Understanding Rapid PET Degradation via Reactive Molecular Dynamics Simulation and Kinetic Modeling.

As the demand for PET plastic products continues to grow, developing effective processes to reduce their pollution is of critical importance. Pyrolysis, a promising technology to produce lighter and recyclable components from wasted plastic products, has therefore received considerable attention. In this work, the rapid pyrolysis of PET was studied by using reactive molecular dynamics (MD) simulations. Mechanisms for yielding gas species were unraveled, which involve the generation of ethylene and TPA radicals from ester oxygen-alkyl carbon bond dissociation and condensation reactions to consume TPA radicals with the products of long chains containing a phenyl benzoate structure and CO2. As atomistic simulations are typically conducted at the time scale of a few nanoseconds, a high temperature (i.e., >1000 K) is adopted for accelerated reaction events. To apply the results from MD simulations to practical pyrolysis processes, a kinetic model based on a set of ordinary differential equations was established, which is capable of describing the key products of PET pyrolysis as a function of time and temperature. It was further exploited to determine the optimal reaction conditions for low environmental impact. Overall, this study conducted a detailed mechanism study of PET pyrolysis and established an effective kinetic model for the main species. The approach presented herein to extract kinetic information such as detailed kinetic constants and activation energies from atomistic MD simulations can also be applied to related systems such as the pyrolysis of other polymers.

Predicting the Solubility of Organic Energy Storage Materials Based on Functional Group Identity and Substitution Pattern.

Organic electrode materials (OEMs) provide sustainable alternatives to conventional electrode materials based on transition metals. However, the application of OEMs in lithium-ion and redox flow batteries requires either low or high solubility. Currently, the identification of new OEM candidates relies on chemical intuition and trial-and-error experimental testing, which is costly and time intensive. Herein, we develop a simple empirical model that predicts the solubility of anthraquinones based on functional group identity and substitution pattern. Within this statistical scaffold, a training set of 18 anthraquinone derivatives allows us to predict the solubility of 808 quinones. Internal and external validations show that our model can predict the solubility of anthraquinones in battery electrolytes within log S ± 0.7, which is a much higher accuracy than existing solubility models. As a demonstration of the utility of our approach, we identified several new anthraquinones with low solubilities and successfully demonstrated their utility experimentally in Li-organic cells.

Deep learning for characterizing the self-assembly of three-dimensional colloidal systems.

Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems' stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about structures unless these are well-defined reference crystalline structures. Dimensionality reduction methods are thus required to translate the neighborhood graphs into a low-dimensional space that can be easily interpreted and used to characterize non-reference structures. We investigate a framework for colloidal system state characterization that employs deep learning methods to reduce the dimensionality of neighborhood graphs. The framework next uses agglomerative hierarchical clustering techniques to partition the low-dimensional space and assign physically meaningful classifications to the resulting partitions. We first demonstrate the proposed colloidal self-assembly state characterization framework on a three-dimensional in silico system of 500 multi-flavored colloids that self-assemble under isothermal conditions. We next investigate the generalizability of the characterization framework by applying the framework to several independent self-assembly trajectories, including a three-dimensional in silico system of 2052 colloidal particles that undergo evaporation-induced self-assembly.

Ultrasensitive and Selective Detection of SARS-CoV-2 Using Thermotropic Liquid Crystals and Image-Based Machine Learning.

Rapid, robust virus-detection techniques with ultrahigh sensitivity and selectivity are required for the outbreak of the pandemic coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Here, we report that the femtomolar concentrations of single-stranded ribonucleic acid (ssRNA) of SARS-CoV-2 trigger ordering transitions in liquid crystal (LC) films decorated with cationic surfactant and complementary 15-mer single-stranded deoxyribonucleic acid (ssDNA) probe. More importantly, the sensitivity of the LC to the SARS ssRNA, with a 3-bp mismatch compared to the SARS-CoV-2 ssRNA, is measured to decrease by seven orders of magnitude, suggesting that the LC ordering transitions depend strongly on the targeted oligonucleotide sequence. Finally, we design a LC-based diagnostic kit and a smartphone-based application (app) to enable automatic detection of SARS-CoV-2 ssRNA, which could be used for reliable self-test of SARS-CoV-2 at home without the need for complex equipment or procedures.

Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions.

We present a novel surrogate modeling method that can be used to accelerate the solution of uncertainty quantification (UQ) problems arising in nonlinear and non-smooth models of biological systems. In particular, we focus on dynamic flux balance analysis (DFBA) models that couple intracellular fluxes, found from the solution of a constrained metabolic network model of the cellular metabolism, to the time-varying nature of the extracellular substrate and product concentrations. DFBA models are generally computationally expensive and present unique challenges to UQ, as they entail dynamic simulations with discrete events that correspond to switches in the active set of the solution of the constrained intracellular model. The proposed non-smooth polynomial chaos expansion (nsPCE) method is an extension of traditional PCE that can effectively capture singularities in the DFBA model response due to the occurrence of these discrete events. The key idea in nsPCE is to use a model of the singularity time to partition the parameter space into two elements on which the model response behaves smoothly. Separate PCE models are then fit in both elements using a basis-adaptive sparse regression approach that is known to scale well with respect to the number of uncertain parameters. We demonstrate the effectiveness of nsPCE on a DFBA model of an E. coli monoculture that consists of 1075 reactions and 761 metabolites. We first illustrate how traditional PCE is unable to handle problems of this level of complexity. We demonstrate that over 800-fold savings in computational cost of uncertainty propagation and Bayesian estimation of parameters in the substrate uptake kinetics can be achieved by using the nsPCE surrogates in place of the full DFBA model simulations. We then investigate the scalability of the nsPCE method by utilizing it for global sensitivity analysis and maximum a posteriori estimation in a synthetic metabolic network problem with a larger number of parameters related to both intracellular and extracellular quantities.

An Analytical Solution for Exciton Generation, Reaction, and Diffusion in Nanotube and Nanowire-Based Solar Cells.

Excitonic solar cells based on aligned or unaligned networks of nanotubes or nanowires offer advantages with respect of optical absorption, and control of excition and electrical carrier transport; however, there is a lack of predictive models of the optimal orientation and packing density of such devices to maximize efficiency. Here-in, we develop a concise analytical framework that describes the orientation and density trade-off on exciton collection computed from a deterministic model of a carbon nanotube (CNT) photovoltaic device under steady-state operation that incorporates single- and aggregate-nanotube photophysics published earlier (Energy Environ Sci, 2014, 7, 3769). We show that the maximal film efficiency is determined by a parameter grouping, α, representing the product of the network density and the effective exciton diffusion length, reflecting a cooperativity between the rate of exciton generation and the rate of exciton transport. This allows for a simple, master plot of EQE versus film thickness, parametric in α allowing for optimal design. This analysis extends to any excitonic solar cell with anisotropic transport elements, including polymer, nanowire, quantum dot, and nanocarbon photovoltaics.

Layer number dependence of MoS2 photoconductivity using photocurrent spectral atomic force microscopic imaging.

Atomically thin MoS2 is of great interest for electronic and optoelectronic applications because of its unique two-dimensional (2D) quantum confinement; however, the scaling of optoelectronic properties of MoS2 and its junctions with metals as a function of layer number as well the spatial variation of these properties remain unaddressed. In this work, we use photocurrent spectral atomic force microscopy (PCS-AFM) to image the current (in the dark) and photocurrent (under illumination) generated between a biased PtIr tip and MoS2 nanosheets with thickness ranging between n = 1 to 20 layers. Dark current measurements in both forward and reverse bias reveal characteristic diode behavior well-described by Fowler-Nordheim tunneling with a monolayer barrier energy of 0.61 eV and an effective barrier scaling linearly with layer number. Under illumination at 600 nm, the photocurrent response shows a marked decrease for layers up to n = 4 but increasing thereafter, which we describe using a model that accounts for the linear barrier increase at low n, but increased light absorption at larger n creating a minimum at n = 4. Comparative 2D Fourier analysis of physical height and photocurrent images shows high spatial frequency spatial variations in substrate/MoS2 contact that exceed the frequencies imposed by the underlying substrates. These results should aid in the design and understanding of optoelectronic devices based on quantum confined atomically thin MoS2.