Showing 1–18 of 18 results for author: Rack, P D

SPEAR: Structure Property Explainability with Attention Regularization

Authors: Aditya Raghavan, Utkarsh Pratiush, Dalton A. Pearl, Jade Holliman Jr, Katharine Page, Philip D Rack, Sergei V Kalinin

Abstract: Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions. Although attention mechanisms are frequently treated as inherently explainable, unregularized attention can yield unstable, fragmented, or intensity driven attribution pattern… ▽ More Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions. Although attention mechanisms are frequently treated as inherently explainable, unregularized attention can yield unstable, fragmented, or intensity driven attribution patterns that obscure the physical origin of these relationships. Here we introduce SPEAR (Structure Property Explainability with Attention Regularization), a framework that constrains attention distributions during training to improve their stability, selectivity, and physical interpretability. SPEAR augments attention based regression with a learnable temperature that controls attention concentration and a smoothness penalty that enforces coherence across neighboring spectral positions, treating attention as a learnable explanatory object rather than a post hoc visualization. Using synthetic spectral benchmarks with known generative structure, we show that attention regularization produces smooth, contiguous attribution profiles aligned with causal features while preserving predictive accuracy. Applied to experimental X ray diffraction data from a combinatorial rare earth zirconate thin film library, the regularized model selectively emphasizes physically relevant diffraction features and decouples feature importance from raw peak intensity. The reflection it identified prompted a reassessment of our earlier structural analysis, revealing a correlation between the 220 peak position, the tetragonal distortion that accommodates cation size disorder, and the local thermal conductivity. Attention regularization therefore provides a principled training constraint for explainable structure property regression, yielding mechanistically meaningful explanations without sacrificing predictive performance. △ Less

Submitted 13 August, 2026; originally announced August 2026.

Composition design of refractory compositionally complex alloys using machine learning models

Authors: Tao Liang, Eric A. Lass, Haochen Zhu, Carla Joyce C. Nocheseda, Philip D. Rack, Stephen Puplampu, Dayakar Penumadu, Haixuan Xu

Abstract: Refractory compositionally complex alloys (RCCAs) are considered the next generation high-temperature materials. However, their high-dimensional composition spaces are too large to explore by traditional density functional theory or experimental means, making new RCCA discovery slow and cumbersome. This work has addressed these challenges with an integrated composition design framework that can ef… ▽ More Refractory compositionally complex alloys (RCCAs) are considered the next generation high-temperature materials. However, their high-dimensional composition spaces are too large to explore by traditional density functional theory or experimental means, making new RCCA discovery slow and cumbersome. This work has addressed these challenges with an integrated composition design framework that can efficiently and exhaustively explore the relationship between the compositions and two fundamental aspects: 1) the phase stability, including the target body-centered cubic (BCC) phase and its competing phases (hexagonal closed-pack (HCP) structures, Laves and B2 intermetallic phases), and 2) the mechanical properties. This framework is demonstrated with RCCAs within nine refractory metals (Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, and W). Theory-guided machine learning (ML) models were employed to find the composition-mechanical property relationship of RCCAs, where the established theory is used to supplement the yield strength data at ultra-high temperature, and a forward sequential feature selection (SFS) is used to determine feature selection. The resulting ML model for temperature-dependent yield strength was found to have an R_squared value of 0.98 over the entire temperature range (from 0 to 2000 K). The impact of each constituent element on the six key properties is evaluated. The addition of Nb tends to stabilize the BCC phase and the addition of Ti improves the ductility of RCCAs. Combined with all methods involved in this framework, the on-demand designer allows the alloy designers to have all properties for any RCCA compositions and narrow down the composition space by applying custom screening criteria. The output from the predictor and screener provides valuable guidance for our experimental study of RCCAs and accelerates the pace of materials discovery. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: 32 pages including 12 pages of SI, 6 figures in manuscript and 6 figures in SI, 50 references

