Bumjoon Seo

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

Nanoparticles from commercial kraft lignin were developed using a facile, one pot green technology of a compressed CO2 antisolvent. N,N-Dimethylformamide (DMF) was employed as an organic solvent to prepare the lignin solution. 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. Moreover, the quality of lignin nanoparticles were elucidated by ATR-FTIR, XPS, XRD, DSC, TG/DTA and UV-vis measurements. This study showed that as the temperature and lignin concentration increased, and the pressure and solution flow rate decreased, the degree of particle aggregation/coalescence and the size increased along with the broader size distribution. In particular, the coalescence of particles was strongly influenced by the operational pressure, and even more significant with the increasing temperature. As a result, uniform, quasi-spherical nanoparticles with a mean particle diameter of 38 nm were obtained at 280.2 K, 15.0 MPa, and 0.06 kg h−1 of the solution flow rate and 5.3 wt% of the initial lignin concentration. Besides, the lignin nanoparticles have a relatively high BET surface area (nearly 92 m2 g−1) which primarily consisted of mesopores, and exhibited higher UV absorbing and dispersion stability, enhanced solubility, and homogeneous thermal degradation activity as compared with the raw lignin. Noteworthily, their biodegradable and biocompatible character may render them a candidate for applications in cosmetics, health and drug delivery systems.

IO Workload Characterization Revisited: A Data-Mining Approach

Over the past few decades, IO workload characterization has been a critical issue for operating system and storage community. Even so, the issue still deserves investigation because of the continued introduction of novel storage devices such as solid-state drives (SSDs), which have different characteristics from traditional hard disks. We propose novel IO workload characterization and classification schemes, aiming at addressing three major issues: (i) deciding right mining algorithms for IO traffic analysis, (ii) determining a feature set to properly characterize IO workloads, and (iii) defining essential IO traffic classes state-of-the-art storage devices can exploit in their internal management. The proposed characterization scheme extracts basic attributes that can effectively represent the characteristics of IO workloads and, based on the attributes, finds representative access patterns in general workloads using various clustering algorithms. The proposed classification scheme finds a small number of representative patterns of a given workload that can be exploited for optimization either in the storage stack of the operating system or inside the storage device.