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The optical formation of coherent superposition states, a wavepacket, can allow the study of zeroth-order states, the evolution of which exhibit structural and electronic changes as a function of time: this leads to the notion of a molecular movie. Intramolecular vibrational energy redistribution, due to anharmonic coupling between modes, is the molecular movie considered here. There is no guarantee, however, that the formed superposition will behave semi-classically (e.g. Gaussian wavepacket dynamics) or even as an intuitively useful zeroth-order state. Here we present time-resolved photoelectron spectroscopy (TRPES) studies of an electronically excited triatomic molecule wherein the vibrational dynamics must be treated quantum mechanically and the simple picture of population flow between coupled normal modes fails. Specifically, we report on vibronic wavepacket dynamics in the zeroth-order 3pσ2Σu Rydberg state of NO2. This wavepacket exemplifies two general features of excited state dynamics in polyatomic molecules: anharmonic multimodal vibrational coupling (forming polyads); nonadiabatic coupling between nuclear and electronic coordinates, leading to predissociation. The latter suggests that the polyad vibrational states in the zeroth-order 3p Rydberg manifold are quasi-bound and best understood to be scattering resonances. We observed a rapid dephasing of an initially prepared 'bright' valence state into the relatively long-lived 3p Rydberg state whose multimodal vibrational dynamics and decay we monitor as a function of time. Our quantum simulations, based on an effective spectroscopic Hamiltonian, describe the essential features of the multimodal Fermi resonance-driven vibrational dynamics in the 3p state. We also present evidence of polyad-specificity in the state-dependent predissociation rates, leading to free atomic and molecular fragments. We emphasize that a quantum molecular movie is required to visualize wavepacket dynamics in the 3pσ2Σu Rydberg state of NO2.

Process-monitoring laboratories in the pulp and paper industry generally use a combination of wet chemical analyses and physical measurements to certify the fitness of a production pulp for a specific end-use. These laboratory tests require time and the effort of trained personnel, limiting their utility for real-time process control. Here we show that Raman probes of unrefined cellulosic pulps, well-suited to the online measurement of in-process materials, can predict the quality attributes of manufactured papers. The accuracy of prediction improves when the covariance is modelled in a reduced measurement space selected by a data-driven, feature-selection technique referred to as a Template Oriented Genetic Algorithm (TOGA). TOGA, combined with discrete wavelet transform (DWT), isolates functional-group features that correlate best with mechanical properties paper derived from refined pulp. Paper makers refine market pulps to build sheet strength using a beating process that decreases freeness as it increases fibre-fibre bonding. Methods demonstrated here predict manufactured sheet properties obtainable after any specified degree of refining from the Raman spectrum of an unrefined pulp. This analysis capacity will enable both vendors of market pulp and makers of sheet paper to specify in advance the amount of beating required to produce a desired product, thereby saving cost and conserving resources.

The pH at the electrocatalyst surface plays a key role in defining the activity and selectivity of the CO2 reduction reaction (CO2RR). We report here operando Raman measurements of the catalyst sur...

The highest performance flow cells capable of electrolytically converting CO2 into higher value chemicals and fuels pass a concentrated hydroxide electrolyte across the cathode. A major problem for CO2 electrolysis is that this strongly alkaline medium converts the majority of CO2 into unreactive HCO3– and CO32– rather than CO2 reduction reaction (CO2RR) products. The electrolysis of CO (instead of CO2) does not suffer from this same problem because CO does not react with hydroxide. Moreover, CO can be more readily converted into products containing two or more carbon atoms (i.e., C2+ products). While several solid-state electrocatalysts have proven competent at converting CO into C2+ products, we demonstrate here that molecular electrocatalysts are also effective at mediating this transformation in a flow cell. Using a molecular copper phthalocyanine (CuPc) electrocatalyst, CO was electrolyzed into C2+ products at high rates of product formation (i.e., current densities J ≥200 mA/cm2), and at high Faradaic efficiencies for C2+ production (FEC2+; 72% at 200 mA/cm2). These findings present a new class of electrocatalysts for making carbon-neutral chemicals and fuels.

We investigate fully self-consistent multiscale quantum-classical algorithms on current generation superconducting quantum computers, in a unified approach to tackle the correlated electronic structure of large systems in both quantum chemistry and condensed matter physics. In both of these contexts, a strongly correlated quantum region of the extended system is isolated and self-consistently coupled to its environment via the sampling of reduced density matrices. We analyze the viability of current generation quantum devices to provide the required fidelity of these objects for a robust and efficient optimization of this subspace. We show that with a simple error mitigation strategy and optimization of compact tensor product bases to minimize the number of terms to sample, these self-consistent algorithms are indeed highly robust, even in the presence of significant noises on quantum hardware. Furthermore, we demonstrate the use of these density matrices for the sampling of non-energetic properties, including dipole moments and Fermi liquid parameters in condensed phase systems, achieving a reliable accuracy with sparse sampling. It appears that uncertainties derived from the iterative optimization of these subspaces is smaller than variances in the energy for a single subspace optimization with current quantum hardware. This boosts the prospect for routine self-consistency to improve the choice of correlated subspaces in hybrid quantum-classical approaches to electronic structure for large systems in this multiscale fashion.