Accelerated Materials Discovery through Cost-Aware Bayesian Optimization of Real-World Indentation Workflows

Authors: Vivek Chawla, Stephen Puplampu, Haochen Zhu, Philip D. Rack, Dayakar Penumadu, Sergei Kalinin

Abstract: Accelerating the discovery of mechanical properties in combinatorial materials requires autonomous experimentation that accounts for both instrument behavior and experimental cost. Here, an automated nanoindentation (AE-NI) framework is developed and validated for adaptive mechanical mapping of combinatorial thin-film libraries. The method integrates heteroskedastic Gaussian-process modeling with… ▽ More Accelerating the discovery of mechanical properties in combinatorial materials requires autonomous experimentation that accounts for both instrument behavior and experimental cost. Here, an automated nanoindentation (AE-NI) framework is developed and validated for adaptive mechanical mapping of combinatorial thin-film libraries. The method integrates heteroskedastic Gaussian-process modeling with cost-aware Bayesian optimization to dynamically select indentation locations and hold times, minimizing total testing time while preserving measurement accuracy. A detailed emulator and cost model capture the intrinsic penalties associated with lateral motion, drift stabilization, and reconfiguration-factors often neglected in conventional active-learning approaches. To prevent kernel-length-scale collapse caused by disparate time scales, a hierarchical meta-testing workflow combining local grid and global exploration is introduced. Implementation of the workflow is shown on a experimental Ta-Ti-Hf-Zr thin-film library. The proposed framework achieves nearly a thirty-fold improvement in property-mapping efficiency relative to grid-based indentation, demonstrating that incorporating cost and drift models into probabilistic planning substantially improves performance. This study establishes a generalizable strategy for optimizing experimental workflows in autonomous materials characterization and can be extended to other high-precision, drift-limited instruments. △ Less

Submitted 20 November, 2025; originally announced November 2025.

Comments: 29 pages, 8 figures, 2 figures in SI

Morphotropic Phase Boundary (MPB) Induced Enhancement of Ferroelectric and Piezoelectric Properties in Li and Ta modified K0.5Na0.5NbO3

Authors: Satyaranjan Sahoo, Dhiren K. Pradhan, Shalini Kumari, Abhisikta Sahu, Koyal Suman Samantaray, Vikas N. Thakur, Anupam Mishra, M. M. Rahaman, Ashok Kumar, Reji Thomas, Philip D. Rack, Dillip K. Pradhan

Abstract: Lead-free (K0.48Na0.48Li0.04)(Nb1-xTax)O3 (KNLNT-x) ceramics were synthesized to study the effects of Li and Ta substitution on phase transition behavior, microstructure, and ferroelectric, dielectric, and piezoelectric properties. X-ray diffraction and Raman spectroscopy show that compositions with x < 0.10 exhibit a single orthorhombic (Amm2) phase, while 0.10 <= x <= 0.20 show coexistence of or… ▽ More Lead-free (K0.48Na0.48Li0.04)(Nb1-xTax)O3 (KNLNT-x) ceramics were synthesized to study the effects of Li and Ta substitution on phase transition behavior, microstructure, and ferroelectric, dielectric, and piezoelectric properties. X-ray diffraction and Raman spectroscopy show that compositions with x < 0.10 exhibit a single orthorhombic (Amm2) phase, while 0.10 <= x <= 0.20 show coexistence of orthorhombic and tetragonal (Amm2 + P4mm) phases. For x > 0.20, a single tetragonal (P4mm) phase is obtained. Microstructural analysis shows a dense ceramic with decreasing grain size as Ta concentration increases. Temperature-dependent dielectric studies reveal two transitions: orthorhombic-tetragonal (TO-T) and tetragonal-cubic (TC). Both transition temperatures decrease systematically with increasing Ta, and TO-T shifts below room temperature for x > 0.15. The composition KNLNT-0.20 exhibits the highest dielectric constant (Er = 556) and piezoelectric coefficient (d33 = 159 pC/N). The enhanced piezoelectric response is attributed to a morphotropic phase boundary rather than a shift of the polymorphic phase boundary temperature. A composition-temperature phase diagram was constructed based on XRD, Raman, and dielectric data. △ Less

Submitted 16 October, 2025; originally announced October 2025.

