Surface-Enhanced Infrared Absorption Spectroscopy by Resonant Vibrational Coupling with Plasmonic Metal Oxide Nanocrystals.

Coupling between plasmonic resonances and molecular vibrations in nanocrystals (NCs) offers a promising approach for detecting molecules at low concentrations and discerning their chemical identities. Metallic NC superlattices can enhance vibrational signals under far-field detection by generating a myriad of intensified electric field hot spots between the NCs. Yet, their effectiveness is limited by the fixed electron concentration dictated by the metal composition and inefficient hot spot creation due to the large mode volume. Doped metal oxide NCs, such as tin-doped indium oxide (ITO), could overcome these limitations by enabling broad tunability of resonance frequencies in the mid-infrared range through independent variation of size and doping concentration. This study investigates the potential of close-packed ITO NC monolayers for surface-enhanced infrared absorption by quantifying trends in the coupling between their plasmon modes and various molecular vibrations. We show that maximum vibrational signal intensity occurs in monolayers composed of larger, more highly doped NCs, where the plasmon resonance peak lies at higher frequency than the molecular vibration. Using finite element and mutual polarization methods, we establish that near-field enhancement is stronger on the low-frequency side of the plasmon resonance and for more strongly coupled plasmonic NCs, thus rationalizing the design rules we experimentally uncovered. Our results can guide the development of optimal metal oxide NC-based superstructures for sensing target molecules or modifying their chemical properties through vibrational coupling.

Illuminating Disorder: Optical Properties of Complex Plasmonic Assemblies.

The optical properties of disordered plasmonic nanoparticle assemblies can be continuously tuned through the structural organization and composition of their colloidal building blocks. However, progress in the design and experimental realization of these materials has been limited by challenges associated with controlling and characterizing disordered assemblies and predicting their optical properties. This Perspective discusses integrated studies of experimental assembly of disordered optical materials, such as doped metal oxide nanocrystal gels and metasurfaces, with electromagnetic computations on large-scale simulated structures. The simulations prove vital for connecting experimental parameters to disordered structural motifs and optical properties, revealing structure-property relations that inform design choices. Opportunities are identified for optimizing optical property designs for disordered materials using computational inverse methods and tools from machine learning.

Antibody-Conjugated Polymersomes with Encapsulated Indocyanine Green J-Aggregates and High Near-Infrared Absorption for Molecular Photoacoustic Cancer Imaging.

Imaging plays a critical role in all stages of cancer care from early detection to diagnosis, prognosis, and therapy monitoring. Recently, photoacoustic imaging (PAI) has started to emerge into the clinical realm due to its high sensitivity and ability to penetrate tissues up to several centimeters deep. Herein, we encapsulated indocyanine green J (ICGJ) aggregate, one of the only FDA-approved organic exogenous contrast agents that absorbs in the near-infrared range, at high loadings up to ∼40% w/w within biodegradable polymersomes (ICGJ-Ps) composed of poly(lactide-co-glycolide-b-polyethylene glycol) (PLGA-b-PEG). The small Ps hydrodynamic diameter of 80 nm is advantageous for in vivo applications, while directional conjugation with epidermal growth factor receptor (EGFR) targeting cetuximab antibodies renders molecular specificity. Even when exposed to serum, the ∼11 nm-thick membrane of the Ps prevents dissociation of the encapsulated ICGJ for at least 48 h with a high ratio of ICGJ to monomeric ICG absorbances (i.e., I895/I780 ratio) of approximately 5.0 that enables generation of a strong NIR photoacoustic (PA) signal. The PA signal of polymersome-labeled breast cancer cells is proportional to the level of cellular EGFR expression, indicating the feasibility of molecular PAI with antibody-conjugated ICGJ-Ps. Furthermore, the labeled cells were successfully detected with PAI in highly turbid tissue-mimicking phantoms up to a depth of 5 mm with the PA signal proportional to the amount of cells. These data show the potential of molecular PAI with ICGJ-Ps for clinical applications such as tumor margin detection, evaluation of lymph nodes for the presence of micrometastasis, and laparoscopic imaging procedures.

Wavelength Tunable Infrared Perfect Absorption in Plasmonic Nanocrystal Monolayers.

The ability to efficiently absorb light in ultrathin (subwavelength) layers is essential for modern electro-optic devices, including detectors, sensors, and nonlinear modulators. Tailoring these ultrathin films' spectral, spatial, and polarimetric properties is highly desirable for many, if not all, of the above applications. Doing so, however, often requires costly lithographic techniques or exotic materials, limiting scalability. Here we propose, demonstrate, and analyze a mid-infrared absorber architecture leveraging monolayer films of nanoplasmonic colloidal tin-doped indium oxide nanocrystals (ITO NCs). We fabricate a series of ITO NC monolayer films using the liquid-air interface method; by synthetically varying the Sn dopant concentration in the NCs, we achieve spectrally selective perfect absorption tunable between wavelengths of two and five micrometers. We achieve monolayer thickness-controlled coupling strength tuning by varying NC size, allowing access to different coupling regimes. Furthermore, we synthesize a bilayer film that enables broadband absorption covering the entire midwave IR region (λ = 3-5 µm). We demonstrate a scalable platform, with perfect absorption in monolayer films only hundredths of a wavelength in thickness, enabling strong light-matter interaction, with potential applications for molecular detection and ultrafast nonlinear optical applications.

