publications | Zachary W. Ulissi

2024

Catalysis/ML

Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed Ruthenium Dissolution

Jehad Abed, Javier Heras-Domingo, Rohan Yuri Sanspeur, and 8 more authors

Journal of the American Chemical Society, 2024
ML/GNN

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Nima Shoghi, Adeesh Kolluru, John R Kitchin, and 3 more authors

arXiv preprint arXiv:2310.16802, 2024
ML/GNN

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture

Anuroop Sriram, Sihoon Choi, Xiaohan Yu, and 6 more authors

ACS Central Science, 2024

DOI
ML/GNN

Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Nate Gruver, Anuroop Sriram, Andrea Madotto, and 3 more authors

ICLR, 2024
ML/GNN

Generalization of graph-based active learning relaxation strategies across materials

Xiaoxiao Wang, Joseph Musielewicz, Richard Tran, and 6 more authors

Machine Learning: Science and Technology, 2024
ML/GNN

CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks

Brook Wander, Muhammed Shuaibi, John R Kitchin, and 2 more authors

arXiv preprint arXiv:2405.02078, 2024
Catalysis/ML

Adapting OC20-Trained EquiformerV2 Models for High-Entropy Materials

Christian M. Clausen, Jan Rossmeisl, and Zachary W. Ulissi

The Journal of Physical Chemistry C, 2024

DOI
Catalysis/ML

Enumeration of surface site nuclearity and shape in a database of intermetallic low-index surface facets

Unnatti Sharma, Angela Nguyen, John R Kitchin, and 2 more authors

Journal of Catalysis, 2024
ML/GNN

Open materials 2024 (omat24) inorganic materials dataset and models

Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, and 6 more authors

arXiv preprint arXiv:2410.12771, 2024
Catalysis/ML

Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models

Jehad Abed, Jiheon Kim, Muhammed Shuaibi, and 17 more authors

2024
Catalysis/ML

Computational Catalysis

Zachary W Ulissi, Kevin Tran, Junwoong Yoon, and 5 more authors

2024

2023

Catalysis/ML

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

Kirby Broderick, Eric Lopato, Brook Wander, and 3 more authors

Applied Catalysis B: Environmental, Jan 2023

DOI
Catalysis/ML

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

Richard Tran, Janice Lan, Muhammed Shuaibi, and 14 more authors

ACS Catalysis, Jan 2023

DOI
Catalysis/ML

AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials

Janice Lan, Aini Palizhati, Muhammed Shuaibi, and 6 more authors

npj Computational Materials, Jan 2023
ML/GNN

Beyond independent error assumptions in large GNN atomistic models

Janghoon Ock, Tian Tian, John Kitchin, and 1 more author

The Journal of Chemical Physics, Jan 2023
Catalysis/ML

WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions

Rohan Yuri Sanspeur, Javier Heras-Domingo, John R. Kitchin, and 1 more author

Journal of Chemical Information and Modeling, Jan 2023

PMID: 37017312

DOI
ML/GNN

AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification

Muhammed Shuaibi, Yuge Hu, Xiangyun Lei, and 8 more authors

Journal of Open Source Software, Jan 2023
ML/GNN

Chemical Properties from Graph Neural Network-Predicted Electron Densities

Ethan M Sunshine, Muhammed Shuaibi, Zachary W Ulissi, and 1 more author

The Journal of Physical Chemistry C, Jan 2023
Catalysis/ML

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set

Aaron G. Garrison, Javier Heras-Domingo, John R. Kitchin, and 3 more authors

Journal of Chemical Information and Modeling, Jan 2023

PMID: 38049389

DOI
Nanotechnology

Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters

Rajesh K Raju, Saurabh Sivakumar, Xiaoxiao Wang, and 1 more author

Journal of Chemical Information and Modeling, Jan 2023

2022

Catalysis

Heterogeneous Catalysis in Grammar School

Johannes T. Margraf, Zachary W. Ulissi, Yousung Jung, and 1 more author

The Journal of Physical Chemistry C, Jan 2022

DOI
ML/GNN

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?

Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, and 4 more authors

arXiv preprint arXiv:2204.02782, Jan 2022

DOI
Catalysis/ML

FINETUNA: Fine-tuning Accelerated Molecular Simulations

Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, and 1 more author

Machine Learning: Science and Technology, Sep 2022

DOI
ML/GNN

Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

Adeesh Kolluru, Nima Shoghi, Muhammed Shuaibi, and 4 more authors

The Journal of Chemical Physics, Sep 2022

DOI
ML/GNN

Spherical Channels for Modeling Atomic Interactions

C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru, and 5 more authors

NeurIPS, Dec 2022

DOI
Catalysis

Site Geometry as a Descriptor for Catalyst Selectivity in Intermetallics

Unnatti Sharma, Angela Nguyen, Michael John Janik, and 1 more author

Preprint available at SSRN 4145497, Dec 2022
Catalysis

Detailed Microkinetics for the Oxidation of Exhaust Gas Emissions through Automated Mechanism Generation