Specialized applications of nanoparticles often call for particular, well-characterized particle size distributions in solution. But, this property can prove difficult to measure. High-throughput methods, such as dynamic light scattering, detect nanoparticles in solution with an efficiency that scales with diameter to the sixth power. This diminishes the accuracy of any determination that must span a range of particle sizes. The accurate classification of broadly distributed systems thus requires very large numbers of measurements. Mass-filtered particle-sensing techniques offer a better dynamic range, but are labor-intensive and so have low throughput. Progress in many areas of nanotechnology requires a faster, lower-cost, and more accurate measure of particle size distributions, particularly for diameters smaller than 20 nm. Here, we present a tailored interferometric microscope system, combined with a high-speed image-processing strategy, optimized for real-time particle tracking that determines accurate size distributions in nominal 5, 10, and 15 nm colloidal gold nanoparticle systems by automatically sensing and classifying thousands of single particles sampled from solution at rates as high as 4,000 particles per minute. We demonstrate this method by sensing the irreversible binding of gold nanoparticles to poly-D-lysine functionalized coverslips. Variations in the single-particle signal as a function of time and mass, calibrated by TEM, show clear evidence for the presence of diffusion-limited transport that most affects larger particles in solution.

Resonant mm-wave fields drive n0f(2)→(n0±1)g(2) transitions in a state-selected n0f(2) Rydberg gas of NO. This transformation produces a clear signature in the selected field ionisation spectrum an...

The optical formation of coherent superposition states, a wavepacket, can allow the study of zeroth-order states, the evolution of which exhibit structural and electronic changes as a function of time: this leads to the notion of a molecular movie. Intramolecular vibrational energy redistribution, due to anharmonic coupling between modes, is the molecular movie considered here. There is no guarantee, however, that the formed superposition will behave semi-classically (e.g. Gaussian wavepacket dynamics) or even as an intuitively useful zeroth-order state. Here we present time-resolved photoelectron spectroscopy (TRPES) studies of an electronically excited triatomic molecule wherein the vibrational dynamics must be treated quantum mechanically and the simple picture of population flow between coupled normal modes fails. Specifically, we report on vibronic wavepacket dynamics in the zeroth-order 3pσ2Σu Rydberg state of NO2. This wavepacket exemplifies two general features of excited state dynamics in polyatomic molecules: anharmonic multimodal vibrational coupling (forming polyads); nonadiabatic coupling between nuclear and electronic coordinates, leading to predissociation. The latter suggests that the polyad vibrational states in the zeroth-order 3p Rydberg manifold are quasi-bound and best understood to be scattering resonances. We observed a rapid dephasing of an initially prepared 'bright' valence state into the relatively long-lived 3p Rydberg state whose multimodal vibrational dynamics and decay we monitor as a function of time. Our quantum simulations, based on an effective spectroscopic Hamiltonian, describe the essential features of the multimodal Fermi resonance-driven vibrational dynamics in the 3p state. We also present evidence of polyad-specificity in the state-dependent predissociation rates, leading to free atomic and molecular fragments. We emphasize that a quantum molecular movie is required to visualize wavepacket dynamics in the 3pσ2Σu Rydberg state of NO2.

We report three-photon continuous-wave laser excitation of nitric oxide to high lying electronic Rydberg states and its conversion into a detectable current in the nA regime using our optogalvanic trace-gas sensor prototype.

Solving for molecular excited states remains one of the key challenges of modern quantum chemistry. Traditional methods are constrained by existing computational capabilities, limiting the complexity of the molecules that can be studied or the accuracy of the results that can be obtained. Several quantum computing methods have been suggested to address this limitation. However, these typically have hardware requirements which may not be achieved in the near term. We propose a variational quantum machine learning based method to determine molecular excited states aiming at being as resilient as possible to the defects of early noisy intermediate scale quantum computers and demonstrate an implementation for ${\mathrm{H}}_{2}$ on IBM Quantum Computers. Our method uses a combination of two parametrized quantum circuits, working in tandem, combined with a variational quantum eigensolver to iteratively find the eigenstates of a molecular Hamiltonian.

Penning ionization releases electrons in a state-selected Rydberg gas of nitric oxide entrained in a supersonic molecular beam. Subsequent processes of electron impact avalanche, bifurcation, and quench form a strongly coupled, spatially correlated, ultracold plasma of ${\mathrm{NO}}^{+}$ ions and electrons that exhibits characteristics of self-organized criticality. This plasma contains a residue of nitric oxide Rydberg molecules. A conventional fluid dynamics of ion-electron-Rydberg quasi-equilibrium predicts rapid decay to neutral atoms. Instead, the NO plasma endures for a millisecond or more, suggesting that quenched disorder creates a state of suppressed electron mobility. Supporting this proposition, a 60-MHz radio frequency field with a peak-to-peak amplitude less than 1 V ${\mathrm{cm}}^{\ensuremath{-}1}$ acts dramatically to mobilize electrons, causing the plasma to dissipate by dissociative recombination and Rydberg predissociation. An evident density dependence shows that this effect relies on collisions, giving weight to the idea of arrested relaxation as a cooperative property of the ensemble.

Interferrometric scattering microscopy is used to mass quantify individual Cathepsin K enzymes in solution. Treatments for osteoporosis are assessed by monitoring the oligomerization of Cathepsin K.

We present the development of an automated process control technology that employs Raman spectroscopy and chemometrics in the pulp and paper industry, improving efficiency, reducing costs, and providing the opportunity to make product quality guarantees.

