Cyclophane-based two-dimensional polymer formed by an interfacial click reaction
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Roy, Indranil↗
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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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Differential localization patterns of pyruvate kinase isoforms in murine naïve, formative, and primed pluripotent states
Highlights: • PKM1/2 protein abundance is greater in formative mEpiLCs compared to naïve mESCs or primed mEpiSCs. The ratio of PKM1/2 is maintained. • PKM1/2 are localized in both nuclear and cytoplasmic regions relative to GAPDH and OCT4 across the pluripotent continuum. • PKM1 localization is strongly correlated to OCT4 localization, and moderately to GAPDH in formative mEpiLCs. • PKM1/2 localization is moderately correlated to OCT4 and GAPDH localization in naïve mESCs. • PKM1/2 localization are moderately correlated to GAPDH localization in primed mEpiSCs. Mouse embryonic stem cells (mESCs) and mouse epiblast stem cells (mEpiSCs) represent opposite ends of the pluripotency continuum, referred to as naïve and primed pluripotent states, respectively. These divergent pluripotent states differ in several ways, including growth factor requirements, transcription factor expression, DNA methylation patterns, and metabolic profiles. Naïve cells employ both glycolysis and oxidative phosphorylation (OXPHOS), whereas primed cells preferentially utilize aerobic glycolysis, a trait shared with cancer cells referred to as the Warburg Effect. Until recently, metabolism has been regarded as a by-product of cell fate, however, evidence now supports metabolism as being a driver of stem cell state and fate decisions. Pyruvate kinase muscle isoforms (PKM1 and PKM2) are important for generating and maintaining pluripotent stem cells (PSCs) and mediating the Warburg Effect. Both isoforms catalyze the final, rate limiting step of glycolysis, generating adenosine triphosphate and pyruvate, however, the precise role(s) of PKM1/2 in naïve and primed pluripotency is not well understood. The primary objective of this study was to characterize the cellular expression and localization patterns of PKM1 and PKM2 in mESCs, chemically transitioned epiblast-like cells (mEpiLCs) representing formative pluripotency, and mEpiSCs using immunoblotting and confocal microscopy. The results indicate that PKM1 and PKM2 are not only localized to the cytoplasm, but also accumulate in differential subnuclear regions of mESC, mEpiLCs, and mEpiSCs as determined by a quantitative confocal microscopy employing orthogonal projections and airyscan processing. Importantly, we discovered that the subnuclear localization of PKM1/2 changes during the transition from mESCs, mEpiLCs, and mEpiSCs. Finally, we have comprehensively validated the appropriateness and power of the Pearson's correlation coefficient and Manders's overlap coefficient for assessing nuclear and cytoplasmic protein colocalization in PSCs by immunofluorescence confocal microscopy. We propose that nuclear PKM1/2 may assist with distinct pluripotency state maintenance and lineage priming by non-canonical mechanisms. These results advance our understanding of the overall mechanisms controlling naïve, formative, and primed pluripotency.
60 APPLIED LIFE SCIENCES↗
Activity-induced migration of viscous droplets on a solid substrate
Active matter exploits motion to induce changes in shape and conformation via external input. Here, in this paper, we establish theoretically that viscous liquid droplets containing magnetic nanoparticles with frozen-in magnetic moments, sitting on a solid substrate and surrounded by an ambient gas phase, can deform and migrate under the influence of a magnetic torque. The effect arises because the collective rotation of the magnetic nanoparticles at the liquid–gas interface tilts the droplet away from a symmetric configuration, breaks the reflection symmetry with respect to the centre axis, and leads to a left–right asymmetry of the contact angles. A sufficiently strong magnetic torque leads the contact angles to overcome hysteresis effects leading the droplet to migrate. We develop a general framework to explain how symmetry-breaking affects droplet migration. Thus previous results of droplet spreading and migration can be recovered as special cases. Such droplets can be employed as agents in active surfaces and can move against gravity, chemical and thermal gradients, providing a mechanism that could be utilized by both industry and medicine.
Aggarwal, A.↗
Bootstrap current modeling in M3D-C1
Bootstrap current plays a crucial role in the equilibrium of magnetically confined plasmas, particularly in quasi-symmetric stellarators and in tokamaks, where it can represent bulk of the electric current density. Accurate modeling of this current is essential for understanding the magnetohydrodynamic (MHD) equilibrium and stability of these configurations. This study expands the modeling capabilities of M3D-C1, an extended-MHD code, by implementing self-consistent physics models for bootstrap current. It employs two analytical frameworks: a generalized Sauter model (Sauter et al. 1999 Phys. Plasmas vol. 6, no. 7, pp. 2834–2839), and a revised Sauter-like model (Redl et al. 2021 Phys. Plasmas vol. 28, no. 2, pp. 022502). The isomorphism described by Landreman et al. (2022 Phys. Rev. Lett. vol. 128, pp. 035001) is employed to apply these models to quasi-symmetric stellarators. The implementation in M3D-C1 is benchmarked against neoclassical codes, including NEO, XGCa and SFINCS, showing excellent agreement. These improvements allow M3D-C1 to self-consistently calculate the neoclassical contributions to plasma current in axisymmetric and quasi-symmetric configurations, providing a more accurate representation of the plasma behavior in these configurations. A workflow for evaluating the neoclassical transport using SFINCS with arbitrary toroidal equilibria calculated using M3D-C1 is also presented. This workflow enables a quantitative evaluation of the error in the Sauter-like model in cases that deviate from axi- or quasi-symmetry (e.g. through the development of an MHD instability).
fusion plasma↗
Emerging Potassium Metal Anodes: Perspectives on Control of the Electrochemical Interfaces
Potassium metal acts as the anode in emerging potassium metal batteries (KMBs). It further serves as the counter-electrode for potassium ion battery (KIB) half-cells, with its reliable performance being critical for assessing the working electrode material. This first-of-its-kind critical review focuses on the dual challenge of controlling the potassium metal-substrate and the potassium metal-electrolyte interface so as to prevent dendrites. The discussion begins with a comparison of the physical and chemical properties of K metal anodes versus the much oft studied Li and Na metal anodes. Based on established descriptions for root causes of dendrites, the problem should be less severe for K than for Li or Na, while in fact the opposite is observed. The key reason that the K metal surface rapidly becomes dendritic in common electrolytes is its unstable solid electrolyte interphase (SEI). An unstable SEI layer is defined as being non self-passivating. No SEI is perfectly stable during cycling, and all SEI structures are heterogenous both vertically and horizontally relative to the electrolyte interface. The difference between a "stable" and an "unstable" SEI may be viewed as the relative degree to which during cycling it thickens and becomes further heterogeneous. The unstable SEI on K metal leads to a number of interrelated problems, such as low cycling Coulombic efficiency (CE), a severe impedance rise, large overpotentials, and possibly electrical shorting; all of which have been reported to occur as early as in the first ten plating/stripping cycles. Many of the traditional "interface fixes" employed for Li and Na metal anodes, such as various artificial SEIs, surface membranes, barrier layers, the secondary separators, etc. have not been attempted or optimized for the case of K. This is an important area for further exploration, with an understanding that success may come harder than with Li due to K-based SEI reactivity with both ether and ester solvents. The second critical problem with K metal anodes is that they do not thermally or electrochemically wet a standard (untreated) Cu foil current collector. Published experimental and modeling research directly highlights the weak bonding between the K atoms and a Cu surface. Existing surface treatment approaches that achieve improved K wetting are discussed, along with the general design rules for future studies. Also discussed are geometry-based methods to tune nucleation, as well dual approaches where nucleation and SEI structure are tuned through complementary schemes to achieve extended half-cell and full battery stability. We hypothesize that K metal never achieves a planar wetting morphology even at cycle one making the dendrites "baked-in". We propose that classical thin films growth models, Frank van der Merwe (F-M), Volmer Weber (V-W), and Stranski-Krastanov (S-K) can be employed to describe early stage plating behavior. It is demonstrated that island-like V-W growth is the applicable description for the natural plating behavior of K on pristine Cu. Moving forward, there are three inter-related thrusts to be pursued: First, K salt - based electrolyte formulations have to mature and become further tailored to handle the increased reactivity of a metal rather than an ion anode. Second, the K-based SEI structure needs to be further understood and ultimately tuned to be less reactive. Third, the energetics of the K metal - current collector interface must be controlled to promote planar wetting/dewetting throughout cycling.
25 ENERGY STORAGE↗
Data-Driven Strategies for Accelerated Materials Design
The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.