Bjarne Kreitz, Patrick Lott, Jongyoon Bae, and 6 more authors

ACS Catalysis, Dec 2022

DOI
Catalysis/ML

Screening of bimetallic electrocatalysts for water purification with machine learning

Richard Tran, Duo Wang, Ryan Kingsbury, and 4 more authors

The Journal of Chemical Physics, Dec 2022

DOI
ML/GNN

Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials

Yuge Hu, Joseph Musielewicz, Zachary W Ulissi, and 1 more author

Machine Learning: Science and Technology, Dec 2022

Abs DOI

Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess the level of confidence during prediction, as ML models are not inherently physical and can therefore yield catastrophically incorrect predictions. Established a-posteriori UQ methods, including ensemble methods, the dropout method, the delta method, and various heuristic distance metrics, have limitations such as being computationally challenging for large models due to model re-training. In addition, the uncertainty estimates are often not rigorously calibrated. In this work, we propose combining the distribution-free UQ method, known as conformal prediction (CP), with the distances in the neural network’s latent space to estimate the uncertainty of energies predicted by neural network force fields. We evaluate this method (CP+latent) along with other UQ methods on two essential aspects, calibration, and sharpness, and find this method to be both calibrated and sharp under the assumption of independent and identically-distributed (i.i.d.) data. We show that the method is relatively insensitive to hyperparameters selected, and test the limitations of the method when the i.i.d. assumption is violated. Finally, we demonstrate that this method can be readily applied to trained neural network force fields with traditional and graph neural network architectures to obtain estimates of uncertainty with low computational costs on a training dataset of 1 million images to showcase its scalability and portability. Incorporating the CP method with latent distances offers a calibrated, sharp and efficient strategy to estimate the uncertainty of neural network force fields. In addition, the CP approach can also function as a promising strategy for calibrating uncertainty estimated by other approaches.
Catalysis/ML

The Open Catalyst Challenge 2021: Competition Report

Abhishek Das, Muhammed Shuaibi, Aini Palizhati, and 22 more authors

Dec 2022

PDF
Catalysis/ML

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Brook Wander, Kirby Broderick, and Zachary W. Ulissi

Catal. Sci. Technol., Jul 2022

Abs DOI

Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present Catlas, a framework that distributes and automates the generation of adsorbate-surface configurations and ML inference of DFT energies to achieve this goal. Catlas is open source, making ML assisted catalyst screenings easy and available to all. To demonstrate its efficacy, we use Catlas to explore catalyst candidates for the direct conversion of syngas to multi-carbon oxygenates. For this case study, we explore 947 stable/metastable binary, transition metal intermetallics as possible catalyst candidates. On this subset of materials, we are able to predict the adsorption energy of key descriptors, *CO and *OH, with good accuracy (0.16, 0.14 eV MAE, respectively). Using the projected selectivity towards C2+ oxygenates from an existing microkinetic model, we identified 144 candidate materials. For 10 promising candidates, DFT calculations reveal a good correlation with our assessment using ML. Among the top elemental combinations were Pt–Ti, Pd–V, Ni–Nb, and Ti–Zn, all of which appear unexplored experimentally.
Add.Manuf./ML

Predicting Oxidation Behavior of Multi-Principal Element Alloys by Machine Learning Methods

Jose A Loli, Amish R Chovatiya, Yining He, and 3 more authors

Oxidation of Metals, Jul 2022

DOI

2021

Catalysis/ML

Open Catalyst 2020 (OC20) Dataset and Community Challenges

Lowik Chanussot, Abhishek Das, Siddharth Goyal, and 14 more authors

ACS Catalysis, Apr 2021

DOI
Catalysis

Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H_2O_2 Synthesis through Active Motif Screening

Seoin Back, Jonggeol Na, and Zachary W. Ulissi

ACS Catalysis, Feb 2021

DOI
Catalysis/ML

Computational catalyst discovery: Active classification through myopic multiscale sampling

Kevin Tran, Willie Neiswanger, Kirby Broderick, and 3 more authors

The Journal of Chemical Physics, Feb 2021

DOI
Nanotechnology

Elimination of Multidrug-Resistant Bacteria by Transition Metal Dichalcogenides Encapsulated by Synthetic Single-Stranded DNA

Abhishek Debnath, Sanchari Saha, Duo O. Li, and 4 more authors

ACS Applied Materials & Interfaces, Feb 2021

PMID: 33570927

DOI
Catalysis/ML

Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy

Junwoong Yoon, Zhonglin Cao, Rajesh K Raju, and 5 more authors

Machine Learning: Science and Technology, Feb 2021

DOI
ML/GNN

Rotation Invariant Graph Neural Networks using Spin Convolutions

Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, and 4 more authors

arXiv preprint arXiv:2106.09575, Feb 2021

DOI
ML/GNN

The Open Catalyst Challenge 2021: Competition Report.

