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IEEE Conference on Nanotechnology

作者： Evans Addo-Mensah Janet Obaemo Katie Welch Yang Tian Shiva Dolatabadi Hugh Churchill Uchechukwu Wejinya Materials Science and Engineering Graduate Program University of Arkansas Microelectronics and Photonics Program University of Arkansas Department of Physics University of Arkansas Fayetteville Arkansas Department of Mechanical Engineering University of Arkansas Fayetteville Arkansas

The advancement of graphene has created a need in exploring its properties for different applications. One way to explore its properties is by reducing its hydrophobicity. To overcome hydrophobicity of graphene, surfactants have been used in functionalization, hence improving the surface properties of the graphene monolayer. Therefore, investigating surfactant treatment for CVD graphene becomes useful in understanding the surface property effects on graphene. This study utilizes CVD graphene on silicon substrates. Its treatment was done with varying concentrations of Sodium Cholate (SC) for different treatment times. These samples were then characterized using Atomic Force Microscopy (AFM) to investigate the surface properties of the samples before and after treatment. To be optimized, the graphene must remain attached to the silicon substrate. The result shows that the integrity of the graphene, which is basically the Sp2 structure, is preserved as there was no delamination from the substrate even after treatment for as long as 2 hours in 1% weight/volume concentration of the SC solution.

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Microplastic Pollution in Terrestrial Systems: Sources and Implications for Soil Functioning and Plant Performance

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Water, Air, and Soil Pollution 2025年第3期236卷 1-32页

作者： Naqash, Nafiaah Yadav, Krishna Kumar Shaik, Abdul Saddique Alam, Mir Waqas Djajadi, Djajadi Sunarto, Dwi Adi Marjani, Marjani Sujak, Sujak Rochman, Fatkhur Rachmawati, Diding Riajaya, Prima Diarini Kadarwati, Fitriningdyah Tri Rahayu, Farida Wani, Atif Khurshid School of Bioengineering and Biosciences Lovely Professional University Jalandhar144411 India Department of Environmental Science Parul Institute of Applied Sciences Parul University Vadodara Gujrat391760 India Environmental and Atmospheric Sciences Research Group Scientific Research Center Al-Ayen University Thi-Qar Nasiriyah64001 Iraq Department of Mechanical Engineering College of Engineering King Khalid University Abha Saudi Arabia Department of Physics College of Science King Faisal University Al Ahsa31982 Saudi Arabia Research Center for Estate Crops National Research and Innovation Agency Bogor16911 Indonesia Research Centre for Horticulture National Research and Innovation Agency Bogor16911 Indonesia Research Center for Genetic Engineering National Research and Innovation Agency Bogor16911 Indonesia

Recent years have seen a significant increase in microplastic contamination across terrestrial ecosystems, facilitated by various pathways such as atmospheric deposition, wastewater irrigation, and agricultural plastic mulching. This persistence of microplastics in soil raises concerns about their profound impacts on soil health and ecosystem dynamics. Key studies have highlighted detrimental effects, including reduced soil moisture retention, altered soil microbiota composition, and disrupted nutrient cycling processes. Moreover, microplastic pollution adversely affects soil biota, notably earthworms, crucial for soil nutrient cycling and structure maintenance, exhibiting reduced growth rates and increased mortality upon exposure. Notably, microplastics also influence soil microorganisms, potentially compromising overall soil health and ecosystem functioning. Co-exposure of microplastics and other contaminants can also synergistically exacerbate toxicity, impairing ecological balance. Evidence suggests negative repercussions on plant growth, including diminished seed germination rates and altered nutrient profiles in exposed plants. These findings underscore the urgent need for comprehensive reviews to synthesize existing knowledge, and identify research gaps, necessitating a focus on mitigation strategies. Addressing these issues is critical for safeguarding terrestrial ecosystems and ensuring sustainable environmental management in the face of increasing microplastic contamination. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Anthropogenic

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RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equations

arXiv

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arXiv 2024年

作者： Guo, Jiaqi Lopez-Tapia, Santiago Li, Wing Shun Wu, Yunnan Carignano, Marcelo Backman, Vadim Dravid, Vinayak P. Katsaggelos, Aggelos K. The Department of Electrical and Computer Engineering Northwestern University IL60201 United States The Applied Physics Program Northwestern University EvanstonIL60201 United States

Computed tomography is a widely used imaging modality with applications ranging from medical imaging to material analysis. One major challenge arises from the lack of scanning information at certain angles, leading to distorted CT images with artifacts. This results in an ill-posed problem known as the Limited Angle Computed Tomography (LACT) reconstruction problem. To address this problem, we propose Residual Null-Space Diffusion Stochastic Differential Equations (RN-SDEs), which are a variant of diffusion models that characterize the diffusion process with mean-reverting (MR) stochastic differential equations. To demonstrate the generalizability of RN-SDEs, our experiments are conducted on two different LACT datasets, i.e., ChromSTEM and C4KC-KiTS. Through extensive experiments, we show that by leveraging learned Mean-Reverting SDEs as a prior and emphasizing data consistency using Range-Null Space Decomposition (RNSD) based rectification, RN-SDEs can restore high-quality images from severe degradation and achieve state-of-the-art performance in most LACT tasks. Additionally, we present a quantitative comparison of computational complexity and runtime efficiency, highlighting the superior effectiveness of our proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Information geometry of the retinal representation manifold 23

