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Tae Jun Yoon

Researcher at Los Alamos National Laboratory

Publications -  37
Citations -  532

Tae Jun Yoon is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Supercritical fluid & Chemistry. The author has an hindex of 11, co-authored 32 publications receiving 334 citations. Previous affiliations of Tae Jun Yoon include Seoul National University.

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One pot synthesis of environmentally friendly lignin nanoparticles with compressed liquid carbon dioxide as an antisolvent

TL;DR: In this paper, the effects of various process parameters: temperature, pressure, solution flow rate and initial solution concentration, on the product yields, morphology, size, size distribution, surface area and textural properties of the particles were investigated by FESEM, HRTEM analyses and BET analyzers, and their formation mechanisms were deduced by the solubility behavior of lignin with liquid CO2 and DMF in the operating system.
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Widom Delta of Supercritical Gas–Liquid Coexistence

TL;DR: Number fractions of gas-like and liquid-like particles are found to undergo continuous transition across the delta, following a simplified two-state model, providing an integrative explanation to the anomalous behaviors near the critical point and the Widom line.
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Tetracycline nanoparticles precipitation using supercritical and liquid CO2 as antisolvents

TL;DR: In this article, a micronized tetracycline hydrochloride using supercritical and liquid CO2 in the precipitation with compressed fluid antisolvent process (PCA) was used to verify the hypothesis and prevent the coalescence of nanoparticles.
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Current theoretical opinions and perspectives on the fundamental description of supercritical fluids

TL;DR: In this article, the authors briefly overview the theoretical advances for the fundamental description on the supercritical fluids since the late 1980s and introduce the concept of supercritical gas-liquid boundary and its importance.
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Probabilistic characterization of the Widom delta in supercritical region

TL;DR: A probabilistic classification algorithm designed based on the exploratory data analysis on the nearest Voronoi neighbors of subcritical vapor and liquid to understand the phase behavior of a supercritical mesophase is presented.