36 MATERIALS SCIENCE↗
Exploring the impacts of conformer selection methods on ion mobility collision cross section predictions
The prediction of structure dependent molecular properties, such as collision cross sections as measured using ion mobility spectrometry, are crucially dependent on the selection of the correct population of molecular conformers. Here, we report an in-depth evaluation of multiple conformation selection techniques, including simple averaging, Boltzmann weighting, lowest energy selection, low energy threshold reductions, and similarity reduction. Generating 50,000 conformers each for 18 molecules, we used the In Silico Chemical Library Engine (ISiCLE) to calculate the collision cross sections for the entire dataset. First, we employed Monte Carlo simulations to understand the variability between conformer structures as generated using simulated annealing. Then we employed Monte Carlo simulations to the aforementioned conformer selection techniques applied on the simulated molecular property—the ion mobility collision cross section. Based on our analyses, we found Boltzmann weighting to be a good tradeoff between precision and theoretical accuracy. Combining multiple techniques revealed that energy thresholds and root-mean-squared deviation-based similarity reductions can save considerable computational expense while maintaining property prediction accuracy. Molecular dynamic conformer generation tools like AMBER can continue to generate new lowest energy conformers even after tens of thousands of generations, decreasing precision between runs. This reduced precision can be ameliorated and theoretical accuracy increased by running density functional theory geometry optimization on carefully selected conformers.
Nielson, Felicity F.↗
Hybrid-Lipid Bilayers Induce n -Alkyl-Chain Order in Reversed-Phase Chromatographic Surfaces, Impacting their Shape Selectivity for Aromatic Hydrocarbon Partitioning
Shape selectivity is important in reversed-phase liquid chromatographic separations, where stationary phases are capable of separating geometric isomers, thereby resolving solutes based on their three-dimensional structure or shape rather than other chemical differences. Numerous chromatographic studies have been carried out using n-alkyl-chain-modified columns to understand how molecular shape affects retention. For polycyclic aromatic hydrocarbons (PAHs), it was found that planar compounds were selectively retained over non-planar structures of comparable molecular weight on surfaces with longer n-alkyl chains, higher chain-density, or at lower temperatures, where selectivity likely arises with greater ordering of the n-alkyl chains. A limitation of these studies, however, is the small range of chain ordering that can be achieved and lack of a direct measure of the n-alkyl-chain order of the stationary phases. In this work, we employ a C 18 stationary phase modified with a monolayer of phospholipid as a means of significantly varying the n-alkyl chain order. These hybrid-supported lipid bilayers, which have previously been employed as membrane-like stationary phases for measuring lipophilicity, provide a unique approach to control n-alkyl chain ordering by varying the acyl chain length and degree of unsaturation of the phospholipid modifier. The degree of alkyl-chain order of the resulting modified surfaces is determined from the ratio of trans- versus gauche-conformers, measured in situ within individual porous particles by confocal Raman microscopy. This methodology was also used to assess the affinity of these surfaces for planar versus non-planar PAH molecules. The retention selectivity for the planar versus non-planar compounds, thus determined, was found to vary significantly and systematically with the degree of order of the acyl/alkyl chains in the hybrid-supported lipid bilayers. Furthermore, the investigation also demonstrates the utility of confocal Raman microscopy for interrogating the impact of solute partitioning on stationary-phase structure within porous chromatographic particles.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Shaping Nanobodies and Intrabodies against Proteoforms
Proteoforms expand genomic diversity and direct developmental processes. While high-resolution mass spectrometry has accelerated characterization of proteoforms, molecular techniques working to bind, and disrupt the function of specific proteoforms have lagged behind. In this study, we worked to develop intrabodies capable of binding specific proteoforms. Herein we employed a synthetic camelid nanobody library expressed in yeast to identify nanobody binders of different SARS-CoV-2 receptor binding domain (RBD) proteoforms. Importantly, employment of the positive and negative selection mechanism inherent to the synthetic system allowed for amplification of nanobody-expressing yeast that bind to the original (Wuhan strain RBD) but not the E484K (Beta variant) mutation. Nanobodies raised against specific RBD proteoforms were validated by yeast-2-hybrid analysis and sequence comparisons. These results provide a clear framework for development of proteoform-specific nanobodies and intrabodies.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Autocorrelation and Multifractal Detrended Fluctuation Analyses Reveal Superdiffusive Mass Transport in Solvent-Filled Nanoporous Media
Fluorescence fluctuation spectroscopy experiments were conducted to better understand the complex mass transport dynamics of organic molecules in liquid-filled nanoporous media. Anodic aluminum oxide (AAO) membranes incorporating 10 and 20 nm diameter cylindrical pores were employed as model materials. Nile red (NR) dye was used as a fluorescent tracer. The dye was dissolved separately in ethanol and toluene at a concentration of 20 nM and used to fill the membrane nanopores. Confocal fluorescence microscopy was employed to capture photon intensity time series data reflecting apparent diffusion of the dye within the pores. Autocorrelation of these data revealed that NR diffusion within the membranes occurred over a broad range of time scales. The autocorrelation decays were fit to a model for one-dimensional diffusion incorporating both fast and slow components having apparent diffusion coefficients, D f and D s , differing by a factor of ∼100. The fast mechanism was attributed to hindered bulk-like diffusion in the central pore cavity, while slow diffusion likely involved absorption of the dye to the pore surfaces. Unfortunately, important evidence of diffusion anomalies is lost in the broad autocorrelation decays obtained. The method of multifractal detrended fluctuation analysis (MF-DFA) was applied to the same data as a means to overcome this limitation. MF-DFA revealed that time series acquired from within the nanopores were multifractal and exhibited evidence of anomalous superdiffusion, likely resulting from the participation of a desorption-mediated diffusion mechanism. Monte Carlo simulations of time series modeling desorption-mediated diffusion in cylindrical nanopores provided support for this assignment. Here, the new knowledge gained affords an improved understanding of hydrocarbon dynamics within nanoporous oil and gas shales.
