Metal Oxide Heterostructure Array via Spatially Controlled-Growth within Block Copolymer Templates.

Sequential infiltration synthesis (SIS) is a route to the precision deposition of inorganic solids in analogy to atomic layer deposition but occurs within (vs upon) a soft material template. SIS has enabled exquisite nanoscale morphological complexity in various oxides through selective nucleation in block copolymers templates. However, the earliest stages of SIS growth remain unresolved, including the atomic structure of nuclei and the evolution of local coordination environments, before and after polymer template removal. We employed In K-edge extended X-ray absorption fine structure and atomic pair distribution function analysis of high-energy X-ray scattering to unravel (1) the structural evolution of InOxHy clusters inside a poly(methyl methacrylate) (PMMA) host matrix and (2) the formation of porous In2O3 solids (obtained after annealing) as a function of SIS cycle number. Early SIS cycles result in InOxHy cluster growth with high aspect ratio, followed by the formation of a three-dimensional network with additional SIS cycles. That the atomic structures of the InOxHy clusters can be modeled as multinuclear clusters with bonding patterns related to those in In2O3 and In(OH)3 crystal structures suggests that SIS may be an efficient route to 3D arrays of discrete-atom-number clusters. Annealing the mixed inorganic/polymer films in air removes the PMMA template and consolidates the as-grown clusters into cubic In2O3 nanocrystals with structural details that also depend on SIS cycle number.

Three-Dimensional Visualization of Whole Synapses by Stem Tomography

Today’s nanofabrication techniques require multistep and costly processes in order to fabricate complex, multi-materials nanostructures. Performing atomic layer deposition (ALD) within polymeric templates can offer a simple solution for nanostructure fabrication. In this process, named sequential infiltration synthesis (SIS), high partial pressures and long exposures times lead to inorganic materials growth within polymers. Sequential polymer removal results in polymer-templated inorganic nanostructure. While SIS shows great potential in fabricating large variety of structures, it is currently limited to a single material growth process.In our research, we utilized SIS properties to achieve two different objectives: selective spatial growth and homogenous growth for doped materials. We demonstrated, for the first time, multi-material SIS process with control over the spatial location of each material and fabricate heterostructure nanorods and nanowires. Doping materials were fabricated using simultaneously growth of two precursors.We studied SIS within self-assembled block copolymer (BCP) films and electrospun polymer fibers and developed multi-material SIS, where two metal oxides are grown together in a single process, with precise control over their location within the polymer template. We used cylinder forming poly (styrene-block-methyl methacrylate) (PS-b-PMMA) films and electrospun PMMA as the polymeric template and DEZ (diethyl zinc), TMA (trimethyl aluminum) as the organometallic precursors. We achieved control over the growth location of each metal oxide by tuning the organometallic precursors diffusion time, forming heterostructures after polymer removal. A short exposure of the first precursor resulted in a limited growth only at the outer part of the polymer, while a long exposure of the second precursor enabled it to reach the full depth of the polymer besides the section which was already occupied by the first precursor. An exposure to water completed the cycle. We demonstrated this process on BCP films to achieve AlOx-ZnO nanorods arrays (Figure 1A-B) and on polymer fibers to achieve AlOx-ZnO fibers (Figure 1C-D). We performed structural characterization using scanning and transmission electron microscopy (SEM and TEM, respectively) to characterize the nanowires and nanorods as well as three-dimensional characterization scanning TEM (STEM) tomography and energy-dispersive X-ray spectroscopy (EDS) STEM tomography in order to probe the structure and the chemical composition in 3D. To fabricate doped materials, we applied SIS process with TMA and DEZ in the same exposure within homopolymer film. We preformed SEM-EDS to quantify each element and evaluate the doping percentage. This research opens new pathways for multi-materials nano scale structure fabrication through ALD-based growth within polymers.Figure 1 caption: AlOx-ZnO nanorods array: cross-sectional views using A) EsB detector and B) secondary electron in-lens detector. AlOx-ZnO fibers C) EDS map D) HAADF image [1] R. Azoulay et al., Small, 15, 1904657 (2019).[2] R. Azoulay et al., ACS Appl. Nano Mater., 5, 7228 (2022). Figure 1

