arXiv subjects

Raymond R. Unocic

Publications and source records attributed to Raymond R. Unocic.

14 recordsLinked to original sources

arXiv2026

Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials

Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment. This "valley of death" stems from optimization that prioritizes laboratory metrics over industrial viability. We propose a new strategy: "born-qualified" autonomous development, which embeds manufacturability, cost, and durability constraints from the outset. This approach is enabled by four pillars, including the development of multi-objective metrics, causal models, a modular infrastructure, and embedding manufacturing in the discovery loop. Realizing this vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.

cond-mat.mtrl-sci↗

arXiv2025

Autonomous Fabrication of Tailored Defect Structures in 2D Materials using Machine Learning-enabled Scanning Transmission Electron Microscopy

Materials with tailored quantum properties can be engineered from atomic scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advanced machine learning and automated beam control. As a proof of concept, we achieved controlled fabrication of MoS-nanowire (MoS-NW) edge structures by iterative and targeted exposure of $MoS_2$ monolayer to a focused electron beam to selectively eject sulfur atoms, utilizing high-angle annular dark-field (HAADF) imaging for feedback-controlled monitoring structural evolution of defects. A machine learning framework combining a random forest model and convolutional neural networks (CNN) was developed to decode the HAADF image and accurately identify atomic positions and species. This atomic-level information was then integrated into an autonomous decision-making platform, which applied predefined fabrication strategies to instruct beam control about atomic sites to be ejected. The selected sites were subsequently exposed to localized electron beam using an FPGA-controlled scan routine with precise control over beam positioning and duration. While the MoS-NW edge structures produced exhibit promising mechanical and electronic properties, the proposed autonomous fabrication framework is material-agnostic and can be extended to other 2D materials for the creation of diverse defect structures and heterostructures beyond $MoS_2$.

cond-mat.mtrl-sci↗

arXiv2025

A Panoramic View of MXenes via a New Design Strategy

Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. The transition metal and nonmetallic atoms in MXenes can exhibit distinct coordination environments, potentially leading to a wide variety of 2D phases. Despite extensive research and significant advancements, a fundamental understanding of MXenes' phase diversity and its relationship with their hierarchical precursors, including intermediate MAX phases and parent bulk phases, remains limited. Using high-throughput modeling based on first-principles density functional theory, we unveil a wide range of MXenes and comprehensively evaluate their relative stabilities across a large chemical space. The key lies in considering both octahedral and trigonal prismatic coordination environments characteristic of various bulk phases. Through this comprehensive structural library of MXenes, we uncover a close alignment between the phase stability of MXenes and that of their hierarchical 3D counterparts. Building on this, we demonstrate a new design strategy where the atomic coordination environments in parent bulk phases can serve as reliable predictors for the design of MXenes, reducing reliance on intermediate MAX phases. Our study significantly expands the landscape of MXenes, at least doubling the number of possible structures.

cond-mat.mtrl-sci↗

arXiv2023

Direct Fabrication of Atomically Defined Pores in MXenes

Controlled fabrication of nanopores in atomically thin two-dimensional material offers the means to create robust membranes needed for ion transport, nanofiltration, and DNA sensing. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation using electrons or ions; however, aberration-corrected scanning transmission electron microscopy (STEM) offers the advantage of combining a highly energetic, sub-angstrom sized electron beam for atomic manipulation along with atomic resolution imaging. Here, we utilize a method for automated nanopore fabrication with real-time atomic visualization to enhance our mechanistic understanding of beam-induced transformations. Additionally, an electron beam simulation technique, Electron-Beam Simulator (E-BeamSim) was developed to observe the atomic movements and interactions resulting from electron beam irradiation. Using the 2D MXene Ti3C2Tx, we explore the influence of temperature on nanopore fabrication by tracking atomic transformation pathways and find that at room temperature, electron beam irradiation induces random displacement of atoms and results in a pileup of titanium atoms at the nanopore edge. This pileup was confirmed and demonstrated in E-BeamSim simulations around the small, milled area in the MXene monolayer. At elevated temperatures, the surface functional groups on MXene are effectively removed, and the mobility of atoms increases, which results in atomic transformations that lead to the selective removal of atoms layer by layer. Through controllable manufacture using e-beam milling fabrication, the production and then characterization of the fabricated defects can be better understood for future work. This work can lead to the development of defect engineering techniques within functionalized MXene layers.

