Building the Microscopes of Tomorrow

The STROBE Science & Technology Center brings together imaging science experts from seven universities and partners with national labs, industry and academe to push the boundaries of imaging science capabilities and technologies, while building a strong workforce that is prepared for 21st century careers in science and technology.

RESEARCH HIGHLIGHTS

Phase matching in Vector-Beam Driven High Harmonic Generation in 3D-printed Gas Cells

Attiyah, H. et all., Journal of the Optical Society of America B 42, 12 (2025).

Laser driven x-ray sources are capable of achieving nanometer length scale imaging while simultaneously durations on the order of . . .

Multi-Element 3D Imaging with Soft X-ray Ptychography

J. Plumb, et al., Proc. SPIE 13622-4, X-ray Nanoimaging: Instruments and Methods VII, (2025).

A new approach for high-resolution, three-dimensional elemental imaging has been demonstrated using soft X-ray ptychographic tomography. This technique leverages both . . .

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STROBE IN THE NEWS

Atomic Musical Chairs: How Tiny Nanocrystals Are Informing the Future of Energy-Efficient Electronics

March 17, 2026|University of Colorado Boulder|

While most people, when asked about energy innovation, think about some of the "large" technologies, . . .

3D, atom-by-atom maps of disordered materials

January 28, 2026|CNSI News|

Researchers at the California NanoSystems Institute at UCLA published a step-by-step framework for determining the . . .

STROBE AWARDS

Congratulations to Benjamin Hammel for Receiving the Outstanding Graduate Award from the CU Boulder Materials Science and Engineering Program

May 2, 2026|University of Colorado Boulder|

Benjamin Hammel was recognized with the "Outstanding Graduate Award" and served as the Graduation . . .

Congratulations to Leo Illing for Receiving an NSF Graduate Research Fellowship

April 13, 2026|National Science Foundation|

The purpose of the NSF Graduate Research Fellowship Program (GRFP) is to help ensure the . . .

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More Awards

STROBE IN THE NEWS

Atomic Musical Chairs: How Tiny Nanocrystals Are Informing the Future of Energy-Efficient Electronics

March 17, 2026|University of Colorado Boulder|

While most people, when asked about energy innovation, think about some of the "large" technologies, . . .

3D, atom-by-atom maps of disordered materials

January 28, 2026|CNSI News|

Researchers at the California NanoSystems Institute at UCLA published a step-by-step framework for determining the . . .

More News

STROBE AWARDS

Congratulations to Benjamin Hammel for Receiving the Outstanding Graduate Award from the CU Boulder Materials Science and Engineering Program

May 2, 2026|University of Colorado Boulder|

Benjamin Hammel was recognized with the "Outstanding Graduate Award" and served as the Graduation . . .

Congratulations to Leo Illing for Receiving an NSF Graduate Research Fellowship

April 13, 2026|National Science Foundation|

The purpose of the NSF Graduate Research Fellowship Program (GRFP) is to help ensure the . . .

More Awards

Optimizing experimental design for fast and accura $Optimizing experimental design for fast and accurate CD metrology of interconnects using coherent extreme-ultraviolet scatterometry C. Klein, N. W. Jenkins, Y. Shao, Y. Li, J. Shin, S. Park, W. Kim, H. C. Kapteyn, M. M. Murnane, Journal of Micro/Nanopatterning, Materials, and Metrology, 25, 014001-1, (2026). DOI: 10.1117/1.jmm.25.1.014001 We study the effect of experimental design parameters (wavelength and incidence angle) on EUV scatterometry. We explore the fundamental sensitivity capabilities of EUV scatterometry for interconnect metrology when data are collected such that the information content in a single EUV diffraction pattern is maximized by tuning the wavelength and incidence angle. The uncertainty of the sample parameters depends strongly on wavelength and incidence angle, necessitating experimental design methods to achieve maximal sensitivity. When data are collected under an optimal experimental design, simulations indicate that sub-angstrom accuracy can be obtained from only a single diffraction pattern for the dishing depth of interconnect pads (0.02 nm standard deviation), and the substrate density can additionally be extracted (0.015g/cm3 standard deviation). Decorrelation of the sample parameters is observed with optimal design, and 13.8 nm EUV light provides better sensitivity than longer wavelengths such as 30 nm.$

