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in electronics, optics, and other related areas. [1][2][3][4][5][6][7][8][9] However, the current synthetic methods of SWCNTs difficultly produce a population of single-structure SWCNTs with identical properties despite recent creative breakthroughs in controlling the structural growth, [10][11][12][13] which seriously restricts the evaluation of their property and the corresponding technical applications. The structural control of SWCNTs by the postgrowth separation methods are more developed. Until now, various separation techniques such as DNA wrapping chromatography, [14,15] polymer wrapping, [16,17] density gradient ultracentrifugation (DGU), [18,19] aqueous twophase extraction (ATPE), [20][21][22] and gel chromatography [23,24] have been developed for the structural sorting of the synthetic SWCNT mixtures. With these techniques, SWCNTs can be separated based on not only their electronic type (i.e., metallic and semiconducting SWCNTs) but also chirality. In these solution-sorting techniques, the selective interactions of DNA, polymers, or surfactants with the SWCNTs is critical for their separation efficiency and purity. DGU, ATPE, and gel chromatography are currently the major methods because of the possibility of the mass production of single-chirality SWCNTs. [18][19][20][21][22][23][24] The principal feature in these methods is that the surfactants like sodium dodecyl sulfate The selective interaction is systematically explored between the surfactants (sodium deoxycholate (DOC), sodium dodecyl sulfate (SDS), and sodium cholate (SC)) and the single-wall carbon nanotubes (SWCNTs) by the gel chromatography technique. The results show that DOC preferentially interacts with small-diameter semiconducting SWCNTs (S-SWCNTs), exhibiting the strongest interaction strength for the SWCNTs, and the highest structuralselectivity. The surfactant SC shows high selectivity toward the chiral angles of the SWCNTs. Its interaction strength and structural recognition ability are slightly higher than that of SDS but lower than that of DOC. Combining with the proved selectivity of SDS in the adsorption onto the S-SWCNTs with small CC bond curvature, it is discovered that the synergistic effect of the triple surfactants amplified the interaction difference among the different SWCNTs and the gel, and thus dramatically improved the separation efficiency and structural purity of the SWCNTs, achieving the separation of distinct (n, m) single-chirality species and their enantiomers in one step. This work not only provides deeper insights into the separation mechanism of SWCNTs with the surfactant sorting techniques, but also has a profound significance in studying the interaction between the SWCNTs and other small molecules.

Purpose: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many laborintensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. Methods:To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. Results:We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization.Conclusions: This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.