An ellipsoidal volume of Rydberg molecules, entrained in a supersonic molecular beam, evolves on a nanosecond timescale to form a strongly coupled ultracold plasma. We present coupled rate-equation simulations that model the underlying kinetic processes and molecular dissociation channels in both a uniformly distributed plasma and under the conditions dictated by our experimental geometry. Simulations predict a fast electron-driven collisional avalanche to plasma followed by slow electron-ion recombination. Within 20 μs, release of Rydberg binding energy raises the electron temperature of a static plasma to T e = 100 K. Providing for a quasi-self-similar expansion, the hot electron gas drives ion radial motion, reducing T e . These simulations provide a classical baseline model from which to consider quantum effects in the evolution of charge gradients and ambipolar forces in an experimental system undergoing responsive avalanche dynamics.

Thiabendazole, a systemic fungicide used to treat vegetables and fruits during postharvest process, persists as detrimental residue to consumers. We combine a molecularly imprinted polymers (MIPs) with surface enhanced Raman spectroscopy (SERS) to form a novel MISPE-SERS chemosensor and determined thiabendazole in orange juice. Kinetic and static adsorption tests validated the efficient and selective adsorption of thiabendazole using synthesized MIPs via precipitation polymerization. Synthesized MIPs were packed into solid phase extraction (SPE) cartridge to serve as tailor-made sorbents for the separation of thiabendazole in orange juice. Silver colloids synthesized by reduction of AgNO3 by trisodium citrate were used as SERS-active substrate to quantify the eluted thiabendazole from MISPE. The overall process including sample preparation and detection took 23min and the limit of detection of this chemosensor was 4ppm for thiabendazole in orange juice. This chemosensor can be used for rapid and sensitive detection of thiabendazole in agri-foods.

Polytetrafluoroethylene (PTFE) flat profiles were extruded using slit dies, which promoted orientation of fibrils in two directions (2-D). Uniaxial tensile experiments were performed on the collected extrudates using the Sentmanat Extensional Rheometer (SER) at different temperatures and Hencky strain rates to determine the mechanical properties of the material. A nonlinear viscoelastic model developed by Matsuoka was found to describe well the transient tensile results using Poisson's ratio as a parameter. Polarized Raman Spectroscopy was also used to gain additional information on the degree of fibril orientation at different locations both along and across the width and length of the extrudates. Results of the Raman spectra were found to be in agreement with the fibril structure/morphology obtained from SEM micrographs. The compressibility of the extrudates upon stretching was studied by measuring the relative density. The results are modeled using a simple equation including the elastic strain recovery coefficient (κ) and Poisson's ratio.

We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits that use structure optimization perform significantly better than circuits that use parameter updates alone, making this method particularly suitable for noisy intermediate-scale quantum computers. We demonstrate the method for optimizing a variational quantum eigensolver for finding the ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer., Comment: 13 pages, 6 figures. Added section "Optimization of circuits with limited expressibility". The previous version was titled "Quantum circuit structure learning"

Long-range interparticle interactions are revealed in extremely dilute thermal atomic ensembles using highly sensitive nonlinear femtosecond spectroscopy., Long-range interparticle interactions are revealed in extremely dilute thermal atomic ensembles using highly sensitive nonlinear femtosecond spectroscopy. Delocalized excitons are detected in the atomic systems at particle densities where the mean interatomic distance (>10 μm) is much greater than the laser wavelength and multi-particle coherences should destructively interfere over the ensemble average. With a combined experimental and theoretical analysis, we identify an effective interaction mechanism, presumably of dipolar nature, as the origin of the excitonic signals. Our study implies that even in highly-dilute thermal atom ensembles, significant transition dipole–dipole interaction networks may form that require advanced modeling beyond the nearest neighbor approximation to quantitatively capture the details of their many-body properties.

Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers. Two parametrized circuits are optimized in tandem: One tries to approximate the target state, the other tries to distinguish between target and approximated state. Supported by numerical simulations, we show that resilient backpropagation algorithms perform remarkably well in optimizing the two circuits. We use the bipartite entanglement entropy to design an efficient heuristic for the stopping criterion. Our approach may find application in quantum state tomography., Comment: 14 pages, 6 figures. Minor revisions. As published in New J. Phys

We demonstrate the applicability of 2-photon Rydberg excitations of nitric oxide (NO) at room temperature in a gas mixture with helium (He) as an optogalvanic gas sensor. The charges created initially from succeeding collisions of excited NO Rydberg molecules with free electrons are measured as a current on metallic electrodes inside a glass cell and amplified using a custom-designed highbandwidth transimpedance amplifier attached to the cell. We fnd that this gas sensing method is capable of detecting NO concentrations lower than 10 ppm even at atmospheric pressures, currently only limited by the way we prepare the gas dilutions.

We introduce a rapid, cost effective and reliable approach to determine histamine level in canned tuna. Molecularly imprinted polymers (MIPs) were synthesized as artificial antibodies towards histamine by utilizing the interaction between histamine and a functional monomer (methacrylic acid) to impress specific binding sites on polymer particles after polymerization. Polyvinyl chloride (PVC) was used to immobilize the MIPs, yielding a MIPs-PVC film that functioned as a recognition element to specifically separate histamine from tuna extract. A gold colloid solution served both as an eluting solvent to extract histamine from MIPs-PVC film and furnish a substrate for surface enhanced Raman spectroscopy (SERS) detection of histamine signals. Principal component analysis together with a partial least square regression (PLSR) model (R(2) = 0.947, RMSECV = 3.526) verified the reliability of MIPs-PVC-SERS approach for the detection and spectral analysis of histamine. Linear regression models were also constructed to relate the intensity of different histamine SERS bands with the corresponding spiking levels. One such model (using a band at 1576 cm(-1)) performed slightly better in predicting histamine content in tuna than the PLSR model. We conclude that our new MIPs-PVC-SERS approach can rapidly and reliably determine histamine at levels from 3 to 90 ppm in canned tuna meat.