Abhishek Das, Muhammed Shuaibi, Aini Palizhati, and 8 more authors

In NeurIPS (Competition and Demos), Feb 2021

2020

Soft Materials

Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces

Junwoong Yoon, and Zachary W. Ulissi

Langmuir, Jan 2020

PMID: 31891511

DOI
ML/GNN

Methods for comparing uncertainty quantifications for material property predictions

Kevin Tran, Willie Neiswanger, Junwoong Yoon, and 3 more authors

Machine Learning: Science and Technology, May 2020

DOI
Catalysis

Parallelized Screening of Characterized and DFT-Modeled Bimetallic Colloidal Cocatalysts for Photocatalytic Hydrogen Evolution

Eric M Lopato, Emily A Eikey, Zoe C Simon, and 8 more authors

ACS Catalysis, Mar 2020

DOI
Education

Computational Notebooks in Chemical Engineering Curricula

Jonathan Verrett, Fani Boukouvala, Alexander Dowling, and 2 more authors

Chemical Engineering Education, Jul 2020
Catalysis/ML

Accelerated discovery of CO2 electrocatalysts using active machine learning

Miao Zhong, Kevin Tran, Yimeng Min, and 19 more authors

Nature, May 2020

Abs DOI

The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy1,2. Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy3-8. Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products9-16, and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 ± 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction17. Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C-C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.
Catalysis/ML

Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening

Geun Ho Gu, Juhwan Noh, Sungwon Kim, and 3 more authors

The Journal of Physical Chemistry Letters, Mar 2020

DOI
Catalysis

In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation

Seoin Back, Jonggeol Na, Kevin Tran, and 1 more author

Phys. Chem. Chem. Phys., Aug 2020

Abs DOI

Various databases of density functional theory (DFT) calculations for materials and adsorption properties are currently available. Using the Materials Project and GASpy databases of material stability and binding energies (H* and CO*), respectively, we evaluate multiple aspects of catalysts to discover active, stable, CO-tolerant, and cost-effective hydrogen evolution and oxidation catalysts. Finally, we suggest a few candidate materials for future experimental validations. We highlight that the stability analysis is easily obtainable but provides invaluable information to assess thermodynamic and electrochemical stability, bridging the gap between simulations and experiments. Furthermore, it reduces the number of expensive DFT calculations required to predict catalytic activities of surfaces by filtering out unstable materials.
Catalysis

Discovery of Acid-Stable Oxygen Evolution Catalysts: High-throughput Computational Screening of Equimolar Bimetallic Oxides

Seoin Back, Kevin Tran, and Zachary W Ulissi

ACS Applied Materials & Interfaces, Aug 2020

DOI
Catalysis/ML

Enabling robust offline active learning for machine learning potentials using simple physics-based priors

Muhammed Shuaibi, Saurabh Sivakumar, Rui Qi Chen, and 1 more author

Machine Learning: Science and Technology, Dec 2020

Abs DOI

Machine learning surrogate models for quantum mechanical simulations has enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a substantial amount of data to make reliable predictions of the potential energy landscape or careful active learning and uncertainty estimates. When starting with small datasets, convergence of active learning approaches is a major outstanding challenge which limited most demonstrations to online active learning. In this work we demonstrate a Δ-machine learning approach that enables stable convergence in offline active learning strategies by avoiding unphysical configurations with initial datasets as little as a single data point. We demonstrate our framework’s capabilities on a structural relaxation, transition state calculation, and molecular dynamics simulation, with the number of first principle calculations being cut down anywhere from 70-90%. The approach is incorporated and developed alongside AMP\textittorch, an open-source machine learning potential package, along with interactive Google Colab notebook examples.
Catalysis/ML

Differentiable Optimization for the Prediction of Ground State Structures (DOGSS)

Junwoong Yoon, and Zachary W Ulissi

Physical Review Letters, Dec 2020

DOI
Catalysis/ML

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

C Lawrence Zitnick, Lowik Chanussot, Abhishek Das, and 8 more authors

arXiv preprint arXiv:2010.09435, Dec 2020

DOI

2019

ML/GNN

Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts

Seoin Back, Junwoong Yoon, Nianhan Tian, and 3 more authors

The Journal of Physical Chemistry Letters, Jul 2019

DOI
Catalysis/ML

Towards Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks

Aini Palizhati, Wen Zhong, Kevin Tran, and 2 more authors

Journal of Chemical Information and Modeling, Jul 2019

DOI
Systems

Optimization-Based Design of Active and Stable Nanostructured Surfaces

Christopher L. Hanselman, Wen Zhong, Kevin Tran, and 2 more authors

The Journal of Physical Chemistry C, Jul 2019

DOI

2018

Catalysis/ML

Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution

Kevin Tran, and Zachary W. Ulissi

Nature Catalysis, Sep 2018

DOI
Catalysis

Copper Silver Thin Films with Metastable Miscibility for Oxygen Reduction Electrocatalysis in Alkaline Electrolytes