Structural Control of Plasmon Resonance in Molecularly Linked Metal Oxide Nanocrystal Gel Assemblies.

Nanocrystal gels exhibit collective optical phenomena based on interactions among their constituent building blocks. However, their inherently disordered structures have made it challenging to understand, predict, or design properties such as optical absorption spectra that are sensitive to the coupling between the plasmon resonances of the individual nanocrystals. Here, we bring indium tin oxide nanocrystal gels under chemical control and show that their infrared absorption can be predicted and systematically tuned by selecting the nanocrystal sizes and compositions and molecular structures of the link-mediating surface ligands. Thermoreversible assemblies with metal-terpyridine links form reproducible gel architectures, enabling us to derive a plasmon ruler that governs the spectral shifts upon gelation, predicated on the nanocrystal and ligand compositions. This empirical guide is validated using large-scale, many-bodied simulations to compute the optical spectra of gels with varied structural parameters. Based on the derived plasmon ruler, we design and demonstrate a nanocrystal mixture whose spectrum exhibits distinctive line narrowing upon assembly.

Characterizing Protein-Protein Interactions and Viscosity of a Monoclonal Antibody from Low to High Concentration Using Small-Angle X-ray Scattering and Molecular Dynamics Simulations.

Understanding protein-protein interactions and formation of reversible oligomers (clusters) in concentrated monoclonal antibody (mAb) solutions is necessary for designing stable, low viscosity (η) concentrated formulations for processing and subcutaneous injection. Here we characterize the strength (K) of short-range anisotropic attractions (SRA) for 75-200 mg/mL mAb2 solutions at different pH and cosolute conditions by analyzing structure factors (Seff(q)) from small-angle X-ray scattering (SAXS) using coarse-grained molecular dynamics simulations. Best fit simulations additionally provide cluster size distributions, fractal dimensions, cluster occluded volume, and mAb coordination numbers. These equilibrium properties are utilized in a model to account for increases in viscosity caused by occluded volume in the clusters (packing effects) and dissipation of stress across lubricated fractal clusters. Seff(q) is highly sensitive to K at 75 mg/mL where mAbs can mutually align to form SRA contacts but becomes less sensitive at 200 mg/mL as steric repulsion due to packing becomes dominant. In contrast, η at 200 mg/mL is highly sensitive to SRA and the average cluster size from SAXS/simulation, which is observed to track the cluster relaxation time from shear thinning. By analyzing the distribution of sub-bead hot spots on the 3D mAb surface, we identify a strongly attractive hydrophobic patch in the complementarity determining region (CDR) at pH 4.5 that contributes to the high K and consequently large cluster sizes and high η. Adding NaCl screens electrostatic interactions and increases the impact of hydrophobic attraction on cluster size and raises η, whereas nonspecific binding of Arg attenuates all SRA, reducing η. The hydrophobic patch is absent at higher pH values, leading to smaller K, smaller clusters, and lower η. This work constitutes a first attempt to use SAXS and CG modeling to link both structural and rheological properties of concentrated mAb solutions to the energetics of specific hydrophobic patches on mAb surfaces. As such, our work opens an avenue for future research, including the possibility of designing coarse-grained models with physically meaningful interacting hot spots.

Correction: Magnetic field enabled in situ control over the structure and dynamics of colloids interacting via SALR potentials.

Correction for 'Magnetic field enabled in situ control over the structure and dynamics of colloids interacting via SALR potentials' by Hashir M. Gauri et al., Soft Matter, 2023, 19, 4439-4448, https://doi.org/10.1039/D3SM00354J.

Hierarchically Doped Plasmonic Nanocrystal Metamaterials.

Assembling plasmonic nanocrystals in regular superlattices can produce effective optical properties not found in homogeneous materials. However, the range of these metamaterial properties is limited when a single nanocrystal composition is selected for the constituent meta-atoms. Here, we show how continuously varying doping at two length scales, the atomic and nanocrystal scales, enables tuning of both the frequency and bandwidth of the collective plasmon resonance in nanocrystal-based metasurfaces, while these features are inextricably linked in single-component superlattices. Varying the mixing ratio of indium tin oxide nanocrystals with different dopant concentrations, we use large-scale simulations to predict the emergence of a broad infrared spectral region with near-zero permittivity. Experimentally, tunable reflectance and absorption bands are observed, owing to in- and out-of-plane collective resonances. These spectral features and the predicted strong near-field enhancement establish this multiscale doping strategy as a powerful new approach to designing metamaterials for optical applications.