Comments: 45 Pages, 7 main Figures

SAM$^{*}$: Task-Adaptive SAM with Physics-Guided Rewards

Authors: Kamyar Barakati, Utkarsh Pratiush, Sheryl L. Sanchez, Aditya Raghavan, Delia J. Milliron, Mahshid Ahmadi, Philip D. Rack, Sergei V. Kalinin

Abstract: Image segmentation is a critical task in microscopy, essential for accurately analyzing and interpreting complex visual data. This task can be performed using custom models trained on domain-specific datasets, transfer learning from pre-trained models, or foundational models that offer broad applicability. However, foundational models often present a considerable number of non-transparent tuning p… ▽ More Image segmentation is a critical task in microscopy, essential for accurately analyzing and interpreting complex visual data. This task can be performed using custom models trained on domain-specific datasets, transfer learning from pre-trained models, or foundational models that offer broad applicability. However, foundational models often present a considerable number of non-transparent tuning parameters that require extensive manual optimization, limiting their usability for real-time streaming data analysis. Here, we introduce a reward function-based optimization to fine-tune foundational models and illustrate this approach for SAM (Segment Anything Model) framework by Meta. The reward functions can be constructed to represent the physics of the imaged system, including particle size distributions, geometries, and other criteria. By integrating a reward-driven optimization framework, we enhance SAM's adaptability and performance, leading to an optimized variant, SAM$^{*}$, that better aligns with the requirements of diverse segmentation tasks and particularly allows for real-time streaming data segmentation. We demonstrate the effectiveness of this approach in microscopy imaging, where precise segmentation is crucial for analyzing cellular structures, material interfaces, and nanoscale features. △ Less

Submitted 8 September, 2025; originally announced September 2025.

Comments: 19 pages, 8 figures

Materials Discovery in Combinatorial and High-throughput Synthesis and Processing: A New Frontier for SPM

Authors: Boris N. Slautin, Yongtao Liu, Kamyar Barakati, Yu Liu, Reece Emery, Seungbum Hong, Astita Dubey, Vladimir V. Shvartsman, Doru C. Lupascu, Sheryl L. Sanchez, Mahshid Ahmadi, Yunseok Kim, Evgheni Strelcov, Keith A. Brown, Philip D. Rack, Sergei V. Kalinin

Abstract: For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and… ▽ More For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast-growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe structure and functionalities of materials on small scales and with high throughput. SPM inherently meets these criteria, offering a rich and diverse array of data from a single measurement. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization. △ Less

Submitted 11 April, 2025; v1 submitted 5 January, 2025; originally announced January 2025.

Comments: 63 pages, 15 figures

Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy

Authors: Yu Liu, Roger Proksch, Jason Bemis, Utkarsh Pratiush, Astita Dubey, Mahshid Ahmadi, Reece Emery, Philip D. Rack, Yu-Chen Liu, Jan-Chi Yang, Sergei V. Kalinin

Abstract: Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time, but also often leads to frequent probe and sample damage, poor image quality and reproducibility issues for new types of samples or inexperienced users. Despite wide use,… ▽ More Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time, but also often leads to frequent probe and sample damage, poor image quality and reproducibility issues for new types of samples or inexperienced users. Despite wide use, optimization of tapping mode imaging is an extremely hard problem, ill-suited to either classical control methods or machine learning. Here we introduce a reward-driven workflow to automate the optimization of SPM in the tapping mode. The reward function is defined based on multiple channels with physical and empirical knowledge of good scans encoded, representing a sample-agnostic measure of image quality and imitating the decision-making logic employed by human operators. This automated workflow gives optimal scanning parameters for different probes and samples and gives high-quality SPM images consistently in the attractive mode. This study broadens the application and accessibility of SPM and opens the door for fully automated SPM. △ Less

Submitted 25 December, 2024; v1 submitted 7 August, 2024; originally announced August 2024.