Drew Higgins, Melissa Wette, Brenna M. Gibbons, and 10 more authors

ACS Applied Energy Materials, May 2018

DOI
Catalysis

Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment

Michael T. Tang, Zachary W. Ulissi, and Karen Chan

The Journal of Physical Chemistry C, May 2018

DOI
Catalysis

Dynamic workflows for routine materials discovery in surface science

Kevin Tran, Aini Palizhati, Seoin Back, and 1 more author

Journal of Chemical Information and Modeling, May 2018

DOI

2017

Catalysis/ML

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Zachary W. Ulissi, A. J. Medford, Thomas Bligaard, and 1 more author

Nature Communications, Mar 2017

DOI
Catalysis/ML

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

Zachary W. Ulissi, Michael T. Tang, Jianping Xiao, and 9 more authors

ACS Catalysis, Oct 2017

DOI

2016

Nanotechnology

Persistently Auxetic Materials: Engineering the Poisson Ratio of 2D Self-Avoiding Membranes under Conditions of Non-Zero Anisotropic Strain

Zachary W Ulissi, Ananth Govind Rajan, and Michael S Strano

ACS Nano, Oct 2016

DOI
Catalysis/ML

Automated Discovery and Construction of Surface Phase Diagrams using Machine Learning

Zachary W Ulissi, Aayush R Singh, Charlie Tsai, and 1 more author

The Journal of Physical Chemistry Letters, Oct 2016

Abs DOI

Surface phase diagrams are necessary for understanding surface chemistry in electrochemical catalysis, where a range of adsorbates and coverages exist at varying applied potentials. These diagrams are typically constructed using intuition, which risks missing complex coverages and configurations at potentials of interest. More accurate cluster expansion methods are often difficult to implement quickly for new surfaces. We adopt a machine learning approach to rectify both issues. Using a Gaussian process regression model, the free energy of all possible adsorbate coverages for surfaces is predicted for a finite number of adsorption sites. Our result demonstrates a rational, simple, and systematic approach for generating accurate free-energy diagrams with reduced computational resources. The Pourbaix diagram for the IrO2(110) surface (with nine coverages from fully hydrogenated to fully oxygenated surfaces) is reconstructed using just 20 electronic structure relaxations, compared to approximately 90 using typical search methods. Similar efficiency is demonstrated for the MoS2 surface.

2015

Nanotechnology

A Mathematical Formulation and Solution of the CoPhMoRe Inverse Problem for Helically Wrapping Polymer Corona Phases on Cylindrical Substrates

Gili Bisker, Jiyoung Ahn, Sebastian Kruss, and 3 more authors

The Journal of Physical Chemistry C, Oct 2015

Abs DOI

Corona phase molecular recognition (CoPhMoRe) is a new technique that generates a nanoparticle-coupled polymer phase, capable of recognizing a specific molecule with high affinity and selectivity. CoPhMoRe has been successfully demonstrated using polymer wrapped single walled carbon nanotubes, resulting in molecular recognition complexes, to date, for dopamine, estradiol, riboflavin, and l-thyroxine, utilizing combinatorial library screening. A rational alternative design to this empirical library screening is to solve the mathematical formulation that we introduce as the CoPhMoRe inverse problem. This inverse problem seeks a linear function representing the position of monomers or functional groups along a polymer backbone that results in a 3-dimensional structure capable of recognizing a specific molecule when mapped to a nanoparticle surface. The potential solution space for such an inverse problem is infinite in general, but for the specific constraint of a helically wrapping polymer, mapped to a cylindrical nanoparticle, we show in this work that two types of inverse problems are exactly solvable. In one case, the polymer pitch and composition can be designed to allow for the specific binding of a small molecule analyte in the occluded space on the nanotube surface. In the other, a larger macromolecule can interact with a deformed helix, which partially conforms to it. A simplified, coarse-grained molecular model of a helically wrapping polymer demonstrates the inhomogeneous binding potential formed by a wrapping with a given pitch. Calculating the potential maps for various pitch values illustrates that there is an optimal pitch that enables the selective and specific binding of the target analyte. An additional coarse-grained model of a helical wrapping by a polymer consisting of alternating hydrophobic–hydrophilic segments demonstrates the resulting deformed helix corona around the nanotube, which forms accessible binding pockets between the hydrophilic loops. While these are the idealized forms of actual CoPhMoRe phases, the formation and solution of such inverse problems 5 serve to reduce the dimensionality of library screening for CoPhMoRe discoveries, as well as provide a theoretical basis for understanding certain types of CoPhMoRe recognition.
Nanotechnology

A 2D Equation-of-State Model for Corona Phase Molecular Recognition on Single-Walled Carbon Nanotube and Graphene Surfaces