Diffusion↗
Examining the Influence of Silver(I) Ion Coordination Environment in Ionic Liquids on Olefin–Paraffin Separations using Inverse Gas Chromatography
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Tryon-Tasson, Nicholas↗
Microextraction-Single Particle-Inductively Coupled Plasma-Mass Spectrometry for the Direct Analysis of Nanoparticles on Surfaces
A novel employment of single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS) was developed, where a microextraction (ME) probe is used to sample nanoparticles from a surface and analyze them in a single analytical step. The effects of several parameters on the performance of ME-SP-ICP-MS were investigated, including the flow rate, choice of carrier solution, particle size, and the design of the microextraction probe head itself. The optimized ME-SP-ICP-MS technique was used to compare the extraction efficiency (EE, defined as the ratio of particles measured to particles deposited on the surface) of the commercial probe head to a newly designed SP polyether ether ketone (PEEK) probe head. The SP PEEK probe head was found to have increased EE compared to the commercial probe head (8.5 ± 3% vs 3.9 ± 3%, respectively). Increasing the carrier solution flow rate was found to decrease the total analysis time at the cost of decreasing EE. Extraction efficiencies for ME-SP-ICP-MS were typically 4–10%, which is similar to transport efficiencies (1–10%) for conventional SP-ICP-MS. Lastly, ME-SP-ICP-MS was employed for the analysis of nano- and microparticles. The sizes of gold nanoparticles, 30 ± 3 and 51 ± 1.9 nm (certified sizes), and iron-based microparticles, 1000 ± 50 nm (certified size), were accurately determined to be 32.2 ± 2.5, 50.8 ± 3.4, and 1030 ± 57 nm, respectively, by ME-SP-ICP-MS. Further, this work demonstrates the potential of ME-SP-ICP-MS for the direct analysis of particles on common collection surfaces (GSR tabs, carbon planchettes, etc.) while retaining spatial information on particle distribution across the surface.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Design and Function of α-Helix-Rich, Heme-Binding Peptide Materials
Peptide materials often employ short peptides that self-assemble into unique nanoscale architectures and have been employed across many fields relevant to medicine and energy. A majority of peptide materials are high in beta-sheet, secondary structure content, including heme-binding peptide materials. To broaden the structural diversity of heme-binding peptide materials, a small series of peptides were synthesized to explore the design criteria required for (1) folding into an alpha-helix structure, (2) assembling into a nanoscale material, (3) binding heme, and (4) demonstrating functions similar to that of heme proteins. One peptide was identified to meet all four criteria, including the heme protein function of CO binding and its microsecond-to-millisecond recombination rates, as measured by transient absorption spectroscopy. In conclusion, implications of new design criteria and peptide material function through heme incorporation are discussed.
59 BASIC BIOLOGICAL SCIENCES↗
Exploiting Colorimetry for Fidelity in Data Visualization
Advances in multimodal characterization methods fuel the generation of increasing immense hyper-dimensional data sets. Color mapping is employed for conveying higher dimensional data in two-dimensional (2D) representations for human consumption without relying on multiple projections. How one constructs these color maps, however, critically affects how accurately one perceives data. For simple scalar fields, perceptually uniform color maps and color selection have been shown to improve data readability and interpretation across research fields. In this work, we review core concepts underlying the design of perceptually uniform color maps and extend the concepts from scalar fields to two-dimensional vector fields and three-component composition fields frequently found in materials-chemistry research to enable high-fidelity visualization. Additionally, we develop the software tools PAPUC and CMPUC to enable researchers to utilize these colorimetry principles and employ perceptually uniform color spaces for rigorously meaningful color mapping of higher dimensional data representations. Last, we demonstrate how these approaches deliver immediate improvements in data readability and interpretation in microscopies and spectroscopies routinely used in discerning materials structure, chemistry, and properties.