Comments: 20 pages, 6 figures

Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation

Authors: Yu Liu, Utkarsh Pratiush, Jason Bemis, Roger Proksch, Reece Emery, Philip D. Rack, Yu-Chen Liu, Jan-Chi Yang, Stanislav Udovenko, Susan Trolier-McKinstry, Sergei V. Kalinin

Abstract: The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements towards operationalization of automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here we build a Py… ▽ More The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements towards operationalization of automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer (HPC), which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning. △ Less

Submitted 20 May, 2024; originally announced May 2024.

Comments: 16 pages, 7 figures

Journal ref: Rev. Sci. Instrum. 95, 093701 (2024)

Controlling hydrocarbon transport and electron beam induced deposition on single layer graphene: toward atomic scale synthesis in the scanning transmission electron microscope

Authors: Ondrej Dyck, Andrew R. Lupini, Philip D. Rack, Jason Fowlkes, Stephen Jesse

Abstract: Focused electron beam induced deposition (FEBID) is a direct write technique for depositing materials on a support substrate akin to 3D printing with an electron beam (e-beam). Opportunities exist for merging this existing technique with aberration-corrected scanning transmission electron microscopy to achieve molecular- or atomic-level spatial precision. Several demonstrations have been performed… ▽ More Focused electron beam induced deposition (FEBID) is a direct write technique for depositing materials on a support substrate akin to 3D printing with an electron beam (e-beam). Opportunities exist for merging this existing technique with aberration-corrected scanning transmission electron microscopy to achieve molecular- or atomic-level spatial precision. Several demonstrations have been performed using graphene as the support substrate. A common challenge that arises during this process is e-beam-induced hydrocarbon deposition, suggesting greater control over the sample environment is needed. Various strategies exist for cleaning graphene in situ. One of the most effective methods is to rapidly heat to high temperatures, e.g., 600 C or higher. While this can produce large areas of what appears to be atomically clean graphene, mobile hydrocarbons can still be present on the surfaces. Here, we show that these hydrocarbons are primarily limited to surface migration and demonstrate an effective method for interrupting the flow using e-beam deposition to form corralled hydrocarbon regions. This strategy is effective for maintaining atomically clean graphene at high temperatures where hydrocarbon mobility can lead to substantial accumulation of unwanted e-beam deposition. △ Less

Submitted 11 July, 2023; originally announced July 2023.

Doping transition-metal atoms in graphene for atomic-scale tailoring of electronic, magnetic, and quantum topological properties

Authors: Ondrej Dyck, Lizhi Zhang, Mina Yoon, Jacob L. Swett, Dale Hensley, Cheng Zhang, Philip D. Rack, Jason D. Fowlkes, Andrew R. Lupini, Stephen Jesse

Abstract: Atomic-scale fabrication is an outstanding challenge and overarching goal for the nanoscience community. The practical implementation of moving and fixing atoms to a structure is non-trivial considering that one must spatially address the positioning of single atoms, provide a stabilizing scaffold to hold structures in place, and understand the details of their chemical bonding. Free-standing grap… ▽ More Atomic-scale fabrication is an outstanding challenge and overarching goal for the nanoscience community. The practical implementation of moving and fixing atoms to a structure is non-trivial considering that one must spatially address the positioning of single atoms, provide a stabilizing scaffold to hold structures in place, and understand the details of their chemical bonding. Free-standing graphene offers a simplified platform for the development of atomic-scale fabrication and the focused electron beam in a scanning transmission electron microscope can be used to locally induce defects and sculpt the graphene. In this scenario, the graphene forms the stabilizing scaffold and the experimental question is whether a range of dopant atoms can be attached and incorporated into the lattice using a single technique and, from a theoretical perspective, we would like to know which dopants will create technologically interesting properties. Here, we demonstrate that the electron beam can be used to selectively and precisely insert a variety of transition metal atoms into graphene with highly localized control over the doping locations. We use first-principles density functional theory calculations with direct observation of the created structures to reveal the energetics of incorporating metal atoms into graphene and their magnetic, electronic, and quantum topological properties. △ Less

Submitted 11 July, 2023; originally announced July 2023.

