- Award ID(s):
- 1663041
- NSF-PAR ID:
- 10281751
- Date Published:
- Journal Name:
- Communications Materials
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2662-4443
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Chemical organization in reaction-diffusion systems offers a strategy for the generation of materials with ordered morphologies and structural hierarchy. Periodic structures are formed by either molecules or nanoparticles. On the premise of new directing factors and materials, an emerging frontier is the design of systems in which the precipitation partners are nanoparticles and molecules. We show that solvent evaporation from a suspension of cellulose nanocrystals (CNCs) and l -(+)-tartaric acid [ l -(+)-TA] causes phase separation and precipitation, which, being coupled with a reaction/diffusion, results in rhythmic alternation of CNC-rich and l -(+)-TA–rich rings. The CNC-rich regions have a cholesteric structure, while the l -(+)-TA–rich bands are formed by radially aligned elongated bundles. The moving edge of the pattern propagates with a finite constant velocity, which enables control of periodicity by varying film preparation conditions. This work expands knowledge about self-organizing reaction-diffusion systems and offers a strategy for the design of self-organizing materials.more » « less
-
Mechanical decrystallization and water-promoted recrystallization of cellulose were studied to understand the effects of cellulose crystallinity on reaction engineering models of its acid-catalyzed hydrolysis. Microcrystalline cellulose was ball-milled for different periods of time, which decreased its crystallinity and increased the glucose yield obtained from acid hydrolysis treatment. Crystallinity increased after acid hydrolysis treatment, which has previously been explained in terms of rapid hydrolysis of amorphous cellulose, despite conflicting evidence of solvent promoted recrystallization. To elucidate the mechanism, decrystallized samples were subjected to various non-hydrolyzing treatments involving water exposure. Interestingly, all non-hydrolyzing hydrothermal treatments resulted in recovery of crystallinity, including a treatment consisting of heat-up and quenching that was selected as a way to estimate the crystallinity at the onset of hydrolysis. Therefore, the proposed mechanism involving rapid hydrolysis of amorphous cellulose must be incomplete, since the recrystallization rate of amorphous cellulose is greater than the hydrolysis rate. Several techniques (solid-state nuclear magnetic resonance, X-ray diffraction, and Raman spectroscopy) were used to establish that water contact promotes conversion of amorphous cellulose to a mixture of crystalline cellulose I and cellulose II. Crystallite size may also be reduced by the decrystallization-recrystallization treatment. Ethanolysis was used to confirm that the reactivity of the cellulose I/cellulose II mixture is distinct from that of truly amorphous cellulose. These results strongly point to a revised, more realistic model of hydrolysis of mechanically decrystallized cellulose, involving recrystallization and hydrolysis of the cellulose I/cellulose II mixture.more » « less
-
Abstract The reaction-diffusion system is naturally used in chemistry to represent substances reacting and diffusing over the spatial domain. Its solution illustrates the underlying process of a chemical reaction and displays diverse spatial patterns of the substances. Numerical methods like finite element method (FEM) are widely used to derive the approximate solution for the reaction-diffusion system. However, these methods require long computation time and huge computation resources when the system becomes complex. In this paper, we study the physics of a two-dimensional one-component reaction-diffusion system by using machine learning. An encoder-decoder based convolutional neural network (CNN) is designed and trained to directly predict the concentration distribution, bypassing the expensive FEM calculation process. Different simulation parameters, boundary conditions, geometry configurations and time are considered as the input features of the proposed learning model. In particular, the trained CNN model manages to learn the time-dependent behaviour of the reaction-diffusion system through the input time feature. Thus, the model is capable of providing concentration prediction at certain time directly with high test accuracy (mean relative error <3.04%) and 300 times faster than the traditional FEM. Our CNN-based learning model provides a rapid and accurate tool for predicting the concentration distribution of the reaction-diffusion system.
-
Accurate traffic speed prediction is critical to many applications, from routing and urban planning to infrastructure management. With sufficient training data where all spatio-temporal patterns are well- represented, machine learning models such as Spatial-Temporal Graph Convolutional Networks (STGCN), can make reasonably accurate predictions. However, existing methods fail when the training data distribution (e.g., traffic patterns on regular days) is different from test distribution (e.g., traffic patterns on special days). We address this challenge by proposing a traffic-law-informed network called Reaction-Diffusion Graph Ordinary Differential Equation (RDGODE) network, which incorporates a physical model of traffic speed evolution based on a reliable and interpretable reaction- diffusion equation that allows the RDGODE to adapt to unseen traffic patterns. We show that with mismatched training data, RDGODE is more robust than the state-of-the-art machine learning methods in the following cases. (1) When the test dataset exhibits spatio-temporal patterns not represented in the training dataset, the performance of RDGODE is more consistent and reliable. (2) When the test dataset has missing data, RDGODE can maintain its accuracy by intrinsically imputing the missing values.more » « less
-
Abstract Natural gas hydrate is often found in marine sediment in heterogeneous distributions in different sediment types. Diffusion may be a dominant mechanism for methane migration and affect hydrate distribution. We use a 1‐D advection‐diffusion‐reaction model to understand hydrate distribution in and surrounding thin coarse‐grained layers to examine the sensitivity of four controlling factors in a diffusion‐dominant gas hydrate system. These factors are the particulate organic carbon content at seafloor, the microbial reaction rate constant, the sediment grading pattern, and the cementation factor of the coarse‐grained layer. We use available data at Walker Ridge 313 in the northern Gulf of Mexico where two ~3‐m‐thick hydrate‐bearing coarse‐grained layers were observed at different depths. The results show that the hydrate volume and the total amount of methane within thin, coarse‐grained layers are most sensitive to the particulate organic carbon of fine‐grained sediments when deposited at the seafloor. The thickness of fine‐grained hydrate free zones surrounding the coarse‐grained layers is most sensitive to the microbial reaction rate constant. Moreover, it may be possible to estimate microbial reaction rate constants at other locations by studying the thickness of the hydrate free zones using the Damköhler number. In addition, we note that sediment grading patterns have a strong influence on gas hydrate occurrence within coarse‐grained layers.