Sudan I is a carcinogenic and mutagenic azo-compound that has been utilized as a common adulterant in spice and spice blends to impart a desirable red color to foods. A novel biosensor combining molecularly imprinted polymers (MIPs), thin layer chromatography (TLC) and surface enhanced Raman spectroscopy (SERS) could determine Sudan I levels in paprika powder to 1 ppm (or 2 ng/spot). Sudan I spiked paprika extracts (spiking levels: 0, 1, 5, 10, 40, 70 and 100 ppm) were prepared. Sudan I imprinted polymers were synthesized by employing the interaction between Sudan I (template) and methacrylic acid (functional monomer), followed by washing to remove Sudan I leaving the Sudan I-binding sites exposed. MIPs were used as a stationary phase for TLC and could selectively retain Sudan I at the original spot with little interference. A gold colloid SERS substrate could enhance Raman intensity for Sudan I in this MIP-TLC system. Principal component analysis plot and partial least squares regression (R(2)=0.978) models were constructed and a linear regression model (R(2)=0.983) correlated spiking levels (5, 10, 40, 70 and 100 ppm) with the peak intensities (721 cm(-1)) of Sudan I SERS spectra. Both separation (30-40s) and detection (1s or 0.1s) were extremely fast by using both commercial bench-top and custom made portable Raman spectrometers. This biosensor can be applied as a rapid, low-cost and reliable tool for screening Sudan I adulteration in foods.

We have developed a silver nanofilm-coated porous anodic aluminum oxide (AAO) as a surface-enhanced Raman scattering (SERS)-active substrate for the detection of trace level of chloramphenicol, a representative antibiotic in food systems. The ordered aluminum template generated during the synthesis of AAO serves as a patterned matrix on which a coated silver film replicates the patterned AAO matrix to form a 2-dimensional ordered nanostructure. We used atomic force microscopy and scanning electron microscopy images to determine the morphology of this nanosubstrate, and characterized its localized surface plasmon resonance by ultraviolet-visible reflection. We gauged the SERS effect of this nanosubstrate by confocal micro-Raman spectroscopy (782-nm laser), finding a satisfactory and consistent performance with enhancement factors of approximately 2 × 10(4) and a limit of detection for chloramphenicol of 7.5 ppb. We applied principal component analysis to determine the limit of quantification for chloramphenicol of 10 ppb. Using electromagnetic field theory, we developed a detailed mathematical model to explain the mechanism of Raman signal enhancement of this nanosubstrate. With simple sample pretreatment and separation steps, this silver nanofilm-coated AAO substrate could detect 50 ppb chloramphenicol in milk, indicating good potential as a reliable SERS-active substrate for rapid detection of chemical contaminants in agricultural and food products.

We argue that the quenched ultracold plasma presents an experimental platform for studying quantum many-body physics of disordered systems in the long-time and finite energy-density limits. We consider an experiment that quenches a plasma of nitric oxide to an ultracold system of Rydberg molecules, ions and electrons that exhibits a long-lived state of arrested relaxation. The qualitative features of this state fail to conform with classical models. Here, we develop a microscopic quantum description for the arrested phase based on an effective many-body spin Hamiltonian that includes both dipole-dipole and van der Waals interactions. This effective model appears to offer a way to envision the essential quantum disordered non-equilibrium physics of this system., 6 pages main text + 9 pages supplemental materials, 2 figures main text + 7 figures and 2 tables supplemental materials

Spontaneous avalanche to plasma splits the core of an ellipsoidal Rydberg gas of nitric oxide. Ambipolar expansion first quenches the electron temperature of this core plasma. Then, long-range, resonant charge transfer from ballistic ions to frozen Rydberg molecules in the wings of the ellipsoid quenches the centre-of-mass ion/Rydberg molecule velocity distribution. This sequence of steps gives rise to a remarkable mechanics of self-assembly, in which the kinetic energy of initially formed hot electrons and ions drives an observed separation of plasma volumes. These dynamics adiabatically sequester energy in a reservoir of mass transport, starting a process that anneals separating volumes to form an apparent glass of strongly coupled ions and electrons. Short-time electron spectroscopy provides experimental evidence for complete ionization. The long lifetime of this system, particularly its stability with respect to recombination and neutral dissociation, suggests that this transformation affords a robust state of arrested relaxation, far from thermal equilibrium.