Photocatalytic self-cleaning coatings with a high surface area are important for a wide range of applications, including optical coatings, solar panels, mirrors, etc. Here, we designed a highly porous TiO2 coating with photoinduced self-cleaning characteristics and very high hydrophilicity. This was achieved using the swelling-assisted sequential infiltration synthesis (SIS) of a block copolymer (BCP) template, which was followed by polymer removal via oxidative thermal annealing. The quartz crystal microbalance (QCM) was employed to optimize the infiltration process by estimating the mass of material infiltrated into the polymer template as a function of the number of SIS cycles. This adopted swelling-assisted SIS approach resulted in a smooth uniform TiO2 film with an interconnected network of pores. The synthesized film exhibited good crystallinity in the anatase phase. The resulting nanoporous TiO2 coatings were tested for their functional characteristics. Exposure to UV irradiation for 1 h induced an improvement in the hydrophilicity of coatings with wetting angle reducing to unmeasurable values upon contact with water droplets. Furthermore, their self-cleaning characteristics were tested by measuring the photocatalytic degradation of methylene blue (MB). The synthesized porous TiO2 nanostructures displayed promising photocatalytic activity, demonstrating the degradation of approximately 92% of MB after 180 min under ultraviolet (UV) light irradiation. Thus, the level of performance was comparable to the photoactivity of commercial anatase TiO2 nanoparticles of the same quantity. Our results highlight a new robust approach for designing hydrophilic self-cleaning coatings with controlled porosity and composition.

Nanoscale patterning of inorganics is crucial for the fabrication of advanced electronic, photonic, and energy devices. The emerging sequential infiltration synthesis (SIS) method fabricates nanofeatures by block-selective vapor-phase growth in block copolymer templates with tunable patterns. Yet, SIS has been demonstrated mainly for Al2O3 and a few other metal oxides, while deriving metal nanostructures from a single SIS process is a challenge. Here, we present SIS of the Ru metal in polystyrene-block-polymethyl methacrylate (PS-b-PMMA) templates without any pretreatment, using alternating infiltration of RuO4 and H2. RuO4 interacts selectively and strongly with the aromatic C═C and C–H groups in PS, leaving the PMMA domains inert. Density functional theory calculations corroborate that the PS–RuO4 interaction is energetically favorable, with a calculated interaction energy of −1.65 eV, whereas for PMMA–RuO4, the calculated energy of −0.05 eV indicates an unfavorable interaction. Morphological analysis on the di-BCP after the RuO4-H2 process indicates an increase in contrast as a function of SIS cycles and templated Ru incorporation. The crystalline nature of the Ru deposits is confirmed using grazing incidence wide-angle X-ray scattering. Plasma-aided removal of the organic components yields Ru nanolines with lateral dimensions of ca 20 nm. We further highlight the broad potential of RuO4 as a reactant for SIS by generating RuO2 nanopatterns via alternating RuO4 and methanol infiltration.

Atomic resolution low-angle bright-field (LABF) scanning transmission electron-microscope (STEM) images and high-angle bright-field (HABF) STEM images of [011]-orientated Si have been experimentally obtained together with high-angle annular dark-field (HAADF) STEM images. The contrast formation mechanisms of the LABF STEM and HABF STEM images are examined in comparison with HAADF STEM images. The HABF STEM images independent of defocus and thickness have spatial resolution comparable with HAADF STEM images, and are shown to be given as a simple convolution under the non-dispersion approximation of localized Bloch waves.

Strain is a crucial factor that influences the physicochemical properties of nanoparticles. Being able to precisely measure strain is important in understanding the intrinsic mechanism of the enhanced performance of nanoparticles. Techniques that have been developed for strain analysis using scanning transmission electron microscopy (STEM) images can be categorized into diffraction-based method and imaging-based method. Here, using image simulation techniques, it is found that the measured two-dimensional (2D) displacements from annular dark field (ADF) STEM images of a nanoparticle are a good approximation to a projection of the actual three-dimensional (3D) displacements. A methodology for deformation analysis is presented which is based on the detection of atomic columns from atomic-resolution STEM images in real space. Elastic deformation parameters such as strain are usually defined on the basis of a continuum of deformation. The appropriateness of various deformation parameters for atomic-scale investigation on STEM images is explored and a method for determining these is presented. We found that the local lattice parameter and principal strain components are the most physically meaningful parameters to express the materials distortion behaviour. Apart from the local lattice parameter, the other deformation parameters such as normal strains, shear strains and displacements, heavily rely on the choice of reference lattice. It is also found that different reference grids add a series of uniform offsets to these strain variations. Finally, this approach is applied to a PtCo3 bimetallic nanoparticle to quantify its deformation behaviour.

