Zachary W. Ulissi

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

Nanomaterials are often functionalized with biological ligands to enable their use as sensors of biological activity. However, the intricacies of nano-bio interactions are poorly understood, which hampers our ability to design nanomaterial-based sensors. Current experimental tools have been unable to visualize interactions occurring on the nano-bio interface with the spatial and temporal resolution needed to quantify biological interactions at their fundamental length and time scales. To fill the need for concurrent visualization of nanoparticles and biomolecules, we have combined two common microscopy techniques, one being for the study of biomolecules and the other for the study of nanoparticles, into a single instrument that has the capacity to study both nanoparticles and biological molecules simultaneously with spatial and temporal resolution that is appropriate for nanoscale interactions. This novel instrument has been used for the characterization of high-sensitivity sensors by designing synthetic biological polymers to selectively encapsulate single-wall carbon nanotubes. The design of synthetic sensing tools based on nanoparticle-biomolecule hybrids is promising for areas in need of high-specificity sensors, such as label-free detection of molecules within a cell, nanoparticle-based diagnostic tools, and nanoscale therapeutics. We introduce three examples of high-sensitivity and high-selectivity synthetic sensors that have the ability to detect a variety of molecules on a single-molecule scale: riboflavin, L-thyroxine, and oestradiol. These sensors have been used to detect and quantify riboflavin levels within a live murine macrophage cell in real-time. The findings provided herein will enable the development of early-onset diagnostic tools at the level of a single cell.

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

Nanopores that approach molecular dimensions demonstrate exotic transport behaviour and are theoretically predicted to display discontinuities in the diameter dependence of interior ion transport because of structuring of the internal fluid. No experimental study has been able to probe this diameter dependence in the 0.5-2 nm diameter regime. Here we observe a surprising fivefold enhancement of stochastic ion transport rates for single-walled carbon nanotube centered at a diameter of approximately 1.6 nm. An electrochemical transport model informed from literature simulations is used to understand the phenomenon. We also observe rates that scale with cation type as Li(+)>K(+)>Cs(+)>Na(+) and pore blocking extent as K(+)>Cs(+)>Na(+)>Li(+) potentially reflecting changes in hydration shell size. Across several ion types, the pore-blocking current and inverse dwell time are shown to scale linearly at low electric field. This work opens up new avenues in the study of transport effects at the nanoscale.

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

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.

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

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.

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

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.

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

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.

The chemical dynamics of nanosensors capable of single-molecule detection

Recent advances in nanotechnology have produced the first sensor transducers capable of resolving the adsorption and desorption of single molecules. Examples include near infrared fluorescent single-walled carbon nanotubes that report single-molecule binding via stochastic quenching. A central question for the theory of such sensors is how to analyze stochastic adsorption events and extract the local concentration or flux of the analyte near the sensor. In this work, we compare algorithms of varying complexity for accomplishing this by first constructing a kinetic Monte Carlo model of molecular binding and unbinding to the sensor substrate and simulating the dynamics over wide ranges of forward and reverse rate constants. Methods involving single-site probability calculations, first and second moment analysis, and birth-and-death population modeling are compared for their accuracy in reconstructing model parameters in the presence and absence of noise over a large dynamic range. Overall, birth-and-death population modeling was the most robust in recovering the forward rate constants, with the first and second order moment analysis very efficient when the forward rate is large (>10(-3) s(-1)). The precision decreases with increasing noise, which we show masks the existence of underlying states. Precision is also diminished with very large forward rate constants, since the sensor surface quickly and persistently saturates.

Control of nano and microchemical systems

Abstract 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.

2D equation-of-state model for corona phase molecular recognition on single-walled carbon nanotube and graphene surfaces.

Corona phase molecular recognition (CoPhMoRe) has been recently introduced as a means of generating synthetic molecular recognition sites on nanoparticle surfaces. A synthetic heteropolymer is adsorbed and confined to the surface of a nanoparticle, forming a corona phase capable of highly selective molecular recognition due to the conformational imposition of the particle surface on the polymer. In this work, we develop a computationally predictive model for analytes adsorbing onto one type of polymer corona phase composed of hydrophobic anchors on hydrophilic loops around a single-walled carbon nanotube (SWCNT) surface using a 2D equation of state that takes into consideration the analyte-polymer, analyte-nanoparticle, and polymer-nanoparticle interactions using parameters determined independently from molecular simulation. The SWCNT curvature is found to contribute weakly to the overall interaction energy, exhibiting no correlation for three of the corona phases considered, and differences of less than 5% and 20% over a larger curvature range for two other corona phases, respectively. Overall, the resulting model for this anchor-loop CoPhMoRe is able to correctly predict 83% of an experimental 374 analyte-polymer library, generating experimental fluorescence responses within 20% error of the experimental values. The modeling framework presented here represents an important step forward in the design of suitable polymers to target specific analytes.

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

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 × 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.