Anti-microbial properties of a multi-component alloy

Authors: Anne F. Murray, Daniel Bryan, David A. Garfinkel, Cameron S. Jogensen, Nan Tang, WLNC Liyanage, Eric A. Lass, Ying Yang, Philip D. Rack, Thomas G. Denes, Dustin A. Gilbert

Abstract: High traffic touch surfaces such as doorknobs, countertops, and handrails can be transmission points for the spread of pathogens, emphasizing the need to develop materials that actively self-sanitize. Metals are frequently used for these surfaces due to their durability, but many metals also possess antimicrobial properties which function through a variety of mechanisms. This work investigates met… ▽ More High traffic touch surfaces such as doorknobs, countertops, and handrails can be transmission points for the spread of pathogens, emphasizing the need to develop materials that actively self-sanitize. Metals are frequently used for these surfaces due to their durability, but many metals also possess antimicrobial properties which function through a variety of mechanisms. This work investigates metallic alloys comprised of several bioactive metals with the target of achieving broad-spectrum, rapid bioactivity through synergistic activity. An entropy-motivated stabilization paradigm is proposed to prepare scalable alloys of copper, silver, nickel and cobalt. Using combinatorial sputtering, thin-film alloys were prepared on 100 mm wafers with 50% compositional grading of each element across the wafer. The films were then annealed and investigated for alloy stability. Bioactivity testing was performed on both the as-grown alloys and the annealed films using four microorganisms -- Phi6, MS2, Bacillus subtilis and Escherichia coli -- as surrogates for human viral and bacterial pathogens. Testing showed that after 30 s of contact with some of the test alloys, Phi6, an enveloped, single-stranded RNA bacteriophage that serves as a SARS-CoV 2 surrogate, was reduced up to 6.9 orders of magnitude (>99.9999%). Additionally, the non-enveloped, double-stranded DNA bacteriophage MS2, and the Gram-negative E. coli and Gram-positive B. subtilis bacterial strains showed a 5.0, 6.4, and 5.7 log reduction in activity after 30, 20 and 10 minutes, respectively. Bioactivity in the alloy samples showed a strong dependence on the composition, with the log reduction scaling directly with the Cu content. Concentration of Cu by phase separation after annealing improved activity in some of the samples. The results motivate a variety of themes which can be leveraged to design ideal bioactive surfaces. △ Less

Submitted 28 April, 2022; originally announced May 2022.

Comments: 15 pages, 6 figures

Journal ref: Scientific Reports 12, 21427 (2022)

Electron-beam Introduction of Heteroatomic Pt-Si Structures in Graphene

Authors: Ondrej Dyck, Cheng Zhang, Philip D. Rack, Jason D. Fowlkes, Bobby Sumpter, Andrew R. Lupini, Sergei V. Kalinin, Stephen Jesse

Abstract: Electron-beam (e-beam) manipulation of single dopant atoms in an aberration-corrected scanning transmission electron microscope is emerging as a method for directed atomic motion and atom-by-atom assembly. Until now, the dopant species have been limited to atoms closely matched to carbon in terms of ionic radius and capable of strong covalent bonding with carbon atoms in the graphene lattice. In s… ▽ More Electron-beam (e-beam) manipulation of single dopant atoms in an aberration-corrected scanning transmission electron microscope is emerging as a method for directed atomic motion and atom-by-atom assembly. Until now, the dopant species have been limited to atoms closely matched to carbon in terms of ionic radius and capable of strong covalent bonding with carbon atoms in the graphene lattice. In situ dopant insertion into a graphene lattice has thus far been demonstrated only for Si, which is ubiquitously present as a contaminant in this material. Here, we achieve in situ manipulation of Pt atoms and their insertion into the graphene host matrix using the e-beam deposited Pt on graphene as a host system. We further demonstrate a mechanism for stabilization of the Pt atom, enabled through the formation of Si-stabilized Pt heteroatomic clusters attached to the graphene surface. This study provides evidence toward the universality of the e-beam assembly approach, opening a pathway for exploring cluster chemistry through direct assembly. △ Less