Item does not contain fulltext Judgments about personality based on facial appearance are strong effectors in social decision making, and are known to have impact on areas from presidential elections to jury decisions. Recent work has shown that it is possible to predict perception of memorability, trustworthiness, intelligence and other attributes in human face images. The most successful of these approaches require face images expertly annotated with key facial landmarks. We demonstrate a Convolutional Neural Network (CNN) model that is able to perform the same task without the need for landmark features, thereby greatly increasing efficiency. The model has high accuracy, surpassing human-level performance in some cases. Furthermore, we use a deconvolutional approach to visualize important features for perception of 22 attributes and demonstrate a new method for separately visualizing positive and negative features. 2016 23rd International Conference on Pattern Recognition (ICPR) (Cancún, Mexico, December 4-8, 2016)

We integrated molecularly imprinted polymers with surface-enhanced Raman spectroscopy (MIPs-SERS) to develop an innovative nano-biosensor for the determination of chloramphenicol (CAP) in milk and honey products. Template molecule (CAP), functional monomer (acrylamide), cross-linking agent (ethylene glycol dimethacrylate), initiator (2,2'-azobis(isobutyronitrile)), and porogen (methanol) were employed to form MIPs via "dummy" precipitation polymerization. Static and kinetic studies validated the specific selectivity of MIPs toward CAP over nonimprinted polymers (imprinting factor >4). Canadian penny-based silver nano-structure was synthesized as SERS-active substrate for determination of CAP in food matrices. Collected spectra were processed by principal component analysis to differentiate various concentrations of CAP in foods. Partial least squares regression models showed good prediction values (R > 0.9) of actual spiked contents (0, 0.1, 0.5, 1, 5 ppm) of CAP in milk and honey. This developed nano-biosensor is low cost, requires little sample pretreatment, and can provide reliable detection of trace level of chemical hazards in food systems within a total of 15 min.

Natural material variations uncorrelated with physical properties of fibre networks hinder the development of robust calibration models by which to predict paper properties from on-line near-infrared (NIR) spectra of production pulps. Such a simple process gauge of product quality would offer attractive advantages for optimized manufacturing. The present work explores the effectiveness of data processing strategies designed to remove uncorrelated variance from calibration models linking NIR spectra with standard measures of paper quality, including tensile, tear, burst index, wet and dry zero span length, freeness, absorption and scattering coefficients. Post-processing of spectra by discrete wavelet transform (DWT) is shown to suppress the baseline and high-frequency noise, and orthogonal signal correction (OSC) substantially improves prediction accuracy by reducing the amplitude of uncorrelated (orthogonal) variations. We find that combined pretreatment by DWT and OSC yields a spectral dataset that exhibits the best prediction accuracy.

Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially small in the number of qubits. In this technical note we theoretically motivate and empirically validate an initialization strategy which can resolve the barren plateau problem for practical applications. The technique involves randomly selecting some of the initial parameter values, then choosing the remaining values so that the circuit is a sequence of shallow blocks that each evaluates to the identity. This initialization limits the effective depth of the circuits used to calculate the first parameter update so that they cannot be stuck in a barren plateau at the start of training. In turn, this makes some of the most compact ansätze usable in practice, which was not possible before even for rather basic problems. We show empirically that variational quantum eigensolvers and quantum neural networks initialized using this strategy can be trained using a gradient based method.

In this paper, we present ideas that were part of the miniconference on the crossover between High Energy Density Plasmas (HEDP) and Ultracold Neutral Plasmas (UNPs) at the 60th Annual Meeting of the American Physical Society Division of Plasma Physics, November 2018. We give an overview of UNP experiments with an emphasis on measurements of the time-evolving ion density and velocity distributions, the electron-ion thermalization rate, and plasma self-assembly—all just inside the strongly coupled plasma regime. We also present theoretical and computational models that were developed to understand a subset of HEDP experiments. However, because HEDP experiments display similar degrees of strong coupling, many aspects of these models can be vetted using precision studies of UNPs. This comparison is important because some statistical assumptions used for ideal plasmas are of questionable validity in the strongly coupled plasma regime. We summarize two theoretical approaches that extend kinetic theories into the strong-coupling regime and show good agreement for momentum transfer and self-diffusion. As capabilities improve, both computationally and experimentally, UNP measurements may help guide the ongoing development of HEDP-appropriate plasma models. Future opportunities in viscosity, energy relaxation, and magnetized plasmas are discussed.In this paper, we present ideas that were part of the miniconference on the crossover between High Energy Density Plasmas (HEDP) and Ultracold Neutral Plasmas (UNPs) at the 60th Annual Meeting of the American Physical Society Division of Plasma Physics, November 2018. We give an overview of UNP experiments with an emphasis on measurements of the time-evolving ion density and velocity distributions, the electron-ion thermalization rate, and plasma self-assembly—all just inside the strongly coupled plasma regime. We also present theoretical and computational models that were developed to understand a subset of HEDP experiments. However, because HEDP experiments display similar degrees of strong coupling, many aspects of these models can be vetted using precision studies of UNPs. This comparison is important because some statistical assumptions used for ideal plasmas are of questionable validity in the strongly coupled plasma regime. We summarize two theoretical approaches that extend kinetic theories into t...