The endoplasmic reticulum (ER) is a highly dynamic organelle that undergoes significant morphological alterations in response to cellular stress. While conventional transmission electron microscopy (TEM) has provided valuable insights into these changes, such as the formation of crystalloid‐ER and ER whorls, obtaining comprehensive three‐dimensional (3D) information on these large structures within their cellular context has remained a challenge. To overcome these limitations, this study introduces an innovative application of dual‐axis scanning transmission electron microscopy (STEM) tomography to investigate ER morphology under stress conditions in human embryonic kidney (HEK) cells overexpressing the cation channel polycystin‐2 (PC‐2). Benefitting from high‐resolution, increased depth‐of‐focus, and reduced aberrations, STEM tomography enabled the detailed 3D reconstruction of large cellular subvolumes, providing unprecedented views of stress‐induced ER structures. Our findings reveal distinct ultrastructural details of both crystalloid‐ER and ER whorls. Crystalloid‐ER exhibited a tubular architecture with potential interconnectedness, while ER whorls displayed a lamellar organisation and distinct membrane curvature. We observed the co‐occurrence of these distinct smooth ER (sER) morphotypes within the same cell, yet they remained spatially separated, suggesting potential functional specialisation. Furthermore, we identified direct membrane contacts in mixed morphotypes, hinting at a shared origin or dynamic relationship between these structures. The study also elucidated the interactions of these organised smooth ER (OSER) structures with other organelles, such as mitochondria (MAM sites) and vesicles. In summary, the presented ultra‐structural insights have a significant impact on our understanding of stress‐related ER morphology changes. The ability to visualise the intricate 3D architecture and spatial relationships of these structures provides novel perspectives on the ER's adaptive responses to stress, including potential roles in lipid and protein biosynthesis and intracellular communication. These findings underscore the power of dual‐axis STEM tomography for elucidating complex organellar organisation and dynamics in their native cellular context.

Aberration‐corrected scanning transmission electron microscopy (STEM) is providing previously unattainable views of materials at the atomic scale. The quality of STEM data is now often limited by environmental and experimental factors instead of instrument factors (e.g. electron optics). Some of these environmental limitations can be overcome by collecting and processing STEM data using new data science techniques. These techniques expose new atomic‐scale materials information by improving our ability to measure atomic column positions, 3D structure, single point defects, and atomic‐scale composition. The precision in locating atomic column positions in STEM images is fundamentally limited by the image signal to noise ratio (SNR), but typically practical limits including sample and microscope instabilities that produce distortions in STEM images are encountered before reaching the SNR limit. We have developed a non‐rigid registration (NRR) technique that corrects image distortion of all length scales and enables averaging to enhance the SNR.[1–2] Sub‐pm precision images of single crystal materials have been achieved by NRR and averaging high angle annular dark field (HAADF) STEM images. NRR has allowed measurements of pm‐scale bond length variations of Pt nanocatalyst atoms that may help explain their catalytic activity. Figure 1 shows NRR and averaged HAADF STEM data of a Pt nanocatalyst on an alumina support from a side‐view that exhibits ˜2 pm precision. Displacement measurements of each atomic column position reveal moderate Pt surface bond length contraction and strong but localized strain of Pt atoms near nanoparticle‐support interface. Some of the interface strain is transferred up the twin boundary. Determining the three dimensional atomic structure of materials from two dimensional S/TEM images is a major hurdle. The standardless atom counting technique is one promising route to measure local sample thickness by quantitatively comparing experimental and simulated HAADF STEM images and can be used to deduce 3D structure.[3] Unlike previous examples, NRR and averaging STEM images have allowed standardless atom counting with the uncertainty no longer dominated by Poisson noise [1]. This should allow the unique determination of the number of atoms in atomic columns, although this has not yet been demonstrated due to other sample limitations. Point defects are critical to the properties of a wide range of materials, but imaging single defects is challenging. Quantitative STEM has allowed imaging single substitutional and interstitial dopant impurity atoms[4], but experimentally imaging single vacancies has remained elusive. We have used HAADF STEM frozen phonon multislice simulations to predict the detectability of La vacancies in LaMnO 3 by the reduced atomic column intensity and atomic column distortions around the vacancy. NRR and averaging HAADF STEM images of LaMnO 3 improves the SNR and the image precision sufficiently to potentially detect single La vacancies. Experimental images contain candidate single La vacancies that have local atomic column distortions and intensity variations which match simulated predictions. Atomic‐resolution composition maps can be created using STEM energy dispersive x‐ray spectroscopy (EDS) spectrum imaging (SI). However, long total dwell times that may introduce spatial distortions are required because of low x‐ray production and collection efficiency. The most common approach to minimize distortions is to sum multiple SIs using online drift‐correction software that discards the individual SIs and HAADF images. The quality of EDS SIs of a Nd 2/3 TiO 3 sample was improved by saving the simultaneously acquired raw HAADF and EDS SI series, and applying post acquisition NRR and averaging. The resulting elemental maps show less spatial distortions and more atomic localization of x‐rays. In addition, a novel non‐local principle component analysis further enhances the quality of EDS SIs compared to conventional denoising methods.[5]