Submitted 17 March, 2022; originally announced March 2022.

Comments: 23 pages, 6 figures

Journal ref: Carbon 161, 750-757 (2020)

Magnetism in Metastable and Annealed Compositionally Complex Alloys

Authors: Nan Tang, Lizabeth Quigley, Walker L. Boldman, Cameron S. Jorgensen, Rémi Koch, Daniel O'Leary, Hugh R. Meda, Philip D. Rack, Dustin A. Gilbert

Abstract: Compositionally complex materials (CCMs) present a potential paradigm shift in the design of magnetic materials. These alloys exhibit long-range structural order coupled with limited or no chemical order. As a result, extreme local environments exist with a large opposing magnetic energy term, which can manifest large changes in the magnetic behavior. In the current work, the magnetic properties o… ▽ More Compositionally complex materials (CCMs) present a potential paradigm shift in the design of magnetic materials. These alloys exhibit long-range structural order coupled with limited or no chemical order. As a result, extreme local environments exist with a large opposing magnetic energy term, which can manifest large changes in the magnetic behavior. In the current work, the magnetic properties of (Cr, Mn, Fe, Ni) alloys are presented. These materials were prepared by room-temperature combinatorial sputtering, resulting in a range of compositions with a single BCC structural phase and no chemical ordering. The combinatorial growth technique allows CCMs to be prepared outside of their thermodynamically stable phase, enabling the exploration of otherwise inaccessible order. The mixed ferromagnetic and antiferromagnetic interactions in these alloys causes frustrated magnetic behavior, which results in an extremely low coercivity (<1 mT), which increases rapidly at 50 K. At low temperatures, the coercivity achieves values of nearly 500 mT, which is comparable to some high-anisotropy magnetic materials. Commensurate with the divergent coercivity is an atypical drop in the temperature dependent magnetization. These effects are explained by a mixed magnetic phase model, consisting of ferro-, antiferro , and frustrated magnetic regions, and are rationalized by simulations. A machine-learning algorithm is employed to visualize the parameter space and inform the development of subsequent compositions. Annealing the samples at 600 °C orders the sample, more-than doubling the Curie temperature and increasing the saturation magnetization by as much as 5x. Simultaneously, the large coercivities are suppressed, resulting in magnetic behavior that is largely temperature independent over a range of 350 K. △ Less

Submitted 23 November, 2021; originally announced November 2021.

Comments: 13 pages, 6 figures

Journal ref: Phys. Rev. Materials 5, 114405 (2021)

Direct Observation of Infrared Plasmonic Fano Antiresonances by a Nanoscale Electron Probe

Authors: Kevin C. Smith, Agust Olafsson, Xuan Hu, Amber M. Nelson-Quillin, Juan Carlos Idrobo, Robyn Collette, Philip D. Rack, Jon P. Camden, David J. Masiello

Abstract: In this Letter, we exploit recent breakthroughs in monochromated aberration-corrected scanning transmission electron microscopy (STEM) to resolve infrared plasmonic Fano antiresonances in individual nanofabricated disk-rod dimers. Using a combination of electron energy-loss spectroscopy (EELS) and theoretical modeling, we investigate and characterize a subspace of the weak coupling regime betwee… ▽ More In this Letter, we exploit recent breakthroughs in monochromated aberration-corrected scanning transmission electron microscopy (STEM) to resolve infrared plasmonic Fano antiresonances in individual nanofabricated disk-rod dimers. Using a combination of electron energy-loss spectroscopy (EELS) and theoretical modeling, we investigate and characterize a subspace of the weak coupling regime between quasi-discrete and quasi-continuum localized surface plasmon resonances where infrared plasmonic Fano antiresonances appear. This work illustrates the capability of STEM instrumentation to experimentally observe nanoscale plasmonic responses that were previously the domain only of higher resolution infrared spectroscopies. △ Less

Submitted 24 September, 2019; v1 submitted 4 August, 2019; originally announced August 2019.

