Effect of cutoff radius, long range interaction and temperature controller on thermodynamic properties of fluids: Methanol as an example

Obeidat, Abdalla; Jaradat, Abdullah A.; Hamdan, Bushra; Abu-Ghazleh, Hind

doi:10.1016/j.physa.2018.01.001

Cited by 16 publications

(3 citation statements)

References 28 publications

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“…Density value is one of an important parameter to imply the volumemass flow rate conversion [6]. It could be used to estimate the viscosity value [ 7 ] and predict the nucleation phenomena of liquid material at low temperatures [8]. Incorporating L resulted in a lower density due to a replacement with low density of L (0.82 ± 0.08 g. cm -3 at 23-24 °C [9].…”

Section: Results and Discussion Ph And Density Measurementsmentioning

confidence: 99%

Mixed Solvent-Lauric Acid Solvent-Exchange Induced <i>In Situ</i> Forming Gel

Chantadee

Santimaleeworagun

Phorom

et al. 2019

KEM

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Designing lauric acid (L) solvent-exchange induced in situ forming gel (ISG) was conducted in this study by using mixed solvents (N-methyl pyrrolidone (NMP), 2-pyrrolidone (PYR) and dimethyl sulfoxide (DMSO) at different ratios. The pH, density, viscosity, matrix formation, contact angle/surface tension and antimicrobial activities of 50% w/w L in mixed solvents were determined. The pH and density of solvent decreased apparently with L addition. More amount of PYR in the solvent mixture increased the density and viscosity. High viscous manner of PYR resulted in the high surface tension and contact angle. The solvent mixture of NMP/PYR promoted a higher contact angle than the other mixed solvent systems. L in DMSO, PYR or solvent mixture with high ratio of DMSO or PYR transformed into matrix-like rapidly while those in NMP or high ratio of NMP had slower matrix formation after exposure to aqueous fluid. NMP and PYR showed antimicrobial activities effectively against all test microbes while L matrix retarded their activities. The mixed solvent concept is interesting to use for designing ISG comprising L as matrix former to prolong the drug release.

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Section: Results and Discussion Ph And Density Measurementsmentioning

confidence: 99%

Mixed Solvent-Lauric Acid Solvent-Exchange Induced <i>In Situ</i> Forming Gel

Chantadee

Santimaleeworagun

Phorom

et al. 2019

KEM

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“…These include 2 Å, 3 Å, 4 Å, 5 Å, and 6 Å, as well as a natural cutoff based on the adsorbate-metal bond length (the sum of the atomic radii of the adsorbate's central atom and the adsorbent metal atom, typically between 2∼3 Å). [22] Generalized local coordination number and local electron affinity are also included in the geometric features, as they provide information about the bonding behavior around the adsorption site. [18][23] [24][25] Note that there exist other definitions of local electron affinity.…”

Section: Dataset Constructionmentioning

confidence: 99%

Interpreting Chemisorption Strength with AutoML-based Feature Deletion Experiments

Li,

Zhao,

Wang

et al. 2023

Preprint

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The chemisorption energy of reactants on a catalyst surface, E_ads, is among the most informative characters of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents significant difficulties in identifying the pivotal physical quantities determining Eads. In response to this, the study proposes a novel methodology, the feature deletion experiment, based on Automatic Machine Learning (AutoML) for knowledge extraction from a high-throughput density functional theory (DFT) database. The study reveals that, for binary alloy surfaces, the local adsorption site geometric information is the primary physical quantity determining E_ads, compared to the electronic and physiochemical properties of the catalyst alloys. By integrating the feature deletion experiment with instance-wise variable selection (INVASE), a neural network-based explainable AI (XAI) tool, we established the best-performing feature set containing 21 intrinsic, non-DFT computed properties, achieving an MAE of 0.23 eV across a periodic table-wide chemical space involving more than 1,600 types of alloys surfaces and 8,400 chemisorption reactions. This study demonstrates the stability, consistency, and potential of AutoML-based feature deletion experiment in developing concise, predictive, and theoretically meaningful models for complex chemical problems with minimal human intervention.

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“…Due to importance of methanol in our daily life and its applications in engineering, science, medicine and industry [1, 2, 3, 4], scientists investigated and will continue investigate this substance experimentally and theoretically at macro and micro levels [5, 6, 7, 8, 9, 10, 11, 12, 13]. Methanol is one of simplest polar fluids which consists of methyl group (CH3) and hydroxyl group (OH) and due to is availability, computational scientists studied this molecule extensively.…”

Section: Introductionmentioning

confidence: 99%

Critical temperature, surface tension and vapor density estimations of methanol using the scaled model

Obeidat

Badarneh

2019

Heliyon

Self Cite

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In the present work, we applied scaled model to calculate surface tension, vapor densities and the critical temperatures of four different models of methanol: namely, H1, J1, J2 and L1 models. The scaled model is based on calculating the free energy of the system. Free energy calculations were performed by applying the Bennet acceptance ratio (BAR) using Monte-Carlo simulations at low temperature range of 220K–280K. The BAR is based on calculating the free energy difference of n-molecules and (n-1)-molecules plus a free probe on methanol. Estimations of vapor densities are based on extrapolating the intercept of the scaled free energy linear line as number of molecules approaches infinity, which requires a pre-known values for liquid densities. To accomplish this, a series of molecular dynamic simulations were performed at low temperature range of 200K–300K with steps of 10K. All the estimated properties were in excellent agreement with experimental published data.

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Effect of cutoff radius, long range interaction and temperature controller on thermodynamic properties of fluids: Methanol as an example

Cited by 16 publications

References 28 publications

Mixed Solvent-Lauric Acid Solvent-Exchange Induced <i>In Situ</i> Forming Gel

Mixed Solvent-Lauric Acid Solvent-Exchange Induced <i>In Situ</i> Forming Gel

Interpreting Chemisorption Strength with AutoML-based Feature Deletion Experiments

Critical temperature, surface tension and vapor density estimations of methanol using the scaled model

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