High-Purity Monovalent Functionalization of Carbon $High-Purity Monovalent Functionalization of Carbon Nanotubes S. Nishitani, Z. Liang, A. Tabo, W. Zhang, D. Smith, K. Xu, M. P. Landry, Angewandte Chemie International Edition, (2026). DOI: 10.1002/anie.7643898 Single-walled carbon nanotubes (SWCNTs) show promise for probing molecular interactions at single-molecule resolution, yet generating SWCNT populations bearing a single defined functional tag remains challenging because surface functionalization is inherently stochastic. Here, we present a batch-scale strategy to produce predominantly singly tagged SWCNTs by leveraging the stochastic adsorption of single-stranded DNA (ssDNA). Specifically, SWCNTs are dispersed using a mixture of unmodified ssDNA (um-ssDNA) and a minor fraction of modified ssDNA (m-ssDNA) carrying an affinity handle. We developed a probabilistic ssDNA–SWCNT binding model that predicts the distribution of m-ssDNA per nanotube as a function of the input minor-strand fraction p = m-ssDNA/total ssDNA, enabling selection of conditions that maximize single-tag purity. Using magnetic-bead capture via a biotin affinity interaction and subsequent release, we isolate SWCNTs with 97.6% predicted single-tag purity at 2% recovery. Single-molecule fluorescence imaging further supports predominantly single-label occupancy under the model-selected conditions. Thus, this approach provides a general route to SWCNTs bearing a single molecular handle for downstream conjugation and assembly, supporting diverse future applications in SWCNT-based nanotechnologies.$

Ultrafast nano-imaging and nano-spectroscopy B. L.

Accurate determination of the 3D atomic structure

Cryo-EM structures reveal a conserved architecture

Multimodal Nanoscale Mapping of Local Structure an $Multimodal Nanoscale Mapping of Local Structure and CO2 Adsorption in Metal–Organic Frameworks S. L. Karstens, M. N. Dods, A. Saha, M. Garai, W. Dai, K. I. Graf, R. A. Klein, H. H. Jiang, J. Cho, K. C. Bustillo, M. B. Raschke, P. Ercius, J. R. Long, A. M. Minor, Journal of the American Chemical Society, 148, 8474-8486, (2026). DOI: 10.1021/jacs.5c19737 Diamine functionalization of the metal–organic framework Mg2(dobpdc) (dobpdc4– = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate) significantly enhances its selectivity for CO2 capture from flue gases and air. The structure and CO2 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 CO2 occupancy across individual nanocrystals. To resolve this limitation, we demonstrate a multimodal, nanoscale characterization of Mg2(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 CO2, 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 CO2 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.$

Restoration of weak localization in bilayer graphe

Structural and Compositional Evolution of Colloida

Implementing Bayesian optimization to compare inje

Gradient-Adjusted Underdamped Langevin Dynamics fo $Gradient-Adjusted Underdamped Langevin Dynamics for Sampling\ast X. Zuo, S. Osher, W. Li, SIAM/ASA Journal on Uncertainty Quantification, 13, 1735-1765, (2025). DOI: 10.1137/24m1702015 Sampling from a target distribution is a fundamental problem with wide-ranging applications in scientific computing and machine learning. Traditional Markov chain Monte Carlo (MCMC) algorithms, such as the unadjusted Langevin algorithm (ULA), derived from the overdamped Langevin dynamics, have been extensively studied. From an optimization perspective, the Kolmogorov forward equation of the overdamped Langevin dynamics can be treated as the gradient flow of the relative entropy in the space of probability densities embedded with Wasserstein-2 metrics. Several efforts have also been devoted to including momentum-based methods, such as underdamped Langevin dynamics, for faster convergence of sampling algorithms. Recent advances in optimization have demonstrated the effectiveness of primal-dual damping and Hessian-driven damping dynamics in achieving faster convergence when solving optimization problems. Motivated by these developments, we introduce a class of stochastic differential equations (SDEs) called gradient-adjusted underdamped Langevin dynamics (GAUL), which add stochastic perturbations in primal-dual damping dynamics and Hessian-driven damping dynamics from optimization. We prove that GAUL admits the correct invariant distribution, whose marginal is the target distribution. The proposed method outperforms overdamped and underdamped Langevin dynamics regarding convergence speed in the total variation distance for Gaussian target distributions. Moreover, using the Euler–Maruyama discretization, we show that the mixing time toward a biased target distribution only depends on the square root of the condition number of the target covariance matrix. In addition, we propose another discretization scheme based on the splitting method, which yields a smaller first-order asymptotic bias than the Euler–Maruyama scheme when sampling a Gaussian distribution. Numerical experiments for non-Gaussian target distributions, such as Bayesian regression problems and Bayesian neural networks,$

Direct measurement of the electron wind force in c

Phase matching in Vector-Beam Driven High Harmonic

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