The exploration of large-scale many-body phenomena in quantum materials has produced many important experimental discoveries, including novel states of entanglement, topology and quantum order as found for example in quantum spin ices, topological insulators and semimetals, complex magnets, and high-$T_c$ superconductors. Yet, the sheer scale of solid-state systems and the difficulty of exercising exacting control of their quantum mechanical degrees of freedom limit the pace of rational progress in advancing the properties of these and other materials. With extraordinary effort to counteract natural processes of dissipation, precisely engineered ultracold quantum simulators could point the way to exotic new materials. Here, we look instead to the quantum mechanical character of the arrested state formed by a quenched ultracold molecular plasma. This novel class of system arises spontaneously, without a deliberate engineering of interactions, and evolves naturally from state-specified initial conditions, to a long-lived final state of canonical density, in a process that conflicts with classical notions of plasma dissipation and neutral dissociation. We take information from experimental observations to develop a conceptual argument that attempts to explain this state of arrested relaxation in terms of a minimal phenomenological model of randomly interacting dipoles of random energies. This model of the plasma forms a starting point to describe its observed absence of relaxation in terms of many-body localization (MBL). The large number of accessible Rydberg and excitonic states gives rise to an unconventional web of many-body interactions that vastly exceeds the complexity of MBL in a conventional few-level scheme. This experimental platform thus opens an avenue for the coupling of dipoles in disordered environments that will demand the development of new theoretical tools., Comment: 27 pages, 6 figures, 2 tables

We report the development of a novel hybrid "capture-detection" molecularly imprinted polymers-surface-enhanced Raman spectroscopic (MIPs-SERS) biosensor for the detection and quantification of α-tocopherol (α-Toc) in vegetable oils. α-Toc served as the template for MIPs synthesis. Methacrylic acid formed as the functional monomer. Ethylene glycol dimethacrylate was the cross-linking agent, and 2,2'-azobisisobutyronitrile was used as the initiator. The synthesized MIPs functioned to rapidly and selectively adsorb and separate α-Toc from oil components. We validated a dendritic silver nanostructure synthesized by a displacement reaction to be a suitable SERS substrate for the enhancement of Raman signals. Second-derivative transformations and chemometric models based upon SERS spectral features confirmed the possibility of a rapid and precise detection and quantification of different spiking levels of α-Toc in four different sources of vegetable oils (Mahalanobis distance from 15.93 to 34.01 for PCA model; R > 0.92, RMSE < 0.41 for PLSR model). The MIPs-SERS biosensor had a high sensitivity as well as a good recovery for α-Toc analysis in vegetable oils. The entire analysis required 15 min or less to complete with limited sample preparation.

The hemicellulose composition of a pulp significantly affects its chemical and physical properties and thus represents an important process control variable. However, complicated steps of sample preparation make standard methods for the carbohydrate analysis of pulp samples, such as high performance liquid chromatography (HPLC), expensive and time-consuming. In contrast, pulp analysis by attenuated total internal reflection Fourier transform infrared spectroscopy (ATR FT-IR) requires little sample preparation. Here we show that ATR FT-IR with discrete wavelet transform (DWT) and standard normal variate (SNV) spectral preprocessing offers a convenient means for the qualitative and quantitative analysis of hemicelluloses in bleached kraft pulp and alkaline treated kraft pulp. The pulp samples investigated include bleached softwood kraft pulps, bleached hardwood kraft pulps, and their mixtures, as obtained from Canadian industry mills or blended in a lab, and bleached kraft pulp samples treated with 0–6% NaOH solutions. In the principal component analysis (PCA) of these spectra, we find the potential both to differentiate all pulps on the basis of hemicellulose compositions and to distinguish bleached hardwood pulps by species. Partial least squares (PLS) multivariate analysis gives a 0.442 wt% root mean square errors of prediction (RMSEP) for the prediction of xylan content and 0.233 wt% RMSEP for the prediction of mannan content. These data all support the idea that ATR FT-IR has a great potential to rapidly and accurately predict the content of xylan and mannan for bleached kraft pulps (softwood, hardwood, and their mixtures) in industry. However, the prediction of xylan and mannan concentrations presented a difficulty for pulp samples with modified cellulose crystalline structure.

The double-resonant laser excitation of nitric oxide, cooled to 1 K in a seeded supersonic molecular beam, yields a gas of $\approx10^{12}$ molecules cm$^{-3}$ in a single selected Ryberg state. This population evolves to produce prompt free electrons and a durable cold plasma of electrons and intact NO$^{+}$ ions. This plasma travels with the molecular beam through a field free region to encounter a grid. The atomic weight of the expansion gas controls the beam velocity and hence the flight time from the interaction region to the grid. Monitoring electron production as the plasma traverses this grid measures its longitudinal width as a function of flight time. Comparing these widths to the width of the laser beam that defines the initial size of the illuminated volume allows us to gauge the rate of expansion of the plasma. We find that the plasma created from the evolution of a Rydberg gas of NO expands at a small but measurable rate, and that this rate of expansion accords with the Vlasov equations for an initial electron temperature of $T_{e} \approx 8 K$., Comment: Replaced oversized pdf file for figure 6

Penning ionization initiates the evolution of a dense molecular Rydberg gas to plasma. This process selects for pairs of excited molecules separated by a distance of two Rydberg orbital diameters or less. The deactivated Penning partners predissociate, depleting the leading edge of the distribution of nearest-neighbor distances. For certain density and orbital radii, this sequence of events can form a plasma in which large distances separate a disproportionate fraction of the ions. Experimental results and model calculations suggest that the reduced potential energy of this Penning lattice significantly affects the development of strong coupling in an ultracold plasma.