Single-Crystalline Mesoporous Palladium and Palladium-Copper Nanocubes for Highly Efficient Electrochemical CO ₂ Reduction

Directed self-assembly of block copolymers is considered one of the candidates to fulfill the requirements of the next technological nodes [1,2]. Polymer domains are aligned by using a chemical and/or a topographical pre-pattern in which preferential surfaces to one of the two blocks or neutral wetting areas are created. In particular, polystyrene-block-polymetylmethacrylate (PS-b-PMMA) has been extensively studied during the last years showing strong capabilities to define periodic nanostructures. However, the relatively low Flory-Huggins parameter and, the resulting low segregation strength of PS-b-PMMA systems, limit their achievable resolution to around 11 nm [3]. The application of block copolymer on sub-10 nm technologies requires the development of the new block copolymer generation known as high- block copolymers. Additionally, an important requirement for their integration on the lithography roadmap is the capability of selectively remove one of the two blocks. The etch contrast between the two domains is typically low due to their organic chemistry. In this sense, selective sequential infiltration synthesis by ALD into one of the blocks can be used in order to incorporate an inorganic material. The formed organic/inorganic blend sustains better the plasma etching to remove the non-infiltrated organic block. In this contribution, we show the use of high- polystyrene-b-polylactide acid (PS-b-PLA) lamellar block copolymer for line/space applications. PS-b-PLA has a larger Flory-Huggins parameter (=0.218 at room temperature[4]) compared with PS-b-PMMA, allowing the size reduction of the self-assembled domains. The method to control the orientation of the polymer domains is similar to the one typically used for PS-b-PMMA. Chemical contrast and the subsequent alignment of the polymer domains are achieved by the definition of a chemical pre-pattern on a random copolymer PS-r-PMMA (48% of PS) (figure 1). The polymer brush is grafted on the substrate and then locally modified by the combination of e-beam lithography and soft oxygen plasma. Afterwards, the PS-b-PLA block copolymer is spin-coated and thermally annealed on the chemically pre-patterned substrate. A chemical contrast is observed between the modified and unmodified stripes. While, the lamellar domains are oriented perpendicular to the substrate on unmodified areas, PLA domains are strongly attracted to the O2 modified surfaces inducing a parallel orientation to the substrate. Additionally, the wetting behavior of the polymer domains is also studied through the difference of surface free energy between the substrate and each polymer block. The energy estimated by the Young´s equation [Δγ =γSA -γSB= γAB·cos(ØAB)], where γSA and γSB are the interface tensions between homo-polymers A and B with the substrate, and ØAB is the contact angle between A and B homo-polymers which is obtained in de-wetting experiments. Finally, sequential infiltration synthesis is used to selectively infiltrate alumina (Al3O2) on PLA domains (figure 2). A selective infiltration is achieved because the precursor molecules react with the carbonyl (C=O) groups that are only present in the PLA block. After five cycles of SIS, the SIS modified PLA domains become more resistant to O2 plasma etching than PS enabling the PS etching without using other kind of hard-masks. The research leading to these results received funding from the European Union's Seventh Framework Program FP7/2007-2013, under the project CoLiSA and the European Nanoelectronics Initiative Advisory Council under the project PLACYD (ENIAC-2013-2). L. Evangelio acknowledges MECD for the PhD contract FPU13/03746 [1] R. Ruiz, H. Kang, F. A. Detcheverry, E. Dobisz, D. S. Kercher, T. R. Albrecht, J. J. de Pablo, P. F. Nealey, Science 321 (2008) 936-939. [2] M. Fernández-Regúlez, L, Evangelio, M. Lorenzoni, J. Fraxedas, F. Perez-Murano, ACS Appl. Mater. Interfaces 6 (2014) 21596-21602. [3] L. Wan, R. Ruiz, H. Gao, K. C. Patel, T. R. Albrecht, J. Yin, J. Kim, Y. Cao, G. Lin, ACS Nano 9 (2015) 7506-7514 [4] A. S. Zalusky, R. Olayo-Valles , J. H. Wolf , M. A. Hillmyer, J. Am. Chem. Soc. 124 (2002) 12761-12773