36 MATERIALS SCIENCE↗
Closing the Gap Between Modeling and Experiments in the Self-assembly of Biomolecules at Interfaces and in Solution
Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly - biomineralizing peptides, hierarchical peptoids, and large protein assemblies, and will aid in the development of a new synthesis science for achieving precise molecular control in materials design
Sampath, Janani↗
Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations
This work investigates origins of cation ordering in double perovskites using first-principles theory computations combined with machine learning (ML) and causal relations. We have considered various oxidation states of A, A', B, and B' from the family of transition metal ions to construct a diverse compositional space. A conventional framework employing traditional ML classification algorithms such as Random Forest (RF) coupled with appropriate features including geometry-driven and key structural modes leads to accurate prediction (~98%) of A-site cation ordering. We have evaluated the accuracy of ML models by employing analyses of decision paths, assignments of probabilistic confidence bound, and finally a direct non-Gaussian acyclic structural equation model to investigate causality. Our study suggests that structural modes are crucial for classifying layered, columnar, and rock-salt ordering. The charge difference between A and A' is the most important feature for predicting clear layered ordering, which in turn depends on the B and B' charge separation. We have also designed mathematical relationships with these features to derive energy differences to form clear layered ordering. Here, the trilinear coupling between tilt, in-phase rotation, and A-site antiferroelectric displacement in the Landau free-energy expansion becomes the necessary condition behind formation of A-site cation ordering.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Understanding the Control of Speciation of Molybdenum Oxides in MFI-Type Zeolites
Metal oxide-impregnated zeolites are employed in a wide variety of catalytic reactions, including methane dehydroaromatization (MDA). The most studied catalysts for MDA are Mo carbides supported on H-ZSM-5, formed through the carburization of Mo-oxide-loaded H-ZSM-5. A complete structural understanding of these materials has not yet been achieved, limiting the potential for rational catalyst design for improved performance. We hereby pursue experimental and theoretical investigations of these catalyst precursors to uncover rational design principles. We employ temperature-programmed oxidation and extended X-ray absorption fine-structure experiments, density functional theory calculations, and QuantEXAFS analysis to unveil Mo-oxide speciation in H-ZSM-5. Finally, we demonstrate that Mo-oxides exist within these systems as a combination of various motifs, and the relative abundance of these species is controlled through tailored preparation methods. The synergies exploited in this work may be leveraged in other related catalysts. The conclusions drawn are applicable to other relevant applications of zeolite-supported metal oxides.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Molecular Layer Etching of Metalcone Films Using Lithium Organic Salts and Trimethylaluminum
Advances in semiconductor device manufacturing are limited by our ability to precisely add and remove thin layers of material in multistep fabrication processes. Recent reports on atomic layer etching (ALE) have provided the means for the precise removal of inorganic thin films deposited by atomic layer deposition (ALD), opening new avenues for nanoscale device design. Here, we report on a new technique for the precise removal of metal organic thin films deposited by molecular layer deposition (MLD), which we term molecular layer etching. This etching process employs sequential exposures of lithium organic salt (LOS) and trimethylaluminum (TMA) precursors to produce self-limiting etching behavior. We employ quartz crystal microbalance experiments to demonstrate (i) etching of alucone films preloaded with LOS upon TMA exposures and (ii) layer-by-layer etching of alucone films using alternating exposures of LOS and TMA. We also identify the selectivity of these etching mechanisms. We probe the mechanism for the layer-by-layer etching of alucone using a quartz crystal microbalance and Fourier transform infrared spectroscopy and identify that the etching proceeds via heterolytic cleaving of Al-O bonds in alucone upon LOS exposure followed by methylation to produce volatile species upon TMA exposure. The etching process results in the removal of 0.4 nm/cycle of alucone at 160 °C and up to 3.6 nm/cycle of alucone at 266 °C in ex situ etching experiments on silicon wafers. This halogen-free etching process enables etching of MLD films and provides new fabrication pathways for the control of material geometries at the nanoscale.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