scholarly journals Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos

2020 ◽  
Vol 6 (8) ◽  
pp. 1421-1430 ◽  
Author(s):  
Lehan Yao ◽  
Zihao Ou ◽  
Binbin Luo ◽  
Cong Xu ◽  
Qian Chen
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Nanoparticle Dynamics

scholarly journals Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 413
Author(s):  
Sandra Lopez-Zamora ◽  
Jeonghoon Kong ◽  
Salvador Escobedo ◽  
Hugo de Lasa
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Phase Equilibria ◽  
Aspen Plus ◽  
Phase Region ◽  
Two Phase ◽  
Technical Literature ◽  
Phase Liquid ◽  
Liquid Vapor ◽  
Vapor Liquid

The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_ Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10-4 mol fraction range, and it is larger than the 10-5 mol fraction predicted by Aspen Plus and the 10-7 mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.

Two-phase flow regime identification based on the liquid-phase velocity information and machine learning

2020 ◽  
Vol 61 (10) ◽  
Author(s):  
Yongchao Zhang ◽  
Amirah Nabilah Azman ◽  
Ke-Wei Xu ◽  
Can Kang ◽  
Hyoung-Bum Kim
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Phase Velocity ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Regime Identification ◽  
Velocity Information

scholarly journals Prediction of Liquid-Liquid Phase Separation Proteins Using Machine Learning

2020 ◽  
Author(s):  
Tanlin Sun ◽  
Qian Li ◽  
Youjun Xu ◽  
Zhuqing Zhang ◽  
Luhua Lai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phase Separation ◽  
Liquid Phase ◽  
Liquid Phase Separation ◽  
Liquid Liquid Phase Separation

scholarly journals Prediction of liquid-liquid phase separation proteins using machine learning

2019 ◽  
Author(s):  
Tanlin Sun ◽  
Qian Li ◽  
Youjun Xu ◽  
Zhuqing Zhang ◽  
Luhua Lai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phase Separation ◽  
Liquid Phase ◽  
Biological Function ◽  
Liquid Phase Separation ◽  
Sequence Information ◽  
Cellular Functions ◽  
Protein Nucleic Acid ◽  
Liquid Liquid Phase Separation ◽  
General Prediction

AbstractThe liquid-liquid phase separation (LLPS) of bio-molecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular functions. The dysregulation of LLPS might be implicated in a number of diseases. Although the LLPS of biomolecules has been investigated intensively in recent years, the knowledge of the prevalence and distribution of phase separation proteins (PSPs) is still lag behind. Development of computational methods to predict PSPs is therefore of great importance for comprehensive understanding of the biological function of LLPS. Here, a sequence-based prediction tool using machine learning for LLPS proteins (PSPredictor) was developed. Our model can achieve a maximum 10-CV accuracy of 96.03%, and performs much better in identifying new PSPs than reported PSP prediction tools. As far as we know, this is the first attempt to make a direct and more general prediction on LLPS proteins only based on sequence information.

scholarly journals Integrating Machine Learning with Liquid-Phase TEM Imaging to Study Nanoscale Crystallization and Macromolecular Heterogeneity

2021 ◽  
Vol 27 (S2) ◽  
pp. 37-38
Author(s):  
Chang Qian ◽  
Lehan Yao ◽  
Chang Liu ◽  
John W. Smith ◽  
Qian Chen
Keyword(s):  
Machine Learning ◽  
Liquid Phase

scholarly journals Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid–Liquid Phase Fraction Analysis

2021 ◽  
Vol 11 (13) ◽  
pp. 5988
Author(s):  
Takashi Kamiyama ◽  
Kazuma Hirano ◽  
Hirotaka Sato ◽  
Kanta Ono ◽  
Yuta Suzuki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Transmission Spectrum ◽  
Spectroscopic Imaging ◽  
Phase Fraction ◽  
Learning Methods ◽  
Neutron Transmission ◽  
Machine Learning Methods ◽  
Solid Liquid ◽  
Learning Analysis

In neutron transmission spectroscopic imaging, the transmission spectrum of each pixel on a two-dimensional detector is analyzed and the real-space distribution of microscopic information in an object is visualized with a wide field of view by mapping the obtained parameters. In the analysis of the transmission spectrum, since the spectrum can be classified with certain characteristics, it is possible for machine learning methods to be applied. In this study, we selected the subject of solid–liquid phase fraction imaging as the simplest application of the machine learning method. Firstly, liquid and solid transmission spectra have characteristic shapes, so spectrum classification according to their fraction can be carried out. Unsupervised and supervised machine learning analysis methods were tested and evaluated with simulated datasets of solid–liquid spectrum combinations. Then, the established methods were used to perform an analysis with actual measured spectrum datasets. As a result, the solid–liquid interface zone was specified from the solid–liquid phase fraction imaging using machine learning analysis.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Elucidation of block boundaries as the dissolution channels in hydrated cement

1978 ◽  
Vol 36 (1) ◽  
pp. 484-485
Author(s):  
N.V. Belov ◽  
U.I. Papiashwili ◽  
B.E. Yudovich
Keyword(s):  
Liquid Phase ◽  
Grain Boundaries ◽  
Benzoyl Peroxide ◽  
Phase Contrast ◽  
Active Role ◽  
Point Of View ◽  
Hardened Cement ◽  
Hydrated Cement ◽  
Hardened Cement Paste

It has been almost universally adopted that dissolution of solids proceeds with development of uniform, continuous frontiers of reaction.However this point of view is doubtful / 1 /. E.g. we have proved the active role of the block (grain) boundaries in the main phases of cement, these boundaries being the areas of hydrate phases' nucleation / 2 /. It has brought to the supposition that the dissolution frontier of cement particles in water is discrete. It seems also probable that the dissolution proceeds through the channels, which serve both for the liquid phase movement and for the drainage of the incongruant solution products. These channels can be appeared along the block boundaries.In order to demonsrate it, we have offered the method of phase-contrast impregnation of the hardened cement paste with the solution of methyl metacrylahe and benzoyl peroxide. The viscosity of this solution is equal to that of water.

Identification of hepatitis a virus in cell cultures by solid phase immune electron microscopy

1986 ◽  
Vol 44 ◽  
pp. 238-239
Author(s):  
C.D. Humphrey ◽  
T.L. Cromeans ◽  
E.H. Cook ◽  
D.W. Bradley
Keyword(s):  
Electron Microscopy ◽  
Liquid Phase ◽  
Cell Cultures ◽  
Rapid Detection ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Solid Phase ◽  
Immune Electron Microscopy ◽  
Detection And Identification

There is a variety of methods available for the rapid detection and identification of viruses by electron microscopy as described in several reviews. The predominant techniques are classified as direct electron microscopy (DEM), immune electron microscopy (IEM), liquid phase immune electron microscopy (LPIEM) and solid phase immune electron microscopy (SPIEM). Each technique has inherent strengths and weaknesses. However, in recent years, the most progress for identifying viruses has been realized by the utilization of SPIEM.

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