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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mixing Matters: Nanoscale Heterogeneity and Stability in Metal Halide Perovskite Solar Cells

The structural stability of the metal halide perovskite (MHP) absorber material is crucial for the long-term solar cell stability in this thin-film photovoltaic technology. Here, we use mixed A-site FA0.83Cs0.17PbI3 to demonstrate that nanoscale compositional heterogeneity can serve as initiation sites for more macroscale, irreversible phase segregation, which causes device performance degradation. Probing compositional heterogeneity on length scales that has not been detected with conventional characterization techniques, we analyze the tetragonal to cubic phase transition behavior to indirectly determine the level of nanoscale compositional heterogeneity in the initial films. Further, we show that the thermal annealing conditions of the MHP layer during film processing influence the initial nanoscale compositional heterogeneity, and changing these processing conditions can be used to improve the device performance stability. The insights into structural degradation mechanisms initiated by nanoscale compositional heterogeneity and the proposed mitigation strategies will help guide the way toward long-term stable MHP solar cells.

14 SOLAR ENERGY↗
NASA NTRS1982

A parameterized model for the evolution of isotopic heterogeneities in a convecting system

It is experimentally shown that, although steady convective flows are efficient means to heterogeneity within a single cell, they do not produce a dispersal of heterogeneous material over scales that are large by comparison to their depth, which requires that the flow be time-dependent on a time scale comparable to the overturn time. Convection in an internally heated layer does possess this property, and numerical solutions are presently used to study the way in which it disperses a set of neutrally bouyant particles initially confined to a small space. The derived concept of effective diffusivity is applied to the isotopic evolution of the Sm-Nd and Rb-Sr systems, with spatial variations generated by horizontal variations in degree of melting 1.8 billion years ago.

Richter, F. M.↗
DOE OSTI2023

The use of synchrotron X-ray fluorescent imaging to study distribution and content of elements in chemically fixed single cells: a case study using mouse pancreatic beta-cells

Synchrotron X-ray fluorescence microscopy (SXRF) presents a valuable opportunity to study the metallome of single cells because it simultaneously provides high-resolution subcellular distribution and quantitative cellular content of multiple elements. Different sample preparation techniques have been used to preserve cells for observations with SXRF, with a goal to maintain fidelity of the cellular metallome. In this case study, mouse pancreatic beta-cells have been preserved with optimized chemical fixation. We show that cell-to-cell variability is normal in the metallome of beta-cells due to heterogeneity and should be considered when interpreting SXRF data. Additionally, we determined the impact of several immunofluorescence (IF) protocols on metal distribution and quantification in chemically fixed beta-cells and found that the metallome of beta-cells was not well preserved for quantitative analysis. However, zinc and iron qualitative analysis could be performed after IF with certain limitations. To help minimize metal loss using samples that require IF, we describe a novel IF protocol that can be used with chemically fixed cells after the completion of SXRF.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Advances in Cell Wall Matrix Research with a Focus on Mixed-Linkage Glucan

Mixed β(1,3;1,4)-linkage glucan (MLG) is commonly found in the monocot lineage, at particularly high levels in the Poaceae family, but also in the evolutionally distant genus, Equisetum. MLG has several properties that make it unique from other plant cell wall polysaccharides. It consists of β1,4-linked polymers of glucose interspersed with β1,3-linkages, but the presence of β1,3-linkages provides quite different physical properties compared to its closest form of the cell wall component, cellulose. The mechanisms of MLG biosynthesis have been investigated to understand whether single or multiple enzymes are required to build mixed linkages in the glucan chain. Currently, MLG synthesis by a single enzyme is supported by mutagenesis analyses of cellulose synthase-like F6, the major MLG synthase, but further investigation is needed to gather mechanistic insights. Because of transient accumulation of MLG in elongating cells and vegetative tissues, several hypotheses have been proposed to explain the role of MLG in the plant cell wall. Studies have been carried out to identify gene expression regulators during development and light cycles as well as enzymes involved in MLG organization in the cell wall. A role of MLG as a storage molecule in grains is evident, but the role of MLG in vegetative tissues is still not well understood. Characterization of a cell wall component is difficult due to the complex heterogeneity of the plant cell wall. Furthermore, as detailed in this review, recent exciting research has made significant impacts in the understanding of MLG biology in plants.

Transglucosylase/hydrolase (XTH)↗
NASA NTRS1990

Modelling a nickel-cadmium battery as a heterogeneous device

A common failure mode for nickel-cadmium batteries involves a nonuniform degradation of the performance of individual cells. This behavior is manifested as a divergence in voltage during cyclic operation. An empirically based performance model is presented which simulates a battery composed of heterogeneous cells. Battery performance is studied as a function of the distribution of cell performance characteristics. The technique has successfully predicted cell voltage divergence behavior observed during cycle life testing. It is noted that this work enhances understanding of life-test results and may improve life-test design and data analysis.