Magnetic field enabled in situ control over the structure and dynamics of colloids interacting via SALR potentials.

Colloidal suspensions are an ideal model for studying crystallization, nucleation, and glass transition mechanisms, due to the precise control of interparticle interactions by changing the shape, charge, or volume fraction of particles. However, these tuning parameters offer insufficient active control over interparticle interactions and reconfigurability of assembled structures. Dynamic control over the interparticle interactions can be obtained through the application of external magnetic fields that are contactless and chemically inert. In this work, we demonstrate the dual nature of magnetic nanoparticle dispersions to program interactions between suspended nonmagnetic microspheres using an external magnetic field. The nanoparticle dispersion simultaneously behaves as a continuous magnetic medium at the microscale and a discrete medium composed of individual particles at the nanoscale. This enables control over a depletion attractive potential and the introduction of a magnetic repulsive potential, allowing a reversible transition of colloidal structures within a rich phase diagram by applying an external magnetic field. Active control over competing interactions allows us to create a model system encompassing a range of states, from large fractal clusters to low-density Wigner glass states. Monitoring the dynamics of colloidal particles reveals dynamic heterogeneity and a marked slowdown associated with approaching the Wigner glass state.

Subclass Effects on Self-Association and Viscosity of Monoclonal Antibodies at High Concentrations.

The effects of a subclass of monoclonal antibodies (mAbs) on protein-protein interactions, formation of reversible oligomers (clusters), and viscosity (η) are not well understood at high concentrations. Herein, we quantify a short-range anisotropic attraction between the complementarity-determining region (CDR) and CH3 domains (KCDR-CH3) for vedolizumab IgG1, IgG2, or IgG4 subclasses by fitting small-angle X-ray scattering (SAXS) structure factor Seff(q) data with an extensive library of 12-bead coarse-grained (CG) molecular dynamics simulations. The KCDR-CH3 bead attraction strength was isolated from the strength of long-range electrostatic repulsion for the full mAb, which was determined from the theoretical net charge and a scaling parameter ψ to account for solvent accessibility and ion pairing. At low ionic strength (IS), the strongest short-range attraction (KCDR-CH3) and consequently the largest clusters and highest η were observed with IgG1, the subclass with the most positively charged CH3 domain. Furthermore, the trend in KCDR-CH3 with the subclass followed the electrostatic interaction energy between the CDR and CH3 regions calculated with the BioLuminate software using the 3D mAb structure and molecular interaction potentials. Whereas the equilibrium cluster size distributions and fractal dimensions were determined from fits of SAXS with the MD simulations, the degree of cluster rigidity under flow was estimated from the experimental η with a phenomenological model. For the systems with the largest clusters, especially IgG1, the inefficient packing of mAbs in the clusters played the largest role in increasing η, whereas for other systems, the relative contribution from stress produced by the clusters was more significant. The ability to relate η to short-range attraction from SAXS measurements at high concentrations and to theoretical characterization of electrostatic patches on the 3D surface is not only of fundamental interest but also of practical value for mAb discovery, processing, formulation, and subcutaneous delivery.

Plasmonic Response of Complex Nanoparticle Assemblies.

Optical properties of nanoparticle assemblies reflect distinctive characteristics of their building blocks and spatial organization, giving rise to emergent phenomena. Integrated experimental and computational studies have established design principles connecting the structure to properties for assembled clusters and superlattices. However, conventional electromagnetic simulations are too computationally expensive to treat more complex assemblies. Here we establish a fast, materials agnostic method to simulate the optical response of large nanoparticle assemblies incorporating both structural and compositional complexity. This many-bodied, mutual polarization method resolves limitations of established approaches, achieving rapid, accurate convergence for configurations including thousands of nanoparticles, with some overlapping. We demonstrate these capabilities by reproducing experimental trends and uncovering far- and near-field mechanisms governing the optical response of plasmonic semiconductor nanocrystal assemblies including structurally complex gel networks and compositionally complex mixed binary superlattices. This broadly applicable framework will facilitate the design of complex, hierarchically structured, and dynamic assemblies for desired optical characteristics.

Modular mixing in plasmonic metal oxide nanocrystal gels with thermoreversible links.

Gelation offers a powerful strategy to assemble plasmonic nanocrystal networks incorporating both the distinctive optical properties of constituent building blocks and customizable collective properties. Beyond what a single-component assembly can offer, the characteristics of nanocrystal networks can be tuned in a broader range when two or more components are intimately combined. Here, we demonstrate mixed nanocrystal gel networks using thermoresponsive metal-terpyridine links that enable rapid gel assembly and disassembly with thermal cycling. Plasmonic indium oxide nanocrystals with different sizes, doping concentrations, and shapes are reliably intermixed in linked gel assemblies, exhibiting collective infrared absorption that reflects the contributions of each component while also deviating systematically from a linear combination of the spectra for single-component gels. We extend a many-bodied, mutual polarization method to simulate the optical response of mixed nanocrystal gels, reproducing the experimental trends with no free parameters and revealing that spectral deviations originate from cross-coupling between nanocrystals with distinct plasmonic properties. Our thermoreversible linking strategy directs the assembly of mixed nanocrystal gels with continuously tunable far- and near-field optical properties that are distinct from those of the building blocks or mixed close-packed structures.

Multiscale modeling of solute diffusion in triblock copolymer membranes.

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model.

Attractive protein-protein interactions in concentrated monoclonal antibody (mAb) solutions may lead to the formation of clusters that increase viscosity. Here, we propose an analytical model that relates mAb solution viscosity to clustering by accounting for the contributions of suboptimal mAb packing within a cluster and cluster fractal dimension. The influence of short-range, anisotropic attractions and long-range Coulombic repulsion on cluster properties is investigated by analyzing the cluster-size distributions, cluster fractal dimensions, radial distribution functions, and static structure factors from a library of coarse-grained molecular dynamics simulations. The library spans a vast range of mAb charges and attractive interactions in solutions of varying ionic strength. We present a framework for combining the viscosity model and simulation library to successfully characterize the attraction, repulsion, and clustering of an experimental mAb in three different pH and cosolute conditions by fitting the measured viscosity or structure factor from small-angle X-ray scattering. At low ionic strength, the cluster-size distribution is impacted by strong charges, and both the viscosity and net charge or structure factor and net charge must be considered to deconvolute the effects of short-range attraction and long-range repulsion.

Effective Hard-Sphere Repulsions between Oleate-Capped Colloidal Metal Oxide Nanocrystals.

Nanocrystal interactions in solvent influence colloidal stability and dictate self-assembly outcomes. Small-angle X-ray scattering is used to study how dilute oleate-capped In2O3 nanocrystals with 7-19 nm core diameters interact when dispersed in a series of nonpolar solvents. Osmotic second virial coefficient analysis finds toluene-dispersed nanocrystals in this size range interact like effective hard spheres with diameters comprising the inorganic core and a ligand-solvent corona with a core-size independent thickness. Hard-sphere-like structure factors are similarly observed for nanocrystals with a 9.7 nm core diameter dispersed in all the solvents investigated. Nanocrystal hydrodynamic diameters from dynamic light scattering measurements correlate with thermodynamic diameters obtained from the osmotic second virial coefficient analysis for all samples. The ability to prepare nanoscale building blocks of different sizes, and dispersed in a variety of solvents, with effective hard-sphere repulsions provides a foundation for assembly, where secondary linking or depletant molecules can be deliberately added to customize interactions to form superstructures such as gel networks or superlattices.

Dynamics of equilibrium-linked colloidal networks.

Colloids that attractively bond to only a few neighbors (e.g., patchy particles) can form equilibrium gels with distinctive dynamic properties that are stable in time. Here, we use a coarse-grained model to explore the dynamics of linked networks of patchy colloids whose average valence is macroscopically, rather than microscopically, constrained. Simulation results for the model show dynamic hallmarks of equilibrium gel formation and establish that the colloid-colloid bond persistence time controls the characteristic slow relaxation of the self-intermediate scattering function. The model features re-entrant network formation without phase separation as a function of linker concentration, centered at the stoichiometric ratio of linker ends to nanoparticle surface bonding sites. Departures from stoichiometry result in linker-starved or linker-saturated networks with reduced connectivity and shorter characteristic relaxation times with lower activation energies. Underlying the re-entrant trends, dynamic properties vary monotonically with the number of effective network bonds per colloid, a quantity that can be predicted using Wertheim's thermodynamic perturbation theory. These behaviors suggest macroscopic in situ strategies for tuning the dynamic response of colloidal networks.

Treating random sequential addition via the replica method.

While many physical processes are non-equilibrium in nature, the theory and modeling of such phenomena lag behind theoretical treatments of equilibrium systems. The diversity of powerful theoretical tools available to describe equilibrium systems has inspired strategies that map non-equilibrium systems onto equivalent equilibrium analogs so that interrogation with standard statistical mechanical approaches is possible. In this work, we revisit the mapping from the non-equilibrium random sequential addition process onto an equilibrium multi-component mixture via the replica method, allowing for theoretical predictions of non-equilibrium structural quantities. We validate the above approach by comparing the theoretical predictions to numerical simulations of random sequential addition.

Machine Learning-Assisted Design of Material Properties.

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties.