Crude oil fouling on membrane surfaces is a persistent, crippling challenge in oil spill remediation and oilfield wastewater treatment. In this research, we present how a nanosized oxide coating can ...profoundly affect the anti-crude-oil property of membrane materials. Select oxide coatings with a thickness of ∼10 nm are deposited conformally on common polymer membrane surfaces by atomic layer deposition to significantly mitigate fouling during filtration processes. TiO2- and SnO2-coated membranes exhibited far greater anti-crude-oil performance than ZnO- and Al2O3-coated ones. Tightly bound hydration layers play a crucial role in protecting the surface from crude oil adhesion, as revealed by molecular dynamics simulations. This work provides a facile strategy to fabricate crude-oil-resistant membranes with negligible impact on membrane structure, and also demonstrates that, contrary to common belief, excellent crude oil resistance can be achieved easily without implementation of sophisticated, hierarchical structures.
Versatile dyes based on benzothiadiazole and benzoselenadiazole chromophores have been developed that perform efficiently in dye-sensitized solar cells. Power conversion efficiency of 3.77% is ...realized for a dye in which charge recombination is probably hindered by the nonplanar charge-separated structure.
ABSTRACT
Along their path from source to observer, gravitational waves may be gravitationally lensed by massive objects leading to distortion in the signals. Searches for these distortions amongst ...the observed signals from the current detector network have already been carried out, though there have as yet been no confident detections. However, predictions of the observation rate of lensing suggest detection in the future is a realistic possibility. Therefore, preparations need to be made to thoroughly investigate the candidate lensed signals. In this work, we present some follow-up analyses that could be applied to assess the significance of such events and ascertain what information may be extracted about the lens-source system by applying these analyses to a number of O3 candidate events, even if these signals did not yield a high significance for any of the lensing hypotheses. These analyses cover the strong lensing, millilensing, and microlensing regimes. Applying these additional analyses does not lead to any additional evidence for lensing in the candidates that have been examined. However, it does provide important insight into potential avenues to deal with high-significance candidates in future observations.
Molecular dynamics with predefined functional forms is a popular technique for understanding dynamical evolution of systems. The predefined functional forms impose limits on the physics that can be ...captured. Artificial neural network (ANN) models have emerged as an attractive flexible alternative to the expensive quantum calculations (e.g., density functional theory) in the area of molecular force-fields. Ideally, if one is able to train a ANN to accurately predict the correct DFT energy and forces for any given structure, they gain the ability to perform molecular dynamics with high accuracy while simultaneously reducing the computation cost in a dramatic fashion. While this goal is very lucrative, neural networks are interpolative and therefore, it is not always clear how one should go about training a neural network to exhaustively fit the entire phase space of a given system. Currently, ANNs are trained by generating large quantities (on the order of 104 or greater) of training data in hopes that the ANN has adequately sampled the energy landscape both near and far-from-equilibrium. This can, however, be a bit prohibitive when it comes to more accurate levels of quantum theory. As such, it is desirable to train a model using the absolute minimal data set possible, especially when costs of high-fidelity calculations such as CCSD and QMC are high. Here, we present an active learning approach that iteratively trains an ANN model to faithfully replicate the coarse-grained energy surface of water clusters using only 426 total structures in its training data. Our active learning workflow starts with a sparse training data set which is continually updated via a Nested Ensemble Monte Carlo scheme that sparsely queries the energy landscape and tests the network performance. Next, the network is retrained with an updated training set that includes failed configurations/energies from previous iteration until convergence is attained. Once trained, we generate an extensive test set of 100 000 configurations sampled across clusters ranging from 1 to 200 molecules and demonstrate that the trained network adequately reproduces the energies (within mean absolute error (MAE) of 2 meV/molecule) and forces (MAE 40 meV/Å) compared to the reference model. More importantly, the trained ANN model also accurately captures both the structure as well as the free energy as a function of the various cluster sizes. Overall, this study reports a new active learning scheme with promising strategy to develop accurate force-fields for molecular simulations using extremely sparse training data sets.
Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide ...functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here we introduce a machine learning workflow-AI-expert-that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.
Structural defects govern various physical, chemical, and optoelectronic properties of two-dimensional transition-metal dichalcogenides (TMDs). A fundamental understanding of the spatial distribution ...and dynamics of defects in these low-dimensional systems is critical for advances in nanotechnology. However, such understanding has remained elusive primarily due to the inaccessibility of (a) necessary time scales via standard atomistic simulations and (b) required spatiotemporal resolution in experiments. Here, we take advantage of supervised machine learning, in situ high-resolution transmission electron microscopy (HRTEM) and molecular dynamics (MD) simulations to overcome these limitations. We combine genetic algorithms (GA) with MD to investigate the extended structure of point defects, their dynamical evolution, and their role in inducing the phase transition between the semiconducting (2H) and metallic (1T) phase in monolayer MoS2. GA-based structural optimization is used to identify the long-range structure of randomly distributed point defects (sulfur vacancies) for various defect densities. Regardless of the density, we find that organization of sulfur vacancies into extended lines is the most energetically favorable. HRTEM validates these findings and suggests a phase transformation from the 2H-to-1T phase that is localized near these extended defects when exposed to high electron beam doses. MD simulations elucidate the molecular mechanism driving the onset of the 2H to 1T transformation and indicate that finite amounts of 1T phase can be retained by increasing the defect concentration and temperature. This work significantly advances the current understanding of defect structure/evolution and structural transitions in 2D TMDs, which is crucial for designing nanoscale devices with desired functionality.
Several strands of evidence question the dogma that human mitochondrial DNA (mtDNA) is inherited exclusively down the maternal line, most recently in three families where several individuals harbored ...a 'heteroplasmic haplotype' consistent with biparental transmission. Here we report a similar genetic signature in 7 of 11,035 trios, with allelic fractions of 5-25%, implying biparental inheritance of mtDNA in 0.06% of offspring. However, analysing the nuclear whole genome sequence, we observe likely large rare or unique nuclear-mitochondrial DNA segments (mega-NUMTs) transmitted from the father in all 7 families. Independently detecting mega-NUMTs in 0.13% of fathers, we see autosomal transmission of the haplotype. Finally, we show the haplotype allele fraction can be explained by complex concatenated mtDNA-derived sequences rearranged within the nuclear genome. We conclude that rare cryptic mega-NUMTs can resemble paternally mtDNA heteroplasmy, but find no evidence of paternal transmission of mtDNA in humans.
The present study reviewed voxel-based morphometry (VBM) studies on high-risk individuals with schizophrenia, patients experiencing their first-episode schizophrenia (FES), and those with chronic ...schizophrenia. We predicted that gray matter abnormalities would show progressive changes, with most extensive abnormalities in the chronic group relative to FES and least in the high-risk group.
Forty-one VBM studies were reviewed. Eight high-risk studies, 14 FES studies, and 19 chronic studies were analyzed using anatomical likelihood estimation meta-analysis.
Less gray matter in the high-risk group relative to controls was observed in anterior cingulate regions, left amygdala, and right insula. Lower gray matter volumes in FES compared with controls were also found in the anterior cingulate and right insula but not the amygdala. Lower gray matter volumes in the chronic group were most extensive, incorporating similar regions to those found in FES and high-risk groups but extending to superior temporal gyri, thalamus, posterior cingulate, and parahippocampal gryus. Subtraction analysis revealed less frontotemporal, striatal, and cerebellar gray matter in FES than the high-risk group; the high-risk group had less gray matter in left subcallosal gyrus, left amygdala, and left inferior frontal gyrus compared with FES. Subtraction analysis confirmed lower gray matter volumes through ventral-dorsal anterior cingulate, right insula, left amygdala and thalamus in chronic schizophrenia relative to FES.
Frontotemporal brain structural abnormalities are evident in nonpsychotic individuals at high risk of developing schizophrenia. The present meta-analysis indicates that these gray matter abnormalities become more extensive through first-episode and chronic illness. Thus, schizophrenia appears to be a progressive cortico-striato-thalamic loop disorder.
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