Information geometry of the retinal representation manifold

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xuehao Ding Dongsoo Lee Joshua B. Melander George Sivulka Surya Ganguli Stephen A. Baccus Department of Applied Physics Stanford University Neurosciences Phd Program Stanford University Department of Electrical Engineering Stanford University Department of Neurobiology Stanford University

The ability for the brain to discriminate among visual stimuli is constrained by their retinal representations. Previous studies of visual discriminability have been limited to either low-dimensional artificial stimuli or pure theoretical considerations without a realistic encoding model. Here we propose a novel framework for understanding stimulus discriminability achieved by retinal representations of naturalistic stimuli with the method of information geometry. To model the joint probability distribution of neural responses conditioned on the stimulus, we created a stochastic encoding model of a population of salamander retinal ganglion cells based on a three-layer convolutional neural network model. This model not only accurately captured the mean response to natural scenes but also a variety of second-order statistics. With the model and the proposed theory, we computed the Fisher information metric over stimuli to study the most discriminable stimulus directions. We found that the most discriminable stimulus varied substantially across stimuli, allowing an examination of the relationship between the most discriminable stimulus and the current stimulus. By examining responses generated by the most discriminable stimuli we further found that the most discriminative response mode is often aligned with the most stochastic mode. This finding carries the important implication that under natural scenes, retinal noise correlations are information-limiting rather than increasing information transmission as has been previously speculated. We additionally observed that sensitivity saturates less in the population than for single cells and that as a function of firing rate, Fisher information varies less than sensitivity. We conclude that under natural scenes, population coding benefits from complementary coding and helps to equalize the information carried by different firing rates, which may facilitate decoding of the stimulus under principles of information maximizatio

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Josephson-Anderson relation as diagnostic of turbulent drag reduction by polymers

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Physical Review E 2025年第1期111卷 015105-015105页

作者： Samvit Kumar Simon Toedtli Tamer A. Zaki Gregory L. Eyink Department of Applied Mathematics & Statistics Johns Hopkins University Baltimore Maryland 21218 USA Department of Mechanical Engineering Johns Hopkins University Baltimore Maryland 21218 USA Department of Physics & Astronomy The Johns Hopkins University Baltimore Maryland 21218 USA

The detailed Josephson-Anderson relation, which equates instantaneously the volume-integrated vorticity flux and the work by pressure drop, has been the key to drag reduction in superconductors and superfluids. We employ a classical version of this relation to investigate the dynamics of polymer drag-reduced channel flows, particularly in the high-extent drag reduction (HDR) regime which is known to exhibit strong space-time intermittency. We show that high drag is not created instantaneously by near-wall coherent vortex structures as assumed in prior works. These predominantly spanwise near-wall vortex structures can produce a net “up-gradient” flux of vorticity toward the wall, which instead reduces instantaneous drag. Increase of wall vorticity and skin friction due to this up-gradient flux occurs after an apparent lag of several advection times, increasing with the Weissenberg number. This increasing lag appears to be due to polymer damping of up-gradient nonlinear vorticity transport that arises from large-scale eddies in the logarithmic layer. The relatively greater polymer damping of down-gradient transport due to small-scale eddies results in lower net vorticity flux and hence lower drag. The Josephson-Anderson relation thus provides an exact tool to diagnose the mechanism of polymer drag reduction in terms of vorticity dynamics and it explains also prior puzzling observations on transient drag reduction, as for centerline-release experiments in pipe flow.

关键词： Vorticity

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Cornerstones are the Key Stones: Using Interpretable Machine Learning to Probe the Clogging Process in 2D Granular Hoppers

arXiv

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arXiv 2024年

作者： Hanlan, Jesse M. Dillavou, Sam Liu, Andrea J. Durian, Douglas J. Department of Physics & Astronomy University of Pennsylvania PhiladelphiaPA19104-6396 United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

The sudden arrest of flow by formation of a stable arch over an outlet is a unique and characteristic feature of granular materials. Previous work suggests that grains near the outlet randomly sample configurational flow microstates until a clog-causing flow microstate is reached. However, factors that lead to clogging remain elusive. Here we experimentally observe over 50,000 clogging events for a tridisperse mixture of quasi-2D circular grains, and utilize a variety of machine learning (ML) methods to search for predictive signatures of clogging microstates. This approach fares just modestly better than chance. Nevertheless, our analysis using linear Support Vector Machines (SVMs) highlights the position of potential arch cornerstones as a key factor in clogging likelihood. We verify this experimentally by varying the position of a fixed (cornerstone) grain, and show that such a grain dictates the size of feasible flow-ending arches, and thus the time and mass of each flow. Positioning this grain correctly can even increase the ejected mass by over 50%. Our findings demonstrate that interpretable ML algorithms like SVMs can uncover meaningful physics even when their predictive power is below the standards of conventional ML practice. Copyright © 2024, The Authors. All rights reserved.