Zachary W. Ulissi, Jingqing Zhang, Vishnu Sresht, and 2 more authors

Langmuir, Oct 2015

DOI

2014

Nanotechnology

Deterministic modelling of carbon nanotube near-infrared solar cells

Darin O. Bellisario, Rishabh M. Jain, Zachary W. Ulissi, and 1 more author

Energy Environ. Sci., Oct 2014

DOI
Nanotechnology

Quantitative Theory of Adsorptive Separation for the Electronic Sorting of Single-Walled Carbon Nanotubes

Rishabh M. Jain, Kevin Tvrdy, Rebecca Han, and 2 more authors

ACS Nano, Oct 2014

Abs DOI

Recently, several important advances in techniques for the separation of single-walled carbon nanotubes (SWNTs) by chiral index have been developed. These new methods allow for the separation of SWNTs through selective adsorption and desorption of different (n,m) chiral indices to and from a specific hydrogel. Our group has previously developed a kinetic model for the chiral elution order of separation; however, the underlying mechanism that allows for this separation remains unknown. In this work, we develop a quantitative theory that provides the first mechanistic insights for the separation order and binding kinetics of each SWNT chirality (n,m) based on the surfactant-induced, linear charge density, which we find ranges from 0.41 e–/nm for (7,3) SWNTs in 17 mM sodium dodecyl sulfate (SDS) to 3.32 e–/nm for (6,5) SWNTs in 105 mM SDS. Adsorption onto the hydrogel support is balanced by short-distance hard-surface and long-distance electrostatic repulsive SWNT/substrate forces, the latter of which we postulate is strongly dependent on surfactant concentration and ultimately leads to gel-based single-chirality semiconducting SWNT separation. These molecular-scale properties are derived using bulk-phase, forward adsorption rate constants for each SWNT chirality in accordance with our previously published model. The theory developed here quantitatively describes the experimental elution profiles of 15 unique SWNT chiralities as a function of anionic surfactant concentration between 17 and 105 mM, as well as phenomenological observations of the impact of varying preparatory conditions such as extent of ultrasonication and ultracentrifugation. We find that SWNT elution order and separation efficiency are primarily driven by the morphological change of SDS surfactant wrapping on the surface of the nanotube, mediated by SWNT chirality and the ionic strength of the surrounding medium. This work provides a foundational understanding for high-purity, preparative-scale separation of as-produced SWNT mixtures into isolated, single-chirality fractions.
Nanotechnology

Spatiotemporal Intracellular Nitric Oxide Signaling Captured using Internalized, Near Infrared Fluorescent Carbon Nanotube Nanosensors

Zachary W. Ulissi, Fatih Sen, Xun Gong, and 7 more authors

Nano Letters, Oct 2014

Abs DOI

Fluorescent nanosensor probes have suffered from limited molecular recognition and a dearth of strategies for spatial-temporal operation in cell culture. In this work, we spatially imaged the dynamics of nitric oxide (NO) signaling, important in numerous pathologies and physiological functions, using intracellular near-infrared fluorescent single-walled carbon nanotubes. The observed spatial-temporal NO signaling gradients clarify and refine the existing paradigm of NO signaling based on averaged local concentrations. This work enables the study of transient intracellular phenomena associated with signaling and therapeutics.
Nanotechnology

Low Dimensional Carbon Materials for Applications in Mass and Energy Transport

Qing Hua Wang, Darin O. Bellisario, Lee W. Drahushuk, and 7 more authors

Chemistry of Materials, Jan 2014

Abs DOI

Low dimensional materials are those that possess at least one physical boundary small enough to confine the electrons or phonons. This quantum confinement reduces the dimensionality of the material and imparts unique and novel properties that are not seen in their bulk forms. Examples include quantum dots (0-D), carbon nanotubes (1-D), and graphene (2-D). Accordingly, these materials exhibit new concepts in mass and energy transport that can be exploited for technological applications. In this Perspective, we review several topics related to mass and energy transport in and around carbon-based low dimensional materials. Recent developments in the study of matter being transported through carbon nanotube and graphene nanopores are reviewed, as well as applications of excitonic, thermal, and electronic energy transport in carbon nanotubes. The nanometer-scale interior of a single-walled carbon nanotube (SWCNT) has been studied as a unique nanopore, exhibiting periodic ionic conduction currents and dimensionally confined material phases. The mechanism of gas transport through atomic-scale holes in graphene, which is otherwise a perfect barrier material, has been analytically studied. These insights on nanoscale mass transport will have important implications in systems ranging from biological nanopores to advanced water filtration devices. The electronic structure of semiconducting SWCNTs allows photogenerated excitons to be harnessed for single-molecule biosensing and as elements of a new class of all-nanocarbon near-infrared photovoltaics. The extremely high thermal and electrical conductivities of carbon nanotubes allows the generation of electrical energy from chemical reactions. The understanding of how low dimensional physics and chemistry influences mass and energy transport will facilitate the application of these materials to a variety of scientific challenges.