Three important parametric models for describing the correlation functions and spectra of stationary stochastic processes are the autoregressive (AR), moving average (MA), and autoregressive-moving average (ARMA) models. Quite recently, the MATLAB toolbox “ARMASA” has been made publicly available. This toolbox provides state-of-the-art algorithms to perform automatic identification and selection between the models based on the estimated prediction error. ARMASA works on a single segment of data, whereas in some applications, the data are available as multiple segments. We could process each segment independently and average the estimated autocorrelation functions or spectra afterward. Better performance, however, can be expected when all segments are processed simultaneously, for two reasons. Initially, the bias in the estimated model parameters depends on the number of observations in a segment. Averaging models from segments does not reduce this bias, in contrast to identification from all the data at once. In either case, the variance of the estimated model parameters will decrease. By considering all the data at once this variance reduction can be considered in the order selection stage, decreasing the selection bias. In this paper, we extend the parameter estimation algorithms of the ARMASA toolbox to the case of multiple segments. AR parameter estimation from segments has been considered before, but not for the MA and ARMA models. In addition, in the AR case, model order selection can be done easily when the segments are independent. But, in the application we are primarily interested in, the segments are formed by lines of scanning transmission electron microscopy (STEM) images, which can be correlated with each other. Because the correlation among data segments influences the effective number of independent observations available it becomes necessary to estimate that number. This is not an easy problem in general. For observations from a stochastic process with an isotropic 2-D autocorrelation function we can, however, formulate an accurate estimator in terms of the line autocorrelation function. This situation arises, for example, in STEM images of amorphous specimens. From the AR model identification for segments, the extension to the MA and ARMA models is rather straightforward because MA and ARMA algorithms are based on Durbin's methods, whose first step is the estimation of a long AR model. We compare the segment-based algorithms to conventional ARMASA with simulations of stationary stochastic processes. An illustrative example involving simulated STEM images shows large potential benefits.

Our previous study showed quantitative correspondence between the size of ionic aggregates in a Cu-neutralized poly(styrene-ran-methacrylic acid) ionomer (Cu-SMAA) measured from scanning transmission electron microscopy (STEM) images and the sizes determined from X-ray scattering data interpreted using the Yarusso−Cooper model. Here we compare the average sample volume per ionic aggregate (Vp) from STEM images and X-ray scattering data from Cu-SMAA. The relationship between the 3D morphology of hard spheres as defined by the Yarusso−Cooper parameters (R1, RCA, Vp) and the projected 2D images obtained by STEM was elucidated by modeling. These simulations show that a significant number of the ionic aggregates in Cu-SMAA are obscured due to overlap effects and provide a quantitative relationship between the number of spheres in 3D and the number of features in 2D projections as a function of specimen thickness. Upon measuring the local thickness of a Cu-SMAA specimen using electron energy loss spectrometry (EELS), we find that the Vp determined from STEM images strongly agrees with that determined from X-ray scattering data. Thus, quantitative correspondence of both the size and number density of ionic aggregates has been obtained from direct imaging and X-ray scattering of Cu-SMAA. These data provide compelling evidence that the Yarusso−Cooper scattering model presents an excellent description of the nanoscale morphology of this solvent cast ionomer. Interpretation of this morphological data indicates that the ionic aggregates likely include both ionic and nonionic species. This comprehensive characterization provides a foundation on which a systematic evaluation of the factors that influence the nanoscale morphology, and consequently the macroscopic properties, can be accomplished.

Objective molecular dynamics simulations coupled with a density functional-based tight-binding model indicated that a stress-free single-walled ($14,6$) MoS${}_{2}$ nanotube exhibits a torsional deformation of 0.87 deg/nm. Simulated electron diffraction patterns and atomic-resolution annular dark field scanning transmission electron microscopy (ADF-STEM) images of the computed nanotube structures show promise that this peculiar feature can be identified experimentally. The small intrinsic twist removes the translational periodicity prescribed by the rolled-up construction and defines a nanotube for which the atomic order is most fundamentally described by the objective structures concept.

Scanning transmission electron microscopy (STEM) images can be limited by either aberrations or, because of the technique's serial acquisition, by the effects of environmental instabilities. MatLab image-processing code was written which performs a two stage process to restore degraded high-resolution high-angle annular dark-field (HAADF) STEM images. Firstly, individual images were analysed to identify and correct for high-frequency scan noise; image resolution and signal-noise ratio (SNR) are used as performance metrics and were improved by up to 11.8% and 49% respectively. Secondly, a focal series was used to identify variations in information transfer as a function of defocus (aberrations) whereafter a single image was reconstructed yielding an increase in resolution and SNR of 9.88% and 205% respectively.