Double-resonant photoexcitation of nitric oxide in a molecular beam creates a dense ensemble of 51f(2) Rydberg states, which evolves to form a plasma of free electrons trapped in the potential well of an NO+ spacecharge. The plasma travels at the velocity of the molecular beam, and, on passing through a grounded grid, yields an electron time-of-flight signal that gauges the plasma size and quantity of trapped electrons. This plasma expands at a rate that fits with an electron temperature as low as 5 K. Dissociative recombination of NO+ ions with electrons provides the primary dissipation mechanism for the plasma. We have identified three dissociation pathways, and quantified their relative contributions to the measured rate: Two-body dissociative recombination competes with direct three-body recombination to neutral dissociation products, and with a process in which three-body recombination and electron-impact ionization form an equilibrium population of high-Rydberg states that decays by predissociation. Using available collision-theory rate constants for three-body recombination and ionization, together with quantum mechanical estimates of predissociation rates, we predict that the relaxation of the plasma to a high-Rydberg equilibrium outpaces direct three-body dissociative recombination, and, among second-order processes, the rate of two-body electron-cation dissociative recombination substantially exceeds the rate at which the high-Rydberg equilibrium dissociatively relaxes. The rate constant for dissociative recombination extracted from these data conforms with predictions drawn from theory for isolated electron-ion collisions. Methods based on the dissipation of molecular ultracold plasmas may provide a means for estimating rates of dissociative recombination for a variety of complex molecules.

We introduce a convolutional neural network for inferring a compact disentangled graphical description of objects from 2D images that can be used for volumetric reconstruction. The network comprises an encoder and a twin-tailed decoder. The encoder generates a disentangled graphics code. The first decoder generates a volume, and the second decoder reconstructs the input image using a novel training regime that allows the graphics code to learn a separate representation of the 3D object and a description of its lighting and pose conditions. We demonstrate this method by generating volumes and disentangled graphical descriptions from images and videos of faces and chairs.

When paired with high-powered chemometric analysis, spectrometric methods offer great promise for the high-throughput analysis of complex systems. Effective classification or quantification often relies on signal preprocessing to reduce spectral interference and optimize the apparent performance of a calibration model. However, less frequently addressed by systematic research is the affect of preprocessing on the statistical accuracy of a calibration result. The present work demonstrates the effectiveness of two criteria for validating the performance of signal preprocessing in multivariate models in the important dimensions of bias and precision. To assess the extent of bias, we explore the applicability of the elliptic joint confidence region (EJCR) test and devise a new means to evaluate precision by a bias-corrected root mean square error of prediction. We show how these criteria can effectively gauge the success of signal pretreatments in suppressing spectral interference while providing a straightforward means to determine the optimal level of model complexity. This methodology offers a graphical diagnostic by which to visualize the consequences of pretreatment on complex multivariate models, enabling optimization with greater confidence. To demonstrate the application of the EJCR criterion in this context, we evaluate the validity of representative calibration models using standard pretreatment strategies on three spectral data sets. The results indicate that the proposed methodology facilitates the reliable optimization of a well-validated calibration model, thus improving the capability of spectrophotometric analysis.

This paper presents a novel methodology, adaptive multiscale regression (AMR), to adaptively process Raman spectra for quantitative analysis. The proposed methodology aims to construct an optimal calibration model for a Raman spectrum at hand, regardless of its structural characteristics, thus facilitating the application of Raman spectroscopy as a general tool for analytical chemistry. AMR firstly splits the spectra in a calibration set into frequency components at different scales using adaptive wavelet transform (AWT). Parallel member models constructed at different scales are then fused into a final prediction. The contributions of member models to a fusion model are straightforwardly estimated by a partial least square (PLS) model that emerges from a cross-validation results matrix (X) and reference values (Y). This procedure avoids information leakage by fully utilizing the multiscale nature of the input Raman spectra instead of arbitrarily removing some part of the spectral information by calibrating to selected features. Theoretically, we establish that AMR represents an automatic data-driven strategy that captures the Raman spectral structures adaptively and accurately. Our work tests and refines the AMR method by drawing upon the systematic analysis of spectra formulated to yield challenges representative of those encountered in common Raman analyses. AMR compares favorably with other popular preprocessing methods. Satisfactory calibration results suggest that AMR has the capacity to improve robustness and reliability of Raman spectral analysis, and may well extend to other spectroscopic techniques.

The application of Raman spectroscopic techniques combined with multivariate chemometrics signal processing promise new means for the rapid multidimensional analysis of metabolites non-destructively, with little or no sample preparation and little sensitivity to water. However, Rayleigh scattering, fluorescence and uncontrolled variance present substantial challenges for the accurate quantitative analysis of metabolites at physiological levels in bio- logically varying samples. Effective strategies include the application of chemometrics pretreatments for reducing Raman spectral interference. However, the arbitrary application of individual or combined pretreatment procedures can significantly alter the outcome of a measurement, thereby complicating spectral analysis. This paper evaluates and compares six signal pretreatment methods for correcting the baseline variances, together with three variable se- lection methods for eliminating uninformative variables, all within the context of multivariate calibration models based on partial least squares （PLS） regression. Raman spectra of 90 artificial bio-fluid samples with eight urine metabolites at near-physiological concentrations were used to test these models. The combination of multiplicative scatter correction （MSC）, continuous wavelet transform （CWT）, randomization test （RT） and PLS modeling pre- sented the best performance for all the metabolites. The correlation coefficient （R） between predicted and prepared concentration reached as high as 0.96.