2013

Nanotechnology

A Quantitative and Predictive Model of Electromigration-Induced Breakdown of Metal Nanowires

Darin O. Bellisario, Zachary W. Ulissi, and Michael S. Strano

Journal of Physical Chemistry C, Jun 2013

Abs DOI

An isothermal model of electromigration breakdown of metal nanowires 80-700 nm in diameter is developed and validated using experimental data obtained from isolated cylindrical Au nanowires. The model considers electromigration from an applied current producing a net flux of metal atoms, reducing the nanowire radius and conductivity precipitously and accounting for both mass and electronic carrier transport. The model successfully predicts the observed critical failure current, the correct scaling with nanowire radius to 3/2 power, and the impedance evolution prior to breakdown. Application to the case where feedback control is employed to limit the rate of nanowire thinning reproduces key features, including slowed necking, a threshold current and voltage after which lower bias is required to advance formation, and the dependence of these values on feedback parameters.
Nanotechnology

Charge Transfer at Junctions of a Single Layer of Graphene and a Metallic Single Walled Carbon Nanotube

Geraldine L. C. Paulus, Qing Hua Wang, Zachary W. Ulissi, and 5 more authors

Small, Jun 2013

Abs DOI

Junctions between a single walled carbon nanotube (SWNT) and a monolayer of graphene are fabricated and studied for the first time. A single layer graphene (SLG) sheet grown by chemical vapor deposition (CVD) is transferred onto a SiO2/Si wafer with aligned CVD-grown SWNTs. Raman spectroscopy is used to identify metallic-SWNT/SLG junctions, and a method for spectroscopic deconvolution of the overlapping G peaks of the SWNT and the SLG is reported, making use of the polarization dependence of the SWNT. A comparison of the Raman peak positions and intensities of the individual SWNT and graphene to those of the SWNT-graphene junction indicates an electron transfer of 1.12 x 1013 cm-2 from the SWNT to the graphene. This direction of charge transfer is in agreement with the work functions of the SWNT and graphene. The compression of the SWNT by the graphene increases the broadening of the radial breathing mode (RBM) peak from 3.6 +/- 0.3 to 4.6 +/- 0.5 cm-1 and of the G peak from 13 +/- 1 to 18 +/- 1 cm-1, in reasonable agreement with molecular dynamics simulations. However, the RBM and G peak position shifts are primarily due to charge transfer with minimal contributions from strain. With this method, the ability to dope graphene with nanometer resolution is demonstrated.
Nanotechnology

Stochastic Pore Blocking and Gating in PDMS-Glass Nanopores from Vapor-Liquid Phase Transitions

Steven Shimizu, Mark Ellison, Kimberly Aziz, and 5 more authors

Journal of Physical Chemistry C, May 2013

Abs DOI

Polydimethylsiloxane (PDMS) is commonly used in research for microfluidic devices and for making elastomeric stamps for soft lithography. Its biocompatibility and nontoxicitiy also allow it to be used in personal care, food, and medical products. Herein we report a phenomenon observed when patch clamp, a technique normally used to study biological ion channels, is performed on both grooved and planar PDMS surfaces, resulting in stochastic current fluctuations that are due to a nanopore being formed at the interface of the PDMS and glass surfaces and being randomly blocked. Deformable pores between 1.9 +/- 0.7 and 7.4 +/- 2.1 nm in diameter, depending on the calculation method, form upon patching to the surface. Coulter blocking and nanoprecipitation are ruled out, and we instead propose a mechanism of stochastic current fluctuations arising from transitions between vapor and liquid phases, consistent with similar observations and theory from statistical mechanics literature. Interestingly, we find that [Ru(bpy)(3)](2+), a common probe molecule employed in nanopore research, physisorbs inside these hydrophobic nanopores blocking all ionic current flow at concentrations higher than 1 X 10(-4) M, despite the considerably larger pore diameter relative to the molecule. Patch clamp methods are promising for the study of stochastic current fluctuations and other transport phenomenon in synthetic nanopore systems.
Systems

Control of nano and microchemical systems

Zachary W. Ulissi, Michael S. Strano, and Richard D. Braatz

Computers & Chemical Engineering, Apr 2013

Abs DOI

Many advances in the development of nano and microchemical systems have occurred in the last decade. These systems have significant associated identification and control challenges, including high state dimensionality, limitations in real-time measurements and manipulated variables, and significant uncertainties described by non-Gaussian distributions. Some strategies for addressing these challenges are summarized, which include exploiting structure within the stochastic Master equations that describe molecular interactions, manipulating molecular bonds at system boundaries, and manipulating molecules and nanoscale objects through magnetic and electric fields. The strategies are illustrated in a variety of applications that include the estimation of nucleation kinetics of protein and pharmaceutical crystals within fluidic devices, the estimation of concentration fields using DNA-wrapped single-walled carbon nanotube-based sensor arrays, the simultaneous control of nanoscale geometry and electrical activation during thermal annealing in a semiconductor material, and the control of nanostructure formation on surfaces. Promising directions for research and technology development are identified for the next decade. (C) 2012 Elsevier Ltd. All rights reserved.
Nanotechnology

Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes

Wonjoon Choi, Zachary W Ulissi, Steven FE Shimizu, and 3 more authors

Nature Communications, Apr 2013

DOI
Nanotechnology

Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes

Jingqing Zhang, Markita P. Landry, Paul W. Barone, and 24 more authors

Nature Nanotechnology, Dec 2013

Abs DOI

Understanding molecular recognition is of fundamental importance in applications such as therapeutics, chemical catalysis and sensor design. The most common recognition motifs involve biological macromolecules such as antibodies and aptamers. The key to biorecognition consists of a unique three-dimensional structure formed by a folded and constrained bioheteropolymer that creates a binding pocket, or an interface, able to recognize a specific molecule. Here, we show that synthetic heteropolymers, once constrained onto a single-walled carbon nanotube by chemical adsorption, also form a new corona phase that exhibits highly selective recognition for specific molecules. To prove the generality of this phenomenon, we report three examples of heteropolymer-nanotube recognition complexes for riboflavin, L-thyroxine and oestradiol. In each case, the recognition was predicted using a two-dimensional thermodynamic model of surface interactions in which the dissociation constants can be tuned by perturbing the chemical structure of the heteropolymer. Moreover, these complexes can be used as new types of spatiotemporal sensors based on modulation of the carbon nanotube photoemission in the near-infrared, as we show by tracking riboflavin diffusion in murine macrophages.

2012

Catalysis

Modelling and development of photoelectrochemical reactor for H-2 production

C. Carver, Zachary W. Ulissi, C. K. Ong, and 3 more authors

International Journal of Hydrogen Energy, Feb 2012

Abs DOI

Photoelectrolysis of aqueous solutions, using one or more semiconducting electrodes in a photoelectrochemical reactor, is a potentially attractive process for hydrogen production because of its prospectively high energy efficiency, simplicity and potentially low cost. The design requirements and preliminary results of modelling a photoelectrochemical (PEC) reactor are described. Potential and current density distributions, due to ohmic potential losses in thin (non-photo) anodes on poorly conducting fluoride-doped tin oxide coated glass substrates, were modelled. The predicted current densities decayed rapidly from the terminals at the edges, towards the centre of a 0.1 x 0.1 m(2) anode, so limiting scale-up with such substrates. Spatial distributions of dissolved oxygen concentrations were also modelled, aiming to define operating conditions that would avoid forming bubbles, which reflect light specularly decreasing photon absorption efficiencies of photoelectrodes. The implications for the future optimization of the reactor are discussed. Copyright (C) 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
Nanotechnology

Observation of Oscillatory Surface Reactions of Riboflavin, Trolox, and Singlet Oxygen Using Single Carbon Nanotube Fluorescence Spectroscopy

Fatih Sen, Ardemis A. Boghossian, Selda Sen, and 3 more authors

ACS Nano, Dec 2012

Abs DOI

Single-molecule fluorescent microscopy allows semiconducting single-walled carbon nanotubes (SWCNTs) to detect the adsorption and desorption of single adsorbate molecules as a stochastic modulation of emission intensity. In this study, we identify and assign the signature of the complex decomposition and reaction pathways of riboflavin in the presence of the free radical scavenger Trolox using DNA-wrapped SWCNT sensors dispersed onto an aminopropyltriethoxysilane (APTES) coated surface. SWCNT emission is quenched by riboflavin-induced reactive oxygen species (ROS), but increases upon the adsorption of Trolox, which functions as a reductive brightening agent. Riboflavin has two parallel reaction pathways, a Trolox oxidizer and a photosensitizer for singlet oxygen and superoxide generation. The resulting reaction network can be detected in real time in the vicinity of a single SWCNT and can be completely described using elementary reactions and kinetic rate constants measured independently. The reaction mechanism results in an oscillatory fluorescence response from each SWCNT, allowing for the simultaneous detection of multiple reactants. A series-parallel kinetic model is shown to describe the critical points of these oscillations, with partition coefficients on the order of 10(6) - 10(4) for the reactive oxygen and excited state species. These results highlight the potential for SWCNTs to characterize complex reaction networks at the nanometer scale.