Journal ref: Phys. Rev. Lett. 123, 177401 (2019)

Self-assembly of a drop pattern from a two-dimensional grid of nanometric metallic filaments

Authors: Ingrith Cuellar, Pablo D. Ravazzoli, Javier A. Diez, Alejandro G. González, Nicholas A. Roberts, Jason D. Fowlkes, Philip D. Rack, Lou Kondic

Abstract: We report experiments, modeling and numerical simulations of the self--assembly of particle patterns obtained from a nanometric metallic square grid. Initially, nickel filaments of rectangular cross section are patterned on a SiO$_2$ flat surface, and then they are melted by laser irradiation with $\sim 20$ ns pulses. During this time, the liquefied metal dewets the substrate, leading to a linear… ▽ More We report experiments, modeling and numerical simulations of the self--assembly of particle patterns obtained from a nanometric metallic square grid. Initially, nickel filaments of rectangular cross section are patterned on a SiO$_2$ flat surface, and then they are melted by laser irradiation with $\sim 20$ ns pulses. During this time, the liquefied metal dewets the substrate, leading to a linear array of drops along each side of the squares. The experimental data provides a series of SEM images of the resultant morphology as a function of the number of laser pulses or cumulative liquid lifetime. These data are analyzed in terms of fluid mechanical models that account for mass conservation and consider flow evolution with the aim to predict the final number of drops resulting from each side of the square. The aspect ratio, $δ$, between the square sides' lengths and their widths is an essential parameter of the problem. Our models allow us to predict the $δ$-intervals within which a certain final number of drops are expected. The comparison with experimental data shows a good agreement with the model that explicitly considers the Stokes flow developed in the filaments neck region that lead to breakup points. Also, numerical simulations, that solve the Navier-Stokes equations along with slip boundary condition at the contact lines, are implemented to describe the dynamics of the problem. △ Less

Submitted 28 May, 2018; v1 submitted 18 May, 2018; originally announced May 2018.

Journal ref: Phys. Rev. E 98, 043101 (2018)

High conduction hopping behavior induced in transition metal dichalcogenides by percolating defect networks: toward atomically thin circuits

Authors: Michael G. Stanford, Pushpa R. Pudasaini, Elisabeth T. Gallmeier, Nicholas Cross, Liangbo Liang, Akinola Oyedele, Gerd Duscher, Masoud Mahjouri-Samani, Kai Wang, Kai Xiao, David B. Geohegan, Alex Belianinov, Bobby G. Sumpter, Philip D. Rack

Abstract: Atomically thin circuits have recently been explored for applications in next-generation electronics and optoelectronics and have been demonstrated with two-dimensional lateral heterojunctions. In order to form true 2D circuitry from a single material, electronic properties must be spatially tunable. Here, we report tunable transport behavior which was introduced into single layer tungsten diselen… ▽ More Atomically thin circuits have recently been explored for applications in next-generation electronics and optoelectronics and have been demonstrated with two-dimensional lateral heterojunctions. In order to form true 2D circuitry from a single material, electronic properties must be spatially tunable. Here, we report tunable transport behavior which was introduced into single layer tungsten diselenide and tungsten disulfide by focused He$^+$ irradiation. Pseudo-metallic behavior was induced by irradiating the materials with a dose of ~1x10$^{16} He^+/cm^2$ to introduce defect states, and subsequent temperature-dependent transport measurements suggest a nearest neighbor hopping mechanism is operative. Scanning transmission electron microscopy and electron energy loss spectroscopy reveal that Se is sputtered preferentially, and extended percolating networks of edge states form within WSe$_2$ at a critical dose of 1x10$^{16} He^+/cm^2$. First-principles calculations confirm the semiconductor-to-metallic transition of WSe$_2$ after pore and edge defects were introduced by He$^+$ irradiation. The hopping conduction was utilized to direct-write resistor loaded logic circuits in WSe$_2$ and WS$_2$ with a voltage gain of greater than 5. Edge contacted thin film transistors were also fabricated with a high on/off ratio (> 10$^6$), demonstrating potential for the formation of atomically thin circuits. △ Less