The looming obsolescence of 3 billion consumer electronics by 2010 calls for effective recycling of 10 billion pounds of high-value engineering thermoplastics. Because general shredding operations generate mixed plastics of low value, a switching rule is needed rapidly to identify plastics for sortation by colour and type to increase plastics-to-plastics recycling value. The current paper develops a heuristic rule to determine the thresholds for a switching rule that reduces set-up time while managing queue space. Our recycling system is modelled as a multiclass queuing network in a simulation model. Results indicate that laser identification probe utilization, setups, and queue space are sensitive to the switching rule. Our work contributes important insights into the relationship between traffic intensity, setup time, and queue capacity, and generalizes to multiclass queuing systems in other applications.

We have applied triple-resonant optical photoionization spectroscopy to characterize the lineshapes of autoionizing Rydberg resonances in 11BH converging to the X2Σ+ ground state of 11BH+ in vibrational levels from v + = 0 through 4. Third photon transitions originating from the B1Σ+ v′ = 0, 1, 2 or 3 states of 11BH reach high Rydberg resonances in Δv = 0 (below vertical threshold) and Δv = 1 (above vertical threshold) transitions. Observed spectra display contributions from discrete–continuum and discrete–discrete transition moments, where intensities are governed by Franck–Condon factors and the discrete–continuum mixing of final states. The spectra show Fano line profiles attributable to the phase interference between competing photoionization/photodissociation transition moments. Two fitting parameters, q and Γ, which characterize asymmetric spectral lineshapes in terms of an interaction between a single discrete state and single continuum, serve well to model the photoionization cross-section. Quanti...

Formation of a secondary plasma of ${\mathrm{NO}}^{+}$ ions and electrons within an ultracold plasma produces an observable change in the hydrodynamics of the system. Direct photoionization adds energetic electrons, which increases the rate of expansion. The introduction of a secondary Rydberg gas has the opposite effect. In both cases, the added ions create an inertial drag that acts initially to retard the expansion of the electron gas. A cold-ion hydrodynamic shell model, which accounts well for the effect of energy added by photoionization electrons, predicts the formation of collisionless shock waves.

The glucuronoxylan composition of a pulp affects the bonding between cellulosic fibres, and thus correlates with such network properties as tensile strength. Here, we demonstrate the promise of attenuated total-internal-reflection (ATR) FTIR spectroscopy as a rapid means for classifying the xylan contained in commercial bleached kraft pulps. This study draws upon samples composed of bleached eucalyptus kraft pulps and combinations of eucalyptus with other commercial bleached kraft pulps. We subject these pulp samples to systematic extraction by sodium hydroxide solutions with concentrations ranging from 0.5% to 6% to build a standard sample library with varying xylan content, quantified by acid hydrolysis, HPLC carbohydrate separation and titration. This pulp chemistry of mild alkaline extraction removes up to two-thirds of the xylan. In the NaOH concentration regime of 0.5–4%, the infrared spectral variance reflects the decrease in hemicellulose concentration as well as the cellulose crystallinity. A residual xylan component remains resistant to base solutions of higher concentrations. Principal component analysis of infrared spectra distinguishes this residual xylan as structurally variant. Both partial least squares multivariate analysis and univariate analysis confined to a feature at 964 cm −1 in normalized second derivative IR spectra show a very good correlation with xylan content quantified by HPLC.

We describe a novel instrument combining interferometric scattering microscopy (iSCAT) with confocal Raman microscopy. This system images the refractive index morphology of a complex material. Co-axial Raman sampling provides chemical information with a high degree of reproducibility.

Important challenges remain for sustainable design, manufacture, use, and recycling of electronics including materials selection and disassembly time. This paper examines the value relationship between the quantity of plastics separated and the time required for disassembly and segregation. Labor costs for disassembly can constitute a large portion of the total acquisition cost for a recycled material. We report work measurement studies conducted on the disassembly of 21 computers, 22 printers, and 32 monitors manufactured by 27 producers in the years from 1984 to 2001. Results include the weight per total separation time for each plastic part. Each recovered part is identified according to polymer resin using laser Raman spectroscopy by chemometric reference to a library of standards. We extrapolate time as well as the product input required to accumulate various specific types of plastic. We develop disassembly policies and show that they are effective for a variety of computer, printer, or monitor models, which is typical of the random product streams that arrive at electronics recycling facilities. The results demonstrate how new laser identification technology and work measurement can be used for plastics separation planning.

Millions of end-of-life (EOL) electronic products represent more than one million tons of engineering thermoplastics. The economically and environmentally sound recovery of engineering thermoplastics from EOL electronics is a challenge to the sustainability of electronics manufacturing. In this paper, we review the technologies to separate and identify pure post-consumer plastics from EOL electronics, which are followed by the comparison of electronic plastics recycling processes and the network models for plastics recycling processes. We also review successful plastics recycling practices for electronics. In addition, further research directions for recycling plastics from EOL electronics are discussed.

We report new results suggesting the feasibility of Raman spectrometry as a tool by which to examine the variability of tablet coatings. Our experiments feature a probe that can operate with a revolving laser focus to average content and coating non-uniformity. Raman spectral changes are correlated with tablet exposure times in a pan coater by means of partial least squares (PLS) multivariate analysis. Statistical models are found to be improved by pre-processing schemes that emphasize spectral changes while minimizing the effects of background light scattering and fluorescence. These pre-processing techniques include multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation, used in concert with Savitzky-Golay second derivative smoothing (SGSD). The two approaches give comparable results yielding R2 values for PLS calibration and cross-calibrated prediction variance regression of 0.999 and 0.997, respectively. Correlation results and model residual values demonstrate that Raman spectroscopy serves sensitively to reflect the coating thickness of the tablets studied.