2011

Nanotechnology

The chemical dynamics of nanosensors capable of single-molecule detection

Ardemis A. Boghossian, Jingqing Zhang, François T. Le Floch-Yin, and 8 more authors

The Journal of Chemical Physics, Dec 2011

DOI
Catalysis

Effect of multiscale model uncertainty on identification of optimal catalyst properties

Zachary W. Ulissi, Vinay Prasad, and Dionisios Vlachos

Journal of Catalysis, Jul 2011

Abs DOI

Computer-based catalyst design has been a long standing dream of the chemistry community for replacing tedious and expensive experimental trial-and-error. While first-principle kinetic modeling emerges as a powerful tool for catalyst selection, it has mainly been limited to using a single catalyst descriptor, simplified chemical kinetic models, and assumptions that question the predictive capability of computational results in the absence of addressing the effect of error in kinetic parameters. Here, we introduce a new framework to address the effect of model uncertainty on optimal catalyst property identification. The framework is applied to the ammonia decomposition reaction for CO-free H(2) production for fuel cells. It is shown that a range of materials, rather than a single material, should be experimentally screened. Among kinetic model parameters, the often neglected adsorbate-adsorbate interactions can have a profound effect on catalyst selection. The importance of lateral interactions is confirmed with recent experimental data. (C) 2011 Elsevier Inc. All rights reserved.
Nanotechnology

Carbon Nanotubes as Molecular Conduits: Advances and Challenges for Transport through Isolated Sub-2 nm Pores

Zachary W. Ulissi, Steven Shimizu, Chang Young Lee, and 1 more author

Journal of Physical Chemistry Letters, Nov 2011

Abs DOI

Devices that explore transport through the narrowest diameter single-walled carbon nanotubes (SWCNTs) have only recently been enabled by advances in SWCNT synthesis methods and experimental design. These devices hold promise as next-generation sensors, platforms for water desalination, proton conduction, energy storage, and to directly probe molecular transport under significant geometric confinement In this Perspective, we first describe this new generation of devices and then highlight two important concepts that have emerged from recent work. First, the most reliable way to identify transport is to borrow techniques from the biological and silicon nanopore communities and analyze the discrete stochastic events caused by molecules blocking the SWCNT channel. Second, it is nearly impossible to isolate mass transport within a SWCNT without a substantial suppression of leakage transport and around the SWCNT. To highlight this, we discuss experiments showing water transport along the exterior of SWCNTs. Finally, we describe some further innovations to these devices in the near future that will allow for a more complete understanding of confined molecular transport.
Systems

Applicability of Birth-Death Markov Modeling for Single-Molecule Counting Using Single-Walled Carbon Nanotube Fluorescent Sensor Arrays

Zachary W. Ulissi, Jingqing Zhang, Ardemis A. Boghossian, and 4 more authors

Journal of Physical Chemistry Letters, Jul 2011

Abs DOI

In recent work, we have shown that d(AT)(15) DNA-wrapped single-walled carbon nanotubes (SWNTs) are able to detect the adsorption and desorption of single molecules of nitric oxide (NO) from the surface by quenching of the near-infrared fluorescence (Zhang et al. J. Am. Chem. Soc. 2011, 133, 567-581). A central question is how to estimate the local concentration from stochastic dynamics for these types of sensors. Herein, we employ an exact solution to the birth-death Markov model to estimate the local analyte concentration from the stochastic dynamics. Conditions are derived for the intrinsic variance displayed by identical sensor elements, and the homogeneity of the environment is assessed by comparing experimental sensor-to-sensor variance with this limit. We find that d(AT)(15) DNA-wrapped SWNTs demonstrate variances that are close to the idealized limit at relatively high NO concentrations (19.4 mu M). At 780 nM, the sensor-to-sensor variance is approximately double the idealized value, indicating marginal variation in the SWNT array. An NO adsorption coefficient of 2.6 x 10(-4) [mu M(-1)] is identified, and we outline how to predict the local analyte concentration from the sensor dynamics.

2010

Catalysis

High throughput multiscale modeling for design of experiments, catalysts, and reactors: Application to hydrogen production from ammonia

Vinay Prasad, Ayman Karim, Zachary W. Ulissi, and 2 more authors

Chemical Engineering Science, Jan 2010

Abs DOI

A novel approach for design of experiments (DOE) is outlined that combines high throughput multiscale modeling, sensitivity analysis, and information extraction from massive computational data using informatics tools. This approach is implemented by conducting experiments of ammonia decomposition on a Ru/gamma-Al(2)O(3) catalyst in a fixed bed reactor. It is shown that a relatively small number of experiments chosen from this new DOE approach can enable refinement of microkinetic models and render them predictive over the (large) experimentally important parameter space. Microkinetic models are subsequently used for process and product design. Specifically, a membrane fixed bed reactor is simulated and is shown to outperform the conventional fixed bed reactor at intermediate temperatures for hydrogen production. Also, the attributes of the best catalyst for ammonia decomposition are identified as a function of processing conditions. It is shown that for NH(3) decomposition, processing conditions do not significantly affect the best catalyst choice. In contrast, fundamental physicochemical phenomena, such as adsorbate adsorbate interactions, can have a profound effect on catalyst discovery. (C) 2009 Elsevier Ltd. All rights reserved.

2006

Biomed. Optics

Visualization of biological texture using correlation coefficient images

Alexander P Sviridov, Zachary W. Ulissi, Victor V Chernomordik, and 2 more authors

Journal of Biomedical Optics, Jan 2006

DOI