cond-mat.mtrl-sci↗

arXiv2023

When the atoms dance: exploring mechanisms of electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy

Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS2. An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam.

cond-mat.mtrl-sci↗

arXiv2023

A High-Performance Quasi-1D MoS$_2$ Nanoribbon Photodetector

Molybdenum disulfide (MoS$_2$) nanoribbons have attracted increased interest due to their properties which can be tailored by tuning their dimensions. Herein, we demonstrate the growth of highly crystalline quasi-one-dimensional (1D)MoS$_2$ nanoribbons and aligned 3D triangular crystals with predominantly 3R or 2H stacking orientation. The synthesis method relies on the reaction between an ultra-thin MoO3-x film grown by Pulsed Laser Deposition (PLD) and NaF in a sulfur-rich environment. The quasi-1D MoS$_2$ nanoribbons can reach several micrometres in length, and feature single-layer (1L) edges aligned with the nanoribbon core, thereby forming a 1L-multilayer (ML) homojunction due to abrupt discontinuity in thickness. The 1L edges of the nanostructures show a pronounced second harmonic generation (SHG) due to the symmetry breaking, in contrast to the centrosymmetric ML structure, which is unsusceptible to the second-order nonlinear process. A pronounced splitting of the Raman spectra was observed in quasi-1D MoS$_2$ nanoribbons arising from distinct contributions from the 2D edges and a multilayer core. Nanoscale imaging reveals the blue-shifted exciton emission of the monolayer nanoribbon compared to the triangular MoS$_2$ counterpart due to built-in local strain and disorder. We further report on a versatile and ultrasensitive photodetector made of a single quasi-1D MoS$_2$ nanoribbon with a maximum responsivity of 872 A/W at the wavelength of 532 nm. The optoelectronic performance of the MoS$_2$ nanoribbon is superior to the previously reported single-nanoribbon photodetectors. Our findings can inspire the design of TMD semiconductors with tunable geometries for efficient optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2022

Probing electron beam induced transformations on a single defect level via automated scanning transmission electron microscopy

The robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on the ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects graphene. The ELIT-based approach opens the pathway toward the direct on-the-fly analysis of the STEM data and engendering real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

cond-mat.dis-nn↗

arXiv2021

Effective reduction of PdCoO2 thin films via hydrogenation and sign tunable anomalous Hall effect

PdCoO2 , belonging to a family of triangular oxides called delafossite, is one of the most conducting oxides. Its in-plane conductivity is comparable to those of the best metals, and exhibits hydrodynamic electronic transport with extremely long mean free path at cryogenic temperatures. Nonetheless, it is nonmagnetic despite the presence of the cobalt ion. Here, we show that a mild hydrogenation process reduces PdCoO2 thin films to an atomically-mixed alloy of PdCo with strong out-of-plane ferromagnetism and sign-tunable anomalous Hall effect. Considering that many other compounds remain little affected under a similar hydrogenation condition, this discovery may provide a route to creating novel spintronic heterostructures combining strong ferromagnetism, involving oxides and other functional materials.

cond-mat.mtrl-sci↗

arXiv2020

Fluid Guided CVD Growth for Large-scale Monolayer Two-dimensional Materials

Atmospheric pressure chemical vapor deposition (APCVD) has been used extensively for synthesizing two-dimensional (2D) materials, due to its low cost and promise for high-quality monolayer crystal synthesis. However, the understanding of the reaction mechanism and the key parameters affecting the APCVD processes is still in its embryonic stage. Hence, the scalability of the APCVD method in achieving large scale continuous film remains very poor. Here, we use MoSe2 as a model system and present a fluid guided growth strategy for understanding and controlling the growth of 2D materials. Through the integration of experiment and computational fluid dynamics (CFD) analysis in the full-reactor scale, we identified three key parameters: precursor mixing, fluid velocity and shear stress, which play a critical role in the APCVD process. By modifying the geometry of the growth setup, to enhance precursor mixing and decrease nearby velocity shear rate and adjusting flow direction, we have successfully obtained inch-scale monolayer MoSe2. This unprecedented success of achieving scalable 2D materials through fluidic design lays the foundation for designing new CVD systems to achieve the scalable synthesis of nanomaterials.

physics.app-ph↗

arXiv2020

Lattice strain measurement of core@shell electrocatalysts with 4D-STEM nanobeam electron diffraction