Submitted 23 September, 2017; v1 submitted 15 May, 2017; originally announced May 2017.

Comments: 6 figures

Journal ref: Adv. Funct. Mater. 2017, 27, 1702829

Strain Doping: Reversible Single-Axis Control of a Complex Oxide Lattice via Helium Implantation

Authors: Hangwen Guo, Shuai Dong, Philip D. Rack, John D. Budai, Christianne Beekman, Zheng Gai, Wolter Siemons, C. M. Gonzalez, R. Timilsina, Anthony T. Wong, Andreas Herklotz, Paul C. Snijders, Elbio Dagotto, Thomas Z. Ward

Abstract: We report on the use of helium ion implantation to independently control the out-of-plane lattice constant in epitaxial La0.7Sr0.3MnO3 thin films without changing the in-plane lattice constants. The process is reversible by a vacuum anneal. Resistance and magnetization measurements show that even a small increase in the out-of-plane lattice constant of less than 1% can shift the metal-insulator tr… ▽ More We report on the use of helium ion implantation to independently control the out-of-plane lattice constant in epitaxial La0.7Sr0.3MnO3 thin films without changing the in-plane lattice constants. The process is reversible by a vacuum anneal. Resistance and magnetization measurements show that even a small increase in the out-of-plane lattice constant of less than 1% can shift the metal-insulator transition and Curie temperatures by more than 100 °C. Unlike conventional epitaxy-based strain tuning methods which are constrained not only by the Poisson effect but by the limited set of available substrates, the present study shows that strain can be independently and continuously controlled along a single axis. This permits novel control over orbital populations through Jahn-Teller effects, as shown by Monte Carlo simulations on a double-exchange model. The ability to reversibly control a single lattice parameter substantially broadens the phase space for experimental exploration of predictive models and leads to new possibilities for control over materials' functional properties. △ Less

Submitted 29 June, 2015; originally announced June 2015.

Journal ref: Physical Review Letters 114, 256801 (2015)

Electrophoretic-like gating used to control metal-insulator transitions in electronically phase separated manganite wires

Authors: Hangwen Guo, Joo H. Noh, Shuai Dong, Philip D. Rack, Zheng Gai, Xiaoshan Xu, Elbio Dagotto, Jian Shen, T. Zac Ward

Abstract: Electronically phase separated manganite wires are found to exhibit controllable metal-insulator transitions under local electric fields. The switching characteristics are shown to be fully reversible, polarity independent, and highly resistant to thermal breakdown caused by repeated cycling. It is further demonstrated that multiple discrete resistive states can be accessed in a single wire. The r… ▽ More Electronically phase separated manganite wires are found to exhibit controllable metal-insulator transitions under local electric fields. The switching characteristics are shown to be fully reversible, polarity independent, and highly resistant to thermal breakdown caused by repeated cycling. It is further demonstrated that multiple discrete resistive states can be accessed in a single wire. The results conform to a phenomenological model in which the inherent nanoscale insulating and metallic domains are rearranged through electrophoretic-like processes to open and close percolation channels. △ Less

Submitted 28 August, 2013; originally announced August 2013.

Comments: 15 pages, 4 figures, To be published in Nano Letters