关键词： Arches

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engineering dimensionality of collective dipole-dipole interactions using a nanophotonic environment

Engineering dimensionality of collective dipole-dipole inter...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Boddeti, Ashwin K. Wang, Yi Juarez, Xitlali Boltasseva, Alexandra Alaeian, Hadiseh Odom, Teri W. Jacob, Zubin Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States

ISBN: (纸本)9781957171258

We experimentally demonstrate reduced dimensionality in a interacting ensemble of emitters. The well-known stretched exponential decay dynamics, (Equation presented) with β = 0.5 in 3D geometries, is strikingly modified in a nanophotonic environment that supports long-range interaction to β ~ 0.3. © Optica Publishing Group 2023.

关键词： Nanophotonics

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Extended tensor decomposition model reduction methods: training, prediction, and design under uncertainty

arXiv

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arXiv 2023年

作者： Lu, Ye Mojumder, Satyajit Guo, Jiachen Li, Yangfan Liu, Wing Kam Department of Mechanical Engineering University of Maryland Baltimore County Baltimore United States Theoretical and Applied Mechanics Program Northwestern University Evanston United States Department of Mechanical Engineering Northwestern University Evanston United States

This paper introduces an extended tensor decomposition (XTD) method for model reduction. The proposed method is based on a sparse non-separated enrichment to the conventional tensor decomposition, which is expected to improve the approximation accuracy and the reducibility (compressibility) in highly nonlinear and singular cases. The proposed XTD method can be a powerful tool for solving nonlinear space-time parametric problems. The method has been successfully applied to parametric elastic-plastic problems and real time additive manufacturing residual stress predictions with uncertainty quantification. Furthermore, a combined XTD-SCA (self-consistent clustering analysis) strategy is presented for multi-scale material modeling, which enables real time multi-scale multi-parametric simulations. The efficiency of the method is demonstrated with comparison to finite element analysis. The proposed method enables a novel framework for fast manufacturing and material design with uncertainties. Copyright © 2023, The Authors. All rights reserved.

关键词： 3D printing

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Understanding the learning mechanism of convolutional neural network applied to fluorescence spectra 4

Understanding the learning mechanism of convolutional neural...

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AI and Optical Data Sciences IV 2023

作者： Venturini, Francesca Michelucci, Umberto Sperti, Michela Gucciardi, Arnaud Deriu, Marco A. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Technikumstrasse 9 Winterthur8401 Switzerland TOELT llc Machine Learning Research and Development Birchlenstr. 25 Dübendorf8600 Switzerland Lucerne University of Applied Sciences and Arts Computer Science Department Lucerne Switzerland PolitoBIOMed Lab Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy Artificial Intelligence Laboratory University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)9781510659810

The power of artificial neural networks to determine the quality and properties of olive oil was proven by several studies in the last years. Less clear is, however, how the neural network is able to extract useful information from the input data. This work investigates the learning mechanism of one-dimensional convolutional neural networks (1D-CNNs) trained to predict the physicochemical properties of olive oil from single fluorescence spectra. Such a 1D-CNN can successfully predict the parameters relevant to the quality assessment: acidity, peroxide value, and UV absorbance. To go beyond a simple quality assessment algorithm, it is important to identify which spectral features in the measured spectra are correlated with each chemical parameter and therefore with the quality of olive oil. To obtain this information, explainability techniques can be used by studying the latent feature space generated by the intermediate layers of the one-dimensional trained convolutional neural network. This work analyses in detail the common features that are used by the 1D-CNN to predict the two physicochemical parameters: acidity and K232 © 2023 SPIE.

关键词： Olive oil

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DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods

DeepSeeColor: Realtime Adaptive Color Correction for Autonom...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Stewart Jamieson Jonathan P. How Yogesh Girdhar MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering Department of Aeronautics and Astronautics Massachusetts Institute of Technology (MIT) Applied Ocean Physics and Engineering Department Woods Hole Oceanographic Institution (WHOI)

Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenomena involved in underwater image formation. Spectrally-selective light attenuation drains some colors from underwater images while backscattering adds others, making it challenging to perform vision-based tasks underwater. State-of-the-art methods for underwater color correction optimize the parameters of image formation models to restore the full spectrum of color to underwater imagery. However, these methods have high computational complexity that is unfavourable for realtime use by autonomous underwater vehicles (AUVs), as a result of having been primarily designed for offline color correction. Here, we present DeepSeeColor, a novel algorithm that combines a state-of-the-art underwater image formation model with the computational efficiency of deep learning frameworks. In our experiments, we show that DeepSeeColor offers comparable performance to the popular “Sea-Thru” algorithm [1] while being able to rapidly process images at up to 60Hz, thus making it suitable for use onboard AUVs as a preprocessing step to enable more robust vision-based behaviours.

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