Strain engineering enables the direct modification of the atomic bonding and is currently an active area of research aimed at improving the electrocatalytic activity. However, directly measuring the lattice strain of individual catalyst nanoparticles is challenging, especially at the scale of a single unit cell. Here, we quantitatively map the strain present in rhodium@platinum (core@shell) nanocube electrocatalysts using conventional aberration-corrected scanning transmission electron microscopy (STEM) and the recently developed technique of 4D-STEM nanobeam electron diffraction. We demonstrate that 4D-STEM combined with data pre-conditioning allows for quantitative lattice strain mapping with sub-picometer precision without the influence of scan distortions. When combined with multivariate curve resolution, 4D-STEM allows us to distinguish the nanocube core from the shell and to quantify the unit cell size as a function of distance from the core-shell interface. Our results demonstrate that 4D-STEM has significant precision and accuracy advantages in strain metrology of catalyst materials compared to aberration-corrected STEM imaging and is beneficial for extracting information about the evolution of strain in catalyst nanoparticles.

cond-mat.mtrl-sci↗

arXiv2019

Growth of metallic delafossite PdCoO2 by molecular beam epitaxy

The Pd, and Pt based ABO2 delafossites are a unique class of layered, triangular oxides with 2D electronic structure and a large conductivity that rivals the noble metals. Here, we report successful growth of the metallic delafossite PdCoO2 by molecular beam epitaxy (MBE). The key challenge is controlling the oxidation of Pd in the MBE environment where phase-segregation is driven by the reduction of PdCoO2 to cobalt oxide and metallic palladium. This is overcome by combining low temperature (300 °C) atomic layer-by-layer MBE growth in the presence of reactive atomic oxygen with a post-growth high-temperature anneal. Thickness dependence (5-265 nm) reveals that in the thin regime (<75 nm), the resistivity scales inversely with thickness, likely dominated by surface scattering; for thicker films the resistivity approaches the values reported for the best bulk-crystals at room temperature, but the low temperature resistivity is limited by structural twins. This work shows that the combination of MBE growth and a post-growth anneal provides a route to creating high quality films in this interesting family of layered, triangular oxides.

cond-mat.mtrl-sci↗

arXiv2018

Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This progress has been accompanied by an exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extract information from atomically resolved images including location of the atomic species and type of defects. We develop a 'weakly-supervised' approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular 'rotor'. This deep learning based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data.

cond-mat.mtrl-sci↗

arXiv2017

Engineering the thermal conductivity along an individual silicon nanowire by selective helium ion irradiation

The ability to engineer the thermal conductivity of materials allows us to control the flow of heat and derive novel functionalities such as thermal rectification, thermal switching, and thermal cloaking. While this could be achieved by making use of composites and metamaterials at bulk scales, engineering the thermal conductivity at micro- and nano-scale dimensions is considerably more challenging. In this work we show that the local thermal conductivity along a single Si nanowire can be tuned to a desired value (between crystalline and amorphous limits) with high spatial resolution through selective helium ion irradiation with a well-controlled dose. The underlying mechanism is understood through molecular dynamics simulations and quantitative phonon-defect scattering rate analysis, where the behavior of thermal conductivity with dose is attributed to the accumulation and agglomeration of scattering centers at lower doses. Beyond a threshold dose, a crystalline-amorphous transition was observed.

cond-mat.mtrl-sci↗

arXiv2015

Aqueous Proton Transfer Across Single Layer Graphene

Proton transfer across single layer graphene is associated with large computed energy barriers and is therefore thought to be unfavorable at room temperature unless nanoscale holes or dopants are introduced, or a potential bias is applied. Here, we subject single layer graphene supported on fused silica to cycles of high and low pH and show that protons transfer reversibly from the aqueous phase through the graphene to the other side where they undergo acid-base chemistry with the silica hydroxyl groups. After ruling out diffusion through macroscopic pinholes, the protons are found to transfer through rare, naturally occurring atomic defects. Computer simulations reveal low energy barriers of 0.68 to 0.75 eV for aqueous proton transfer across hydroxyl-terminated atomic defects that participate in a Grotthuss-type relay, while pyrylium-like ether terminations shut down proton exchange. Unfavorable energy barriers to helium and hydrogen transfer indicate the transfer process is selective for aqueous protons.

cond-mat.mtrl-sci↗