Glueck, Peter R.↗
NASA NTRS1987

Morphometrics of cellular damage in mice testis receiving X-ray and high-energy particle irradiation

Murine tests were exposed to single, low doses of either X-ray, helium, or argon radiation. Animals were sacrificed seventy-two hours later. Testes were fixed for transmission electron microscopy (TEM) and sectioned at either 60 nm for TEM observation or at 2 micron for counting using routine light microscope methods. Counts of the total population of surviving spermatogonia, including all type A cells, intermediate, and type B cells, were taken from tubule cross sections identified as Stage 6 and Stage 1 according to spermatogonial configuration. The surviving fraction of spermatogonia as compared to control, S/S sub o, was calculated for each dose. For both ions and X-rays, there was a rapid decline in survival at dose levels of .10 to .15 Gy in Stage 6 tubules. This was followed by a more gradual decrease in population. At higher doses, 0.30 Gy for argon and 0.80 Gy for helium and X-rays, the cell survival rates declined rapidly. Pre-leptotene spermatocytes in Stage 1 tubules exhibited a different survival curve indicating the extreme radio-sensitivity of type B spermatogonia. Data verify that the seminiferous tubules are composed of a heterogeneous population of cells with different radio-sensitivities and that these differences are manifested even at very low doses.

Sapp, Walter J.↗
DOE OSTI2026

Multimodal Nanoscale Mapping of Local Structure and CO 2 Adsorption in Metal–Organic Frameworks

Diamine functionalization of the metal−organic framework Mg 2 (dobpdc) (dobpdc 4− = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate) significantly enhances its selectivity for CO 2 capture from flue gases and air. The structure and CO 2 capacity of such materials are typically assessed using bulk techniques that rely on averaging signal over large ensembles of unit cells, obscuring local heterogeneities, such as variations in CO 2 occupancy across individual nanocrystals. To resolve this limitation, we demonstrate a multimodal, nanoscale characterization of Mg 2 (dobpdc) appended with 1,3-diaminopropane. By employing recently developed characterization techniques at progressively smaller length scales, we uncover insights from correspondingly smaller populations of unit cells. First, we use parallel-beam 3D electron diffraction (3D ED) to identify a prominent expansion in lattice parameters upon desorption of CO 2 , as observed at the level of single nanocrystals. Second, we use convergent-probe 4D scanning transmission electron microscopy (4D-STEM) to quantify associated differences in lattice strain as a function of gas loading and diamine appending. These measurements sample small subvolumes within individual nanocrystals. Finally, we apply infrared scattering scanning near-field optical microscopy (IR s- SNOM) to confirm variable CO 2 chemisorption across adsorption sites at the surface of single nanocrystals. This multimodal, multiscale approach allows us to map heterogeneity within individual nanocrystals. Collectively, these findings emphasize the importance of local, nanoscale characterization of metal−organic frameworks in revealing previously unresolvable features that impact their performance.

Karstens, Sarah L. [University of California, Berk↗
DOE OSTI2023

A fast Fourier transform-based solver for elastic micropolar composites

This work presents a spectral micromechanical formulation for obtaining the full-field and homogenized response of elastic micropolar composites. The algorithm relies on a coupled set of convolution integral equations for the micropolar strains, where periodic Green’s operators associated with a linear homogeneous reference medium are convolved with functions of the Cauchy and couple stress fields that encode the material’s heterogeneity, as well as any potential material nonlinearity. Such convolution integral equations take an algebraic form in the reciprocal Fourier space that can be solved iteratively. In this vein, the fast Fourier transform (FFT) algorithm is leveraged to accelerate the numerical solution, resulting in a mesh-free formulation in which the periodic unit cell representing the heterogeneous material can be discretized by a regular grid of pixels in two dimensions (or voxels in three dimensions). For verification, the numerical solutions obtained with the micropolar FFT solver are compared with analytical solutions for a matrix with a dilute circular inclusion subjected to plane strain loading. The developed computational framework is then used to study length-scale effects and effective (micropolar) moduli of composites with various topological configurations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Physics-Based Model to Represent Membrane-Electrode Assemblies of Solid-Oxide Fuel Cells Based on Gadolinium-Doped Ceria

