), the methyl ester in the ortho position yields a high quantum yield of 22%. Here, the proximity of the oxygen atoms to the silver inorganic core correlates to a considerable enhancement of quantum yield. Four crystal structures are solved at a resolution range of 0.8–1.0 Å revealing a collapse of the 2D topology for functional groups in the 2- and 3- positions, resulting in needle-like crystals. Further analysis using density functional theory (DFT) and many-body perturbation theory (MBPT) enables the exploration of complex excitonic phenomena within easily prepared material systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