Near‐field plasmonic coupling and local field enhancement in metal nanoarchitectures, such as arrangements of nanoparticle clusters, have application in many technologies from medical diagnostics, solar cells, to sensors. Although nanoparticle‐based cluster assemblies have exhibited signal enhancements in surface‐enhanced Raman scattering (SERS) sensors, it is challenging to achieve high reproducibility in SERS response using low‐cost fabrication methods. Here an innovative method is developed for fabricating self‐organized clusters of metal nanoparticles on diblock copolymer thin films as SERS‐active structures. Monodisperse, colloidal gold nanoparticles are attached via a crosslinking reaction on self‐organized chemically functionalized poly(methyl methacrylate) domains on polystyrene‐block‐poly(methyl methacrylate) templates. Thereby nanoparticle clusters with sub‐10‐nanometer interparticle spacing are achieved. Varying the molar concentration of functional chemical groups and crosslinking agent during the assembly process is found to affect the agglomeration of Au nanoparticles into clusters. Samples with a high surface coverage of nanoparticle cluster assemblies yield relative enhancement factors on the order of 109 while simultaneously producing uniform signal enhancements in point‐to‐point measurements across each sample. High enhancement factors are associated with the narrow gap between nanoparticles assembled in clusters in full‐wave electromagnetic simulations. Reusability for small‐molecule detection is also demonstrated. Thus it is shown that the combination of high signal enhancement and reproducibility is achievable using a completely non‐lithographic fabrication process, thereby producing SERS substrates having high performance at low cost.

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

Fuel-driven dissipative self-assemblies play essential roles in living systems, contributing both to their complex, dynamic structures and emergent functions. Several dissipative supramolecular materials have been created using chemicals or light as fuel. However, electrical energy, one of the most common energy sources, has remained unexplored for such purposes. Here, we demonstrate a new platform for creating active supramolecular materials using electrically fueled dissipative self-assembly. Through an electrochemical redox reaction network, a transient and highly active supramolecular assembly is achieved with rapid kinetics, directionality, and precise spatiotemporal control. As electronic signals are the default information carriers in modern technology, the described approach offers a potential opportunity to integrate active materials into electronic devices for bioelectronic applications.

Nanoparticles from colloidal solutionwith controlled composition, size, and shapeserve as excellent building blocks for plasmonic devices and metasurfaces. However, understanding hierarchical driving forces affecting the geometry of oligomers and interparticle gap spacings is still needed to fabricate high-density architectures over large areas. Here, electrohydrodynamic (EHD) flow is used as a long-range driving force to enable carbodiimide cross-linking between nanospheres and produces oligomers exhibiting sub-nanometer gap spacing over mm 2 areas. Anhydride linkers between nanospheres are observed via surface-enhanced Raman scattering (SERS) spectroscopy. The anhydride linkers are cleavable via nucleophilic substitution and enable placement of nucleophilic molecules in electromagnetic hotspots. Atomistic simulations elucidate that the transient attractive force provided by EHD flow is needed to provide a sufficient residence time for anhydride cross-linking to overcome slow reaction kinetics. This synergistic analysis shows assembly involves an interplay between long-range driving forces increasing nanoparticle− nanoparticle interactions and probability that ligands are in proximity to overcome activation energy barriers associated with short-range chemical reactions. Absorption spectroscopy and electromagnetic full-wave simulations show that variations in nanogap spacing have a greater influence on optical response than variations in close-packed oligomer geometry. The EHD flow−anhydride cross-linking assembly method enables close-packed oligomers with uniform gap spacings that produce uniform SERS enhancement factors. These results demonstrate the efficacy of colloidal driving forces to selectively enable chemical reactions leading to future assembly platforms for large-area nanodevices.

We investigate for the first time the capacity of a two-dimensional periodic array (a metasurface) of circular nanoclusters (CNCs) of plasmonic nanoparticles to support magnetic Fano resonances. These resonances are characterized by narrow angular and/or spectral features in the reflection/transmission/absorption coefficients associated with a circular disposition of nanoparticles’ dipole moments (forming a current loop) under oblique TE-polarized plane wave incidence illumination. We find that these narrow resonant features are either array-induced or single-CNC-induced, as shown by using a theoretical analysis based on the single dipole approximation and full-wave simulations, leading to enhanced magnetic and electric fields. In particular, array-induced resonances are narrower than single-CNC-induced ones and also provide even larger field enhancements, in particular generating a magnetic field enhancement of about 10-fold and an electric field enhancement of about 40-fold for a representative metasurface. We suggest that the novel results pertaining to metasurfaces made of CNCs shown here may be used for the development of sensors based on enhanced magnetic fields and for the enhancement of magnetic nonlinearities.