This paper reports a physics-based model that predicts membrane-electrode assembly (MEA) performance of solid-oxide fuel cells (SOFCs) with Ce 0.9 Gd 0.1 O 2− δ (GDC10) electrolyte membranes. The paper derives self-consistent thermodynamic and transport properties for GDC1o mobile charged defects (oxide vacancies and reduced-ceria small polarons) by fitting published measurements of oxygen non-stoichiometry and conductivity over ranges of temperature and O 2 partial pressures. The button-cell model is applied to evaluate how mixed ionic-electronic conductivity influences the performance of an SOFC MEA with a GDC10 electrolyte sandwiched between a porous, composite Ni-GDC10 anode and a porous, composite cathode of Sm 0.5 Sr 0.5 CoO 3− δ (i.e., SSC) and GDC10. SSC properties are also derived by fitting published conductivity and oxygen non-stoichiometry measurements. Mixed conductivity of GDC10 and competing charge transfer reactions at both electrodes reduce open circuit voltages due to leakage current and buildup of defect concentrations at electrode-electrolyte interfaces. To fit polarization data, the button-cell model includes heterogeneous reaction rates for defect incorporation on the GDC10 surface along with Butler–Volmer expressions derived for competing charge transfer reaction rates from rigorous analyses assuming rate-limiting, elementary charge transfer reactions for each electrode. The calibrated MEA model can support rigorous SOFC modeling with GDC10 electrolytes over the range of conditions within a fully operating cell.

Electrochemistry↗
DOE OSTI2023

Response of ZrC to swift heavy ion irradiation

Zirconium carbide (ZrC) is commonly used for energy sector research, as well as a surrogate for the proposed advanced nuclear fuel candidate uranium carbide. Here this study investigates structural modifications to nanocrystalline and microcrystalline ZrC resulting from dense electronic excitations induced by swift heavy ion exposure. Samples were irradiated with 946 MeV Au ions to various fluences up to 6 × 10 13 ions cm –2 and characterized using synchrotron-based x-ray diffraction. The evolution of the unit-cell parameter and heterogeneous microstrain were evaluated as a function of fluence and compared with those of nanocrystalline and microcrystalline CeO 2 (a surrogate for UO 2 fuel) irradiated under identical conditions. Distinct differences were observed in the radiation responses of the carbide and oxide across both grain sizes. Most notably, microcrystalline ZrC exhibits swelling characterized by two distinct regimes, which does not result in saturation at the ion fluences achieved. This contrasts with CeO 2 , which exhibits the well-documented direct-impact defect accumulation mechanism, reaching a steady-state saturation of swelling at higher fluences. Nanocrystalline CeO 2 undergoes more pronounced swelling compared with microcrystalline CeO 2 , in contrast to nanocrystalline ZrC, which exhibits only minimal unit-cell changes. These results demonstrate that swift heavy ion-induced structural changes can be quite different in carbides and oxides, which must be considered when extrapolating fission-fragment type damage in current fuels to advanced fuels.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Achieving geometric accuracy in FFT-based micromechanical models using conformal grid

Owing to its efficiency, simplicity and robustness, the FFT-based method has become the standard for computation of mechanical fields in a heterogeneous periodic unit cell. One of the main disadvantages of the FFT-based method is the inaccurate representation of the initial microstructure on a regular grid of voxels, which can be alleviated through the use of distorted initial grids. Here, in this paper, a method for generation of distorted initial grids conforming to the microstructural features (e.g. straight/curved boundaries) is proposed. The method determines the positions of the grid nodes in the initial configuration by solving a system of springs connecting the nodes. Microstructures consisting of layers, Voronoi tessellation and circular/spherical inclusions are considered, and mechanical fields simulated using the FFT-based method. It is found that distorted initial grids, conforming to the microstructural features, lead to more accurate mechanical fields in comparison to the corresponding non-distorted initial grid solution. The effect of initial grid distortion on the convergence of the FFT-based method is analyzed and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Solid-state rigid-rod polymer composite electrolytes with nanocrystalline lithium ion pathways

A critical challenge for next-generation lithium-based batteries lies in development of electrolytes that enable thermal safety along with use of high-energy-density electrodes. We describe molecular ionic composite (MIC) electrolytes based on an aligned liquid crystalline polymer combined with ionic liquids and concentrated Li salt. This high strength (200 MPa) and non-flammable solid electrolyte possesses outstanding Li + conductivity (1 mS cm -1 at 25 °C) and electrochemical stability (5.6 V vs Li|Li + ) while suppressing dendrite growth and exhibiting low interfacial resistance (32 Ω cm 2 ) and overpotentials (≤ 120 mV @ 1 mA cm -2 ) during Li symmetric cell cycling. A heterogeneous salt doping process modifies a locally ordered polymer-ion assembly to incorporate an inter-grain network filled with defective LiFSI & LiBF 4 nanocrystals, strongly enhancing Li + conduction. Furthermore, this modular material fabrication platform shows promise for safe and high-energy-density energy storage and conversion applications, incorporating the fast transport of ceramic-like conductors with the superior flexibility of polymer electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
NASA NTRS2005

Depth-varying density and organization of chondrocytes in immature and mature bovine articular cartilage assessed by 3d imaging and analysis

Articular cartilage is a heterogeneous tissue, with cell density and organization varying with depth from the surface. The objectives of the present study were to establish a method for localizing individual cells in three-dimensional (3D) images of cartilage and quantifying depth-associated variation in cellularity and cell organization at different stages of growth. Accuracy of nucleus localization was high, with 99% sensitivity relative to manual localization. Cellularity (million cells per cm3) decreased from 290, 310, and 150 near the articular surface in fetal, calf, and adult samples, respectively, to 120, 110, and 50 at a depth of 1.0 mm. The distance/angle to the nearest neighboring cell was 7.9 microm/31 degrees , 7.1 microm/31 degrees , and 9.1 microm/31 degrees for cells at the articular surface of fetal, calf, and adult samples, respectively, and increased/decreased to 11.6 microm/31 degrees , 12.0 microm/30 degrees , and 19.2 microm/25 degrees at a depth of 0.7 mm. The methodologies described here may be useful for analyzing the 3D cellular organization of cartilage during growth, maturation, aging, degeneration, and regeneration.

Non-NASA Center↗
NASA NTRS2018

The Optoretinogram at 38

As early as 1980, Donald MacLeod coined the term "optoretinogram" to describe a change in the near-infrared reflectance of the retina following visual stimulation. The hope that such signals might exist was stimulated by an observation of a change in the infrared transmission of isolated toad retinae. This signal showed a time course of several seconds, much slower than the electroretinogram, and was thought to be related to some chemical step in the phototransduction cascade. During the early 1980's, we made several attempts to observe such signals in vivo in humans, without success. In the intervening decades, however, researchers have succeeded in observing what are now known as "intrinsic optical signals" or IOSs. Responses from single photoreceptors (obtained using adaptive optics) are surprisingly heterogenous, with some cells increasing their reflectance in response to visual stimulation, while others decrease. This talk will review our early work, survey recent findings, and consider various theories for the origin of the effect.

noninvasive monitoring↗
DOE OSTI2021

A robust method of nuclei isolation for single-cell RNA sequencing of solid tissues from the plant genus Populus

Single-cell transcriptome analysis has been extensively applied in humans and animal models to uncover gene expression heterogeneity between the different cell types of a tissue or an organ. It demonstrated its capability to discover key regulatory elements that determine cell fate during developmental programs. Single-cell analysis requires the isolation and labeling of the messenger RNA (mRNA) derived from each cell. These challenges were primarily addressed in mammals by developing microfluidic-based approaches. For plant species whose cells contain cell walls, these approaches have generally required the generation of isolated protoplasts. Many plant tissues’ secondary cell wall hinders enzymatic digestion required for individual protoplast isolation, resulting in an unequal representation of cell types in a protoplast population. This limitation is especially critical for cell types located in the inner layers of a tissue or the inner tissues of an organ. Consequently, single-cell RNA sequencing (scRNA-seq) studies using microfluidic approaches in plants have mainly been restricted to Arabidopsis roots, for which well-established procedures of protoplast isolation are available. Here we present a simple alternative approach to generating high-quality protoplasts from plant tissue by characterizing the mRNA extracted from individual nuclei instead of whole cells. We developed the protocol using two different plant materials with varying cellular complexity levels and cell wall structure, Populus shoot apices, and more lignified stems. Using the 10× Genomics Chromium technology, we show that this procedure results in intact mRNA isolation and limited leakage, with a broad representation of individual cell transcriptomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Correlated mechanochemical maps of Arabidopsis thaliana primary cell walls using atomic force microscope infrared spectroscopy

Spatial heterogeneity in composition and organisation of the primary cell wall affects the mechanics of cellular morphogenesis. However, directly correlating cell wall composition, organisation and mechanics has been challenging. To overcome this barrier, we applied atomic force microscopy coupled with infrared (AFM-IR) spectroscopy to generate spatially correlated maps of chemical and mechanical properties for paraformaldehyde-fixed, intact Arabidopsis thaliana epidermal cell walls. AFM-IR spectra were deconvoluted by non-negative matrix factorisation (NMF) into a linear combination of IR spectral factors representing sets of chemical groups comprising different cell wall components. This approach enables quantification of chemical composition from IR spectral signatures and visualisation of chemical heterogeneity at nanometer resolution. Cross-correlation analysis of the spatial distribution of NMFs and mechanical properties suggests that the carbohydrate composition of cell wall junctions correlates with increased local stiffness. Together, our work establishes new methodology to use AFM-IR for the mechanochemical analysis of intact plant primary cell walls.

59 BASIC BIOLOGICAL SCIENCES↗