Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water.

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As3+ and 6.8 pM for Cr6+, 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy.

Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering.

Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring.

Electrically Fueled Active Supramolecular Materials.

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.

Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks.

Single molecule (SM) detection represents the ultimate limit of chemical detection. Over the years, many experimental techniques have emerged with this capacity. Yet, SM detection and imaging methods produce large spectral data sets that benefit from chemometric methods. In particular, surface enhanced Raman scattering spectroscopy (SERS), with extensive applications in biosensing, is demonstrated to be particularly promising because Raman active molecules can be identified without recognition elements and is capable of SM detection. Yet quantification at ultralow analyte concentrations requiring detection of SM events remains an ongoing challenge, with the few existing methods requiring carefully developed calibration curves that must be redeveloped for each analyte molecule. In this work, we demonstrate that a convolutional neural network (CNN) model when applied to bundles of SERS spectra yields a robust, facile method for concentration quantification down to 10 fM using SM detection events. We further demonstrate that transfer learning, the process of reusing the weights of a trained CNN model, greatly reduces the amount of data required to train CNN models on new analyte molecules. These results point the way for unambiguous analysis of large spectral data sets and the use of SERS in important ultra low concentration chemical detection applications such as metabolomic profiling, water quality evaluation, and fundamental research.

Large Continuous Mechanical Gradient Formation via Metal-Ligand Interactions.

Mechanical gradients are often employed in nature to prevent biological materials from damage by creating a smooth transition from strong to weak that dissipates large forces. Synthetic mimics of these natural structures are highly desired to improve distribution of stresses at interfaces and reduce contact deformation in manmade materials. Current synthetic gradient materials commonly suffer from non-continuous transitions, relatively small gradients in mechanical properties, and difficult syntheses. Inspired by the polychaete worm jaw, we report a novel approach to generate stiffness gradients in polymeric materials via incorporation of dynamic monodentate metal-ligand crosslinks. Through spatial control of metal ion content, we created a continuous mechanical gradient that spans over a 200-fold difference in stiffness, approaching the mechanical contrast observed in biological gradient materials.

Electric field enhancement with plasmonic colloidal nanoantennas excited by a silicon nitride waveguide.

We investigate the feasibility of CMOS-compatible optical structures to develop novel integrated spectroscopy systems. We show that local field enhancement is achievable utilizing dimers of plasmonic nanospheres that can be assembled from colloidal solutions on top of a CMOS-compatible optical waveguide. The resonant dimer nanoantennas are excited by modes guided in the integrated silicon nitride waveguide. Simulations show that 100-fold electric field enhancement builds up in the dimer gap as compared to the waveguide evanescent field amplitude at the same location. We investigate how the field enhancement depends on dimer location, orientation, distance and excited waveguide mode.

Nanoantennas report dissipative assembly in oscillatory electric fields.

Understanding driving forces for dissipative, i.e., out of equilibrium, assembly of nanoparticles from colloidal solution at liquid-solid interfaces provides the ability to design external cues for reconfigurable device response. Here electrohydrodynamic flow (EHD) at an electrode-liquid interface is investigated as a dissipative driving force for tuning optical response. EHD results from an oscillatory electric field in a liquid cell between two electrodes and drives assembly of gold nanoparticles (NP) into two-dimensional clusters on electrode surfaces. Clusters are chemically crosslinked during assembly to freeze assemblies for electron microscopy characterization in order to understand how to 'nucleate' cluster formation. Electron microscopy images show deposition with a potential having an amplitude of 5 V and frequency of 100 Hz produces surfaces with isolated NP, which can seed EHD flow. A second deposition step at 5 V and 500 Hz produces a high density of quadramers on surfaces. When exciting near the local surface plasmon resonance of the Au NP clusters formed during assembly, Au NPs serve as in situ nanoantenna reporters of assembly and disassembly. Surface enhanced Raman scattering (SERS) measurements of Au NP capped with 4-mercaptobenzoic acid show order of magnitude signal enhancements occur during cluster formation in the presence of an oscillatory field, which occurs on a time scale of seconds. Confocal fluorescence spectroscopy is used to monitor the dissipative assembly of Au NP over multiple cycles. Results provide insight on how electrical stimuli and seeding local perturbations affects formation of NP clusters and resultant optical response provides insight on how to tune response of optically active surfaces.

Comparison of electric field enhancements: linear and triangular oligomers versus hexagonal arrays of plasmonic nanospheres.

We investigate local electromagnetic field enhancements in oligomers of plasmonic nanospheres. We first evaluate via full-wave simulations the field between spheres in several oligomer systems: linear dimers, linear trimers, trimers 60°, trimers 90° and linear quadrumers. To gain a better understanding of the field enhancement values, we compare the results with local fields in a hexagonal close-packed (HCP) configuration with same structural dimensions. We then inter-relate the field enhancement values found via full-wave simulations to SERS enhancements of actual fabricated self-assembled oligomers. We find that linear oligomers provide the largest field enhancement values. Finally, we provide closed-form formulas for the prediction of the resonance frequency responsible for field enhancement in linear oligomers, namely dimers, trimers and quadrumers, modeling each nanosphere as a single electric dipole. These formulas provide with resonance values less than 7% shifted when compared to full-wave results even when the gap between spheres is only about one fifth of the radius, showing the powerfulness of dipolar approximations. The results shown in this paper demonstrate that ad hoc clusters of nanospheres can be designed and fabricated to obtain larger field enhancements than with the HCP structure and this may pave the way for the development of improved sensors for molecular spectroscopy.

Fano resonances in metasurfaces made of linear trimers of plasmonic nanoparticles.

We investigate Fano resonances in planar two-dimensional periodic arrays of linear trimers of plasmonic nanoparticles that appear under plane wave incidence. The observed Fano resonances are associated to resonances belonging to the trimer (metamolecule) itself, where some are found to be strongly affected by the array periodicity. We observe that array-dependent resonances appearing for oblique incidence are resistant to losses, whereas narrow dipolar-like Fano resonances associated mainly to the metamolecule, which appear also under normal incidence, disappear when losses are too high. In particular, we prove the latter by theoretical (dipolar approximation) and full-wave simulations, in good agreement. We propose that the use of very low-loss plasmonic materials or the use of gain materials to mitigate plasmonic losses may lead to (high-quality factor) dipolar-like Fano resonances under normal incidence, exhibiting a certain degree of fabrication defect tolerance, which might be employed to improve sensors, lasing, switching, and nonlinear devices, for example.

Directing cluster formation of Au nanoparticles from colloidal solution.

Discrete clusters of closely spaced Au nanoparticles can be utilized in devices from photovoltaics to molecular sensors because of the formation of strong local electromagnetic field enhancements when illuminated near their plasmon resonance. In this study, scalable, chemical self-organization methods are shown to produce Au nanoparticle clusters with uniform nanometer interparticle spacing. The performance of two different methods, namely electrophoresis and diffusion, for driving the attachment of Au nanoparticles using a chemical cross-linker on chemically patterned domains of polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) thin films are evaluated. Significantly, electrophoresis is found to produce similar surface coverage as diffusion in 1/6th of the processing time with an ~2-fold increase in the number of Au nanoparticles forming clusters. Furthermore, average interparticle spacing within Au nanoparticle clusters was found to decrease from 2-7 nm for diffusion deposition to approximately 1-2 nm for electrophoresis deposition, and the latter method exhibited better uniformity with most clusters appearing to have about 1 nm spacing between nanoparticles. The advantage of such fabrication capability is supported by calculations of local electric field enhancements using electromagnetic full-wave simulations from which we can estimate surface-enhanced Raman scattering (SERS) enhancements. In particular, full-wave results show that the maximum SERS enhancement, as estimated here as the fourth power of the local electric field, increases by a factor of 100 when the gap goes from 2 to 1 nm, reaching values as large as 10(10), strengthening the usage of electrophoresis versus diffusion for the development of molecular sensors.

Interpreting Kelvin probe force microscopy under an applied electric field: local electronic behavior of vapor-liquid-solid Si nanowires.

Kelvin probe force microscopy (KPFM) is used to characterize the electrical characteristics of vapor-liquid-solid (VLS) Si nanowires (NWs) that are grown in-place between two predefined electrodes. KPFM measurements are performed under an applied bias. Besides contact potential differences due to differing materials, the two other primary contributions to measured variations on Si NWs between electrodes are: trapped charges at interfaces, and the parallel and serial capacitance, which are accounted for with voltage normalization and oxide normalization. These two normalization processes alongside finite-element-method simulations are necessary to characterize the bias-dependent response of Si NWs. After applying both normalization methods on open-circuit NWs, which results in a baseline of zero, we conclude that we have accounted for all the major contributions to CPDs and we can isolate effects due to applied bias such as impurity states and charged carrier flow, as well as find open connections when NWs are connected in parallel. These characterization and normalization methods can also be used to determine that the specific contact resistance of electrodes to the NWs is on the order of µΩ cm². Thus, the VLS growth method between predefined electrodes overcomes the challenge of making low-resistance contacts to nanoscale systems. Thereby, the experiments and analysis presented outline a systematic method for characterizing nanowires in parallel arrays under device operation conditions.

Non-lithographic SERS substrates: tailoring surface chemistry for Au nanoparticle cluster assembly.

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.

Elucidating driving forces for liposome rupture: external perturbations and chemical affinity.

Atomic force microscopy (AFM) studies under aqueous buffer probed the role of chemical affinity between liposomes, consisting of large unilamellar vesicles, and substrate surfaces in driving vesicle rupture and tethered lipid bilayer membrane (tLBM) formation on Au surfaces. 1,2-Distearoyl-sn-glycero-3-phosphoethanolamine-N-poly(ethylene glycol)-2000-N-[3-(2-pyridyldithio) propionate] (DSPE-PEG-PDP) was added to 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) vesicles to promote interactions via Au-thiolate bond formation. Forces induced by an AFM tip leading to vesicle rupture on Au were quantified as a function of DSPE-PEG-PDP composition with and without osmotic pressure. The critical forces needed to initiate rupture of vesicles with 2.5, 5, and 10 mol % DSPE-PEG-PDP are approximately 1.1, 0.8, and 0.5 nN, respectively. The critical force needed for tLBM formation decreases from 1.1 nN (without osmotic pressure) to 0.6 nN (with an osmotic pressure due to 5 mM of CaCl(2)) for vesicles having 2.5 mol % DSPE-PEG-PDP. Forces as high as 5 nN did not lead to LBM formation from pure POPC vesicles on Au. DSPE-PEG-PDP appears to be important to anchor and deform vesicles on Au surfaces. This study demonstrates how functional lipids can be used to tune vesicle-surface interactions and elucidates the role of vesicle-substrate interactions in vesicle rupture.

Mapping Molecular Adsorption Configurations with <5 nm Spatial Resolution through Ambient Tip-Enhanced Raman Imaging.

We interrogate para-mercaptobenzoic acid (MBA) molecules chemisorbed onto plasmonic silver nanocubes through tip-enhanced Raman (TER) spectral nanoimaging. Through a detailed examination of the spectra, aided by correlation analysis and density functional theory calculations, we find that MBA chemisorbs onto the plasmonic particles with at least two distinct configurations: S- and CO2-bound. High spatial resolution TER mapping allows us to distinguish between the distinct adsorption geometries with a pixel-limited (<5 nm) spatial resolution under ambient laboratory conditions.

A facile approach for assembling lipid bilayer membranes on template-stripped gold.

Lipid vesicles are designed with functional chemical groups to promote vesicle fusion on template-stripped gold (TS Au) surfaces that does not spontaneously occur on unfunctionalized Au surfaces. Three types of vesicles were exposed to TS Au surfaces: (1) vesicles composed of only 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipids; (2) vesicles composed of lipid mixtures of 2.5 mol % of 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-poly(ethylene glycol)-2000-N-[3-(2-pyridyldithio)propionate] (DSPE-PEG-PDP) and 97.5 mol % of POPC; and (3) vesicles composed of 2.5 mol % of 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(poly(ethylene glycol))-2000] (DSPE-PEG) and 97.5 mol % POPC. Atomic force microscopy (AFM) topography and force spectroscopy measurements acquired in a fluid environment confirmed tethered lipid bilayer membrane (tLBM) formation only for vesicles composed of 2.5 mol % DSPE-PEG-PDP/97.5 mol % POPC, thus indicating that the sulfur-containing PDP group is necessary to achieve tLBM formation on TS Au via Au-thiolate bonds. Analysis of force-distance curves for 2.5 mol % DSPE-PEG-PDP/97.5 mol % POPC tLBMs on TS Au yielded a breakthrough distance of 4.8 ± 0.4 nm, which is about 1.7 nm thicker than that of POPC lipid bilayer membrane formed on mica. Thus, the PEG group serves as a spacer layer between the tLBM and the TS Au surface. Fluorescence microscopy results indicate that these tLBMs also have greater mechanical stability than solid-supported lipid bilayer membranes made from the same vesicles on mica. The described process for assembling stable tLBMs on Au surfaces is compatible with microdispensing used in array fabrication.

Fabrication of patterned graphitized carbon wires using low voltage near-field electrospinning, pyrolysis, electrodeposition, and chemical vapor deposition.

We herein report a high-resolution nanopatterning method using low voltage electromechanical spinning with a rotating collector to obtain aligned graphitized micro and nanowires for carbon nanomanufacturing. A small wire diameter and a small inter-wire spacing were obtained by controlling the electric field, the spinneret-to-collector distance, the pyrolysis parameters, the linear speed of the spinneret, the rotational speed of the collector. Using a simple scaling analysis, we show how the straightness and the diameter of the wires can be controlled by the electric field and the distance of the spinneret to the collector. A small inter-wire spacing, as predicted by a simple model, was achieved by simultaneously controlling the linear speed of the spinneret and the rotational speed of the collector. Rapid drying of the polymer nanowires enabled the facile fabrication of suspended wires over various structures. Patterned polyacrylonitrile wires were carbonized using standard stabilization and pyrolysis to obtain carbon nanowires. Suspended carbon nanowires with a diameter of <50 nm were obtained. We also established a method for making patterned, highly graphitized structures by using the aforementioned carbon wire structures as a template for chemical vapor deposition of graphite. This patterning technique offers high throughput for nano writing, which outperforms other existing nanopatterning techniques, making it a potential candidate for large-scale carbon nanomanufacturing.

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing.

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.

Morphological work function dependence of rare-earth disilicide metal nanostructures.

The work functions of various DySi(2-x) nanostructures epitaxially grown on a Si(001) surface were correlated with the structure using high-resolution Kelvin probe force microscopy and scanning tunneling microscopy in ultrahigh vacuum. Dy adatoms induce a surface dipole on Si(001) that increases the surface potential from 0.26 to 0.42 eV with respect to 2 x 1 reconstructed Si(001). DySi(2-x) nanowires showed a 0.2-0.23 eV lower work function than DySi(2-x) nanoislands, which can be attributed to confinement of electrons along the surface normal that induces a surface dipole when the film thickness approaches the Fermi wavelength. The ability to tune the work function of metal nanostructures should be useful for understanding how electronic structure affects catalytic activity.

Surface-Enhanced Raman Scattering-Based Odor Compass: Locating Multiple Chemical Sources and Pathogens.

Olfaction is important for identifying and avoiding toxic substances in living systems. Many efforts have been made to realize artificial olfaction systems that reflect the capacity of biological systems. A sophisticated example of an artificial olfaction device is the odor compass which uses chemical sensor data to identify odor source direction. Successful odor compass designs often rely on plume-based detection and mobile robots, where active, mechanical motion of the sensor platform is employed. Passive, diffusion-based odor compasses remain elusive as detection of low analyte concentrations and quantification of small concentration gradients from within the sensor platform are necessary. Further, simultaneously identifying multiple odor sources using an odor compass remains an ongoing challenge, especially for similar analytes. Here, we show that surface-enhanced Raman scattering (SERS) sensors overcome these challenges, and we present the first SERS odor compass. Using a grid array of SERS sensors, machine learning analysis enables reliable identification of multiple odor sources arising from diffusion of analytes from one or two localized sources. Specifically, convolutional neural network and support vector machine classifier models achieve over 90% accuracy for a multiple odor source problem. This system is then used to identify the location of an Escherichia coli biofilm via its complex signature of volatile organic compounds. Thus, the fabricated SERS chemical sensors have the needed limit of detection and quantification for diffusion-based odor compasses. Solving the multiple odor source problem with a passive platform opens a path toward an Internet of things approach to monitor toxic gases and indoor pathogens.

Scalable synthesis of gyroid-inspired freestanding three-dimensional graphene architectures.

Three-dimensional porous architectures of graphene are desirable for energy storage, catalysis, and sensing applications. Yet it has proven challenging to devise scalable methods capable of producing co-continuous architectures and well-defined, uniform pore and ligament sizes at length scales relevant to applications. This is further complicated by processing temperatures necessary for high quality graphene. Here, bicontinuous interfacially jammed emulsion gels (bijels) are formed and processed into sacrificial porous Ni scaffolds for chemical vapor deposition to produce freestanding three-dimensional turbostratic graphene (bi-3DG) monoliths with high specific surface area. Scanning electron microscopy (SEM) images show that the bi-3DG monoliths inherit the unique microstructural characteristics of their bijel parents. Processing of the Ni templates strongly influences the resultant bi-3DG structures, enabling the formation of stacked graphene flakes or fewer-layer continuous films. Despite the multilayer nature, Raman spectra exhibit no discernable defect peak and large relative intensity for the Raman 2D mode, which is a characteristic of turbostratic graphene. Moiré patterns, observed in scanning tunneling microscopy images, further confirm the presence of turbostratic graphene. Nanoindentation of macroscopic pillars reveals a Young's modulus of 30 MPa, one of the highest recorded for sp2 carbon in a porous structure. Overall, this work highlights the utility of a scalable self-assembly method towards porous high quality graphene constructs with tunable, uniform